ITJEMAST-V04(2):: International Transaction Journal of Engineering, Management, & Applied Sciences &
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Transcript of ITJEMAST-V04(2):: International Transaction Journal of Engineering, Management, & Applied Sciences &
IN THIS ISSUE Using DEMATEL Method to Analyze the Causal Relations on Technological Innovation Capability Evaluation Factors in Thai Technology-Based Firms The Effect of the 2,4-Dichlorophenoxy Acetic Acid, Benzyl Adenine and Paclobutrazol, on Vegetative Tissue-Derived Somatic Embryogenesis in Turmeric (Curcuma var. Chattip) Application of Bender’s Decomposition Solving a Feed–mix Problem among Supply and Demand Uncertainties Securing Bank Loans and Mortgages Using Real Estate Information Aided by Geospatial Technologies Theoretical Investigation of Hetero–Diels–Alder Functionalizations on SWCNT and Their Reaction Properties The Phenomenology of Lamban Tuha: The Local Wisdom of South Sumatra Traditional Architecture
International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies
Volume 4 Issue 2 (April 2013)
ISSN 2228-9860 eISSN 1906-9642
http://TuEngr.com
Cover Photo: Marina Bay Sands Building, in Singapore. Photo is taken by Mr. Saranyu SAVASRAT. Photo is used with permission.
International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies
http://TuEngr.com
International Editorial Board Editor-in-Chief Ahmad Sanusi Hassan, PhD Associate Professor Universiti Sains Malaysia, MALAYSIA
Executive Editor Boonsap Witchayangkoon, PhD Associate Professor Thammasat University, THAILAND
Noble Editorial Board: Professor Dr.Mikio SATOMURA (Shizuoka University, JAPAN) Professor Dr.Chuen-Sheng Cheng (Yuan Ze University, TAIWAN) Professor Dr.I Nyoman Pujawan (Sepuluh Nopember Institute of Technology, INDONESIA) Professor Dr.Neven Duić (University of Zagreb, CROATIA) Professor Dr.Lee, Yong-Chang (Incheon City College SOUTH KOREA) Professor Dr.Phadungsak Ratanadecho (Thammasat University, THAILAND) Professor Dr.Dewan M. Nuruzzaman (Dhaka University of Engineering & Technology, BANGLADESH) Professor Dr. Lutero Carmo de Lima (State University of Ceará, BRAZIL ) Associate Prof.Dr. Prapat Wangskarn (Dean of Faculty of Engineering, Thammasat University, THAILAND) Associate Prof.Dr.Uruya Weesakul (Past-Dean of Faculty of Engineering, Thammasat University, THAILAND )
Scientific and Technical Committee & Editorial Review Board on Engineering, Technologies and Applied Sciences: Associate Prof. Dr. Paulo Cesar Lima Segantine (University of São Paulo, BRASIL) Associate Prof. Dr. Kurt B. Wurm (New Mexico State University, USA ) Associate Prof. Dr. Truong Vu Bang Giang (Vietnam National University, Hanoi, VIETNAM ) Dr.H. Mustafa Palancıoğlu (Erciyes University, TURKEY) Associate Prof. Dr.Narin Watanakul (Thammasat University, THAILAND) Associate Prof. Commander Dr.Komsun Suwannarurk (Thammasat University, THAILAND ) Associate Prof.Dr.Peter Kuntu-Mensah (Texas A&M University-Corpus Christi, USA) Associate Prof.Dr. Anchalee Jala (Thammasat University, THAILAND ) Associate Prof. Dr. Masato SAITOH (Saitama University, JAPAN ) Assistant Prof.Dr. Zoe D. Ziaka (International Hellenic University, GREECE ) Associate Prof.Dr. Supornchai Utainarumol (King Mongkut's University of Technology North-Bangkok, THAILAND) Associate Prof.Dr.Chavalit Chaleeraktrakul (Thammasat University, THAILAND ) Associate Prof.Dr.Krittiya Lertpocasombut (Thammasat University, THAILAND ) Associate Prof.Dr. Bovornchok Poopat (King Mongkut's University of Technology Thonburi, THAILAND ) Associate Prof.Dr.Pudit Laksanacharoen (King Monkut's University of Technology North Bangkok, THAILAND) Associate Prof.Dr.K Pianthong (Ubon Ratchathani University, THAILAND) Associate Prof.Dr.Thanaporn Supriyasilp (Chiang Mai University, THAILAND) Associate Prof.Dr. Junji SHIKATA (Yokohama National University, JAPAN) Associate Prof. Dr.Aree Taylor (Thammasat University, THAILAND) Assistant Prof.Dr. Akeel Noori Abdul Hameed (University of Sharjah, UAE) Assistant Prof.Dr. Atch Sreshthaputra (Chulalongkorn University, THAILAND) Assistant Prof.Dr. Rohit Srivastava (Indian Institute of Technology Bombay, INDIA) Assistant Prof.Dr. Watanachai Smittakorn (Chulalongkorn University, THAILAND ) Assistant Prof.Dr. Kitjapat Phuvoravan (Kasetsart University, THAILAND) Assistant Prof.Dr. Khiensak Seangklieng (Thammasat University, THAILAND ) Assistant Prof.Dr. Chainarong Chaktranond (Thammasat University, THAILAND ) Assistant Prof.Dr.Kridayut Chompoming (Thammasat University, THAILAND ) Assistant Prof.Dr. Nopporn Leeprechanon (Thammasat University, THAILAND ) Assistant Prof.Dr. Suphattra KETSARAPONG (Sripatum University, THAILAND ) Assistant Prof.Dr. Sawat Pararach (Thammasat University, THAILAND ) Assistant Prof.Dr. Winai Raksuntorn (Thammasat University, THAILAND ) Assistant Prof.Dr. Watit Pakdee (Thammasat University, THAILAND ) Assistant Prof.Dr. Cattaleeya Pattamaprom (Thammasat University, THAILAND ) Assistant Prof.Dr.Puttipol Dumrongchai (Chiangmai University, THAILAND ) Madam Wan Mariah Wan Harun (Universiti Sains Malaysia, MALAYSIA ) Dr. David Kuria (Kimathi University College of Technology, KENYA ) Dr. Mazran bin Ismail (Universiti Sains Malaysia, MALAYSIA ) Dr.Isares Duchallaya (Thammasat University, THAILAND ) Dr.Bandit Suksawat (King Mongkut's University of Technology North-Bangkok, THAILAND ) Dr. Salahaddin Yasin Baper (Salahaddin University - Hawler, IRAQ ) Dr. Foong Swee Yeok (Universiti Sains Malaysia, MALAYSIA) Dr.Orawan Chunhachart (Kasetsart University Kamphaengsaen Campus, THAILAND ) Dr. Manop Kaewmoracharoen (Chiang Mai University, THAILAND)
2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.
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:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies
Volume 4 Issue 2 (April, 2013) ISSN 2228-9860 http://TuEngr.com eISSN 1906-9642
FEATURE PEER-REVIEWED ARTICLES
Using DEMATEL Method to Analyze the Causal Relations on Technological Innovation Capability Evaluation Factors in Thai Technology-Based Firms 81
The Effect of the 2,4-Dichlorophenoxy Acetic Acid, Benzyl Adenine and Paclobutrazol, on Vegetative Tissue-Derived Somatic Embryogenesis in Turmeric (Curcuma var. Chattip) 105
Application of Bender’s Decomposition Solving a Feed–mix Problem among Supply and Demand Uncertainties 111
Securing Bank Loans and Mortgages Using Real Estate Information Aided by Geospatial Technologies 129
Theoretical Investigation of Hetero–Diels–Alder Functionalizations on SWCNT and Their Reaction Properties 145
The Phenomenology of Lamban Tuha: The Local Wisdom of South Sumatra Traditional Architecture 157
Contact & Office: Associate Professor Dr. Ahmad Sanusi Hassan (Editor-in-Chief), School of Housing, Building and Planning, UNIVERSITI SAINS MALAYSIA, 11800 Minden, Penang, MALAYSIA. Tel: +60-4-653-2835 Fax: +60-4-657 6523, [email protected] Associate Professor Dr. Boonsap Witchayangkoon (Executive Editor), Faculty of Engineering, THAMMASAT UNIVERSITY, Klong-Luang, Pathumtani, 12120, THAILAND. Tel: +66-2-5643005 Ext 3101. Fax: +66-2-5643022 [email protected] Postal Paid in THAILAND.
2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.
*Corresponding author (P.Anuntavoranich). Tel: +66-2-657-6334. E-mail: [email protected], [email protected]. 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/081-103.pdf
81
International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies
http://TuEngr.com, http://Go.to/Research
Using DEMATEL Method to Analyze the Causal Relations on Technological Innovation Capability Evaluation Factors in Thai Technology-Based Firms Detcharat Sumrit a, and Pongpun Anuntavoranich a*
a Technopreneurship and Innovation Management Program, Graduate School, Chulalongkorn University, Bangkok, THAILAND. A R T I C L E I N F O
A B S T RA C T
Article history: Received 25 September 2012 Received in revised form 28 December 2012 Accepted 14 January 2013 Available online 18 January 2013 Keywords: Technology Innovation Capability; TIC evaluation factors; DEMATEL method; Cause and effect relationship.
This study analyzes the technology innovation capabilities (TICs) evaluation factors of enterprises by applying the Decision Making Trial and Evaluation Laboratory (DEMATEL) method. Based on the literature reviews, six main perspectives and sixteen criteria were extracted and then validated by six experts. A questionnaire was constructed and answered by eleven experts. Then the DEMATEL method was applied to analyze the importance of criteria and the casual relations among the criteria were constructed. The result showed that the innovation management capability perspective was the most important perspective and influenced the remaining perspectives. This work also presents the significant criteria for each perspective.
2013 INT TRANS J ENG MANAG SCI TECH.
1. Introduction Innovation’s importance has continuously increased and aligns with global business
growth. Bessant et al., (2005), and Huang (2011) clearly stated that Technological Innovation
Capabilities (TICs) play a crucial part in the initiation of firms’ competency and as the source of
sustainable competitive advantage. The enterprises, thus, are strongly required the periodical
monitoring their TICs and have to continuously strengthen their weak capabilities in order to
2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.
82 Detcharat Sumrit and Pongpun Anuntavoranich
facilitate the competitive advantage. This study mainly focuses on the technological-based
firms since they rely significantly on innovation development to pursue their business growth.
Although TICs were accepted as a main part of enhancing competitive advantage, TICs
assessment is rather complicated due to multi-dimensionality. The measuring indicators of
TICs are also diverse and difficult to assess by any single-dimension scale as they involve the
interaction among various resources (Chiesa et al., 1998, Hansen, 2001, Guan and Ma, 2003,
Burgelman et al., 2004). Guan et al., (2006) defined TICs measurement framework as
benchmark audition on the quantitative evaluation based on traditional DEA approach, which
relies on both technological capability and critical capabilities in the area of manufacturing,
marketing, organization, strategy planning, learning and resources allocation.
However, Wang et al., (2008) proposed the evaluation of high-tech firms’ TICs under both
quantitative assessment (by applying new fuzzy multi-criteria analytical approach) and
qualitative assessment (using five main aspects of capabilities i.e. R&D, innovation decision,
marketing, manufacturing and capital). Wang et al., (2008) viewed that the traditional
multi-criteria were not wholly suitable for TICs assessment. They also stated that the TICs
assessment was considered as subjective and ambiguous.
To clarify and reduce the subjective and ambiguous information, this study uses both
qualitative and quantitative methods. In this study, TICs’ critical evaluation perspectives and
criteria as well as the causal relations among them are presented. The result will aid the
managements in the determination of the degree of importance of critical factors/ criteria and
their influences on these factors.
Following this introduction, literature reviews of TICs and DEMATEL method were
illustrated in Section 2. Research methodology (including research framework, and the
procedure and results) was proposed in Section 3. Discussion and results were conducted in
Section 4. Finally, Section 5 drew the conclusion.
*Corresponding author (P.Anuntavoranich). Tel: +66-2-657-6334. E-mail: [email protected], [email protected]. 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/081-103.pdf
83
2. Literature Review
2.1 Technological innovation capability (TICs) TICs was defined as an enterprises’ ability to improve their technological innovativeness
in order to create new customer value through the introduction of new products and services,
the exploitation of new technologies and the exploration of new skill and competencies
(Perdomo-Ortiz et al., 2009, Wang et al., 2008, Huang, 2011). TICs assessments were also
included the aspects of multi-dimensionality, complexity, interactive innovation activities with
resource allocation to enhance competitive advantage (Wang et al., 2008, Chiesa et al., 1996).
Various researchers have developed the technological innovation framework, approaches
and components to evaluate a firm’s technological or innovation capabilities. For instance,
Baark et al., (2011) classified the assessment of a firm’s TICs into four approaches: (i) the asset
approach (Christensen, 1995), (ii) process approach (Chiesa et al., 1996; Burgelman et al.,
2004), (iii) output-based (Romijin and Albaladejo, 2002), and (iv) functional approach (Guan
and Ma, 2003; Yam. et al., 2004). Yam et al., 2004 developed an audit innovation capability
model by using functional approach, which consisted of seven components: learning capability,
R&D capability, resource allocation capability, manufacturing capability, marketing capability,
organizing capability and strategic planning capability. These studies of technological
innovation capability development are basically related to our research in view of providing an
overall framework for understanding the importance of such capability.
Based on the extensive literature review, overall TICs evaluation factors were concluded in
Table 1.
84 Detcharat Sumrit and Pongpun Anuntavoranich
Table 1: Summary of the perspectives and criteria of TICs’ evaluations Perspectives/ Criteria Author
Innovation Management Capability (P1)
Strategic Management Capability (C1) Burgelman et al., (2004), O’Regan et al., (2006), Ceylan and Koc (2007), Dobni (2008), Yam et
al., (2004), Yam et al., (2011), Türker (2012).
Organization Capability (C2) Guan et al., (2006), O’Regan et al., (2006), Burgelman et al., (2004), Yam et al., (2004), Yam et
al., (2011), Ceylan and Koc (2007), Dobni (2008), Spyropoulou and Kyrgidou (2012), Türker
(2012).
Resource Allocation Capability (C3) Chiesa et al., (1996), Barney and Clark (2007), Burgelman et al., (2004), Guan et al., (2006),
Dobni (2008), Wang et al., (2008), Ceylan and Koc (2007), Yam et al., (2011), Spyropoulou
and Kyrgidou (2012), Voudouris et al., (2012).
Risk Management Capability (C4) Amabile et al., (1996), Isaksen et al., (1999), Forsman (2011), Yang (2012).
Collective Learning Capability (P2)
Learning Capability (C5) Guan et al., (2006), Chiva and Alegre (2007), Teece (2007), Alegre and Chiva (2008), Yam et
al., (2004), Yam et al., (2011), Camisón and Villar-López (2012).
Absorptive Capacity (C6) Ceylan and Koc (2007), Zahra and George (2002), Lane and Koka (2006), Camisón and Forés
(2010), Forsman (2011), Wonglimpiyarat (2010), Kim et al., (2011), Gebauer et al., (2012),
Lin et al., (2012).
Knowledge Management Capability (C7) Forsman (2011), Yang (2012).
Innovation Sourcing Capability(P3)
Network Linkage Capability (C8) Lin (2004), Chesbrough (2004), Tidd (2006), Kim and Song (2007), Spithoven et al., (2010),
Shan and Jolly (2010), Zeng et al., (2010), Huang (2011), Forsman (2011), Mu and Benedetto
(2011), Kim et al., (2011), Voudouris et al., (2012).
Technology Acquisition Capability (C9) Chiesa et al., (1996), Ceylan and Koc (2007), Lee et al., (2009).
Technology Development Capability(P4)
R&D Capability (C10) Guan et al., (2006), Wang et al., (2008), Yam et al., (2004), Yam et al., (2011), Zahra and
George (2002), Levitas and Mc Fadyen (2009), Kim et al., (2011), Forsman (2011), Lin et al.,
(2012).
Project Cross Functional Team Integration
Capability (C11)
Martins and Terblanche (2003), Lin (2004), Camisón and Forés (2010), Kim et al., (2011), Yam
et al., (2011).
Technology Change Management Capability
(C12)
Jansen et al., (2005), Garrison (2009), Forsman (2011).
Robustness Product & Process Design Capability (P5)
Product Structural Design and Engineering
Capability (C13)
Chiesa et al., (1996), Christensen (1995), Zhang et al., (2000), De Toni and Nassimbeni (2001),
Antony et al., (2002), Nassimbeni and Battain (2003), Lin (2004), Ho et al., (2011).
Process Design and Engineering Capability
(C14)
Chiesa et al., (1996), Zhang et al., (2000), De Toni and Nassimbeni (2001), Antony et al.,
(2002), Nassimbeni and Battain, (2003).
Technology Commercialization Capability (P6)
Manufacturing Capability (C15) Lin (2004), Yam et al.,(2004), Guan et al. (2006), Wang et al.,(2008), Yam et al., (2011), Kim
et al., (2011), Yang (2013).
Market Capability (C16) Lin (2004), Yam et al., (2004), Guan et al., (2006), Dobni (2008), Wang et al., (2008), Yam et
al., (2011), Forsman (2011), Mu and Benedetto (2011), Kim et al., (2011).
*Corresponding author (P.Anuntavoranich). Tel: +66-2-657-6334. E-mail: [email protected], [email protected]. 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/081-103.pdf
85
2.2 DEMATEL Method DEMATEL method was originally developed between 1972 to 1979 by the Science and
Human Affairs Program of the Battelle Memorial Institute of Geneva, with the purpose of
studying the complex and intertwined problematic group. It has been widely accepted as one
of the best tools to solve the cause and effect relationship among the evaluation criteria (Chiu et
al., 2006, Liou et al., 2007, Tzeng et al., 2007, Wu and Lee, 2007, Lin and Tzeng, 2009). This
method is applied to analyze and form the relationship of cause and effect among evaluation
criteria (Yang et al., 2008) or to derive interrelationship among factors (Lin and Tzeng, 2009).
Based on Yu and Tseng (2006), Liou, et al., (2007), Tzeng, et al., (2007), Yang, et al., (2008),
Wu and Lee (2007), Shieh et al., (2010), the procedure of DEMATEL method is presented
below:
Figure 1: The process of the DEMATEL method.
Step 1: Gather experts’ opinion and calculate the average matrix Z
A group of m experts and n factors are used in this step. Each expert is asked to view the
degree of direct influence between two factors based on pair-wise comparison. The degree to
which the expert perceived factor i affects on factor j is denoted as xij. The integer score is
ranged from 0 (no influence), 1 (low influence), 2 (medium influence), 3 (high influence), and 4
(very high influence), respectively. For each expert, an n x n non-negative matrix is constructed
as Xk = , where k is the expert number of participating in evaluation process with 1≤ k ≤ m.
Thus, X1, X2, X3,.., Xm are the matrices from m experts.
Step 5:
Set the
threshold value
(α)
Step 6:
Build a cause and
effect relationship
diagram
Yes
No
Step 4:
Calculate the sums
of rows and
columns of matrix T
Step 2:
Calculate the normalized
initial direct-relation
matrix D
Step 3:
Derive the total
relation matrix T
The final cause
and effect
relationship
Step 1:
Gather experts’ opinion and
calculate the average matrix Z
Is a cause and effect
relationship diagram
acceptable?
86 Detcharat Sumrit and Pongpun Anuntavoranich
To aggregate all judgments from m experts, the average matrix Z= [zij] is shown below.
zij = ∑ (1)
Step 2: Calculate the normalized initial direct- relation matrix D
The normalized initial direct-relation matrix D = [dij], where value of each element in
matrix D is ranged between [0, 1]. The calculation is shown below.
D = λ * Z, (2)
or
[dij]nxn = λ [zij]nxn (3)
where
λ = Min ∑ , ∑ (4)
Based on Markov chain theory, is the powers of matrix D, e.g. D2, D3,…, D∞
guarantees the convergent solutions to the matrix inversion as shown below.
lim = [0]nxn, (5)
Step 3: Derive the total relation matrix T
The total-influence matrix T is obtained by utilizing Eq. (7), in which, I is an n x n identity
matrix. The element of tij represents the indirect effects that factor i had on factor j, then the
matrix T reflects the total relationship between each pair of system factors.
T = lim (D + + …+ (6)
= ∑
where
∑ = D1 + + …+
*Corresponding author (P.Anuntavoranich). Tel: +66-2-657-6334. E-mail: [email protected], [email protected]. 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/081-103.pdf
87
= D (I + D1 + + …+ )
= D (I - D)-1(I - D)(I + D1 + + …+ )
= D (I - D)-1(I - Dm)
T = D (I - D)-1 (7)
Step 4: Calculate the sums of rows and columns of matrix T
In the total-influence matrix T, the sum of rows and the sum of columns are represented by
vectors r and c, respectively.
r = [ri]nx1 = ∑ nx1, (8)
c = ́1xn = ∑ ́1xn , (9)
where ́ is denoted as transposition matrix.
Let ri be the sum of ith row in matrix T. The value of ri indicates the total given both directly
and indirectly effects, that factor i has on the other factors.
Let cj be the sum of the jth column in matrix T. The value of cj shows the total received both
directly and indirectly effects, that all other factors have on factor j. If j = i, the value of (ri + ci)
represents the total effects both given and received by factor i. In contrast, the value of (ri-ci)
shows the net contribution by factor i on the system. Moreover, when (ri -ci) was positive, factor
i was a net cause. When (ri -ci) was negative, factor i was a net receiver (Tzeng et al., 2007; Liou
et al., 2007; Yang et al., 2008; Lee et al., 2009).
Step 5: Set a threshold value (α)
The threshold value ), was computed by the average of the elements in matrix T, as
computed by Eq. (11). This calculation aimed to eliminate some minor effects elements in
matrix T. (Yang et al., 2008).
= ∑ ∑
N (10)
where N is the total number of elements in the matrix T.
88 Detcharat Sumrit and Pongpun Anuntavoranich
Step 6: Build a cause and effect relationship diagram
The cause and effect diagram is constructed by mapping all coordinate sets of (ri +ci, ri -ci)
to visualize the complex interrelationship and provide information to judge which are the most
important factors and how influence affected factors (Shieh et al., 2010). The factors that tij is
greater than , are selected shown in cause and effect diagram (Yang et al., 2008).
3. Research Methodology
3.1 Research Framework of TICs This section established a structure for identifying the evaluation perspective and criteria
as well as their relationships of TICs factors. An overview of the proposed TICs evaluation
framework was illustrated in Figure 2. The details of each procedure and the results were
explained in next section.
Figure 2: The proposed procedure of TICs criteria assessment.
3.2 Procedure and the result This section is to describe the process of TICs evaluation perspectives and criteria,
according to Figure 2. Not only the determination of TICs evaluation perspectives and criteria
but also the measurement of the relationship among them was also performed. The process and
the result of each stage were presented in the following stages:
Content validation by experts
Extensive literature reviews
Applying DEMATEL method
Stage 1: Define the problem statement
Stage 2: Explore TICs measurement perspectives and
criteria
Stage 3: Develop interview questionnaire and validity
testing
Stage 4: Interview session with 11 industrial experts
Stage 5: Analyze cause and effect relationship among
and identify evaluation factors
*Corresponding author (P.Anuntavoranich). Tel: +66-2-657-6334. E-mail: [email protected], [email protected]. 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/081-103.pdf
89
3.2.1 Stage of defining the problem statements To encounter the fierce competition of the dynamic global environment and the upcoming,
TICs are considered as one of the significant factors of Thai technology-based firms to sustain
competitiveness. Hence, an evaluation of TICs turns to be a tool to aid managements to define
strengths and weaknesses in term of TICs. Appropriate factors of TICs then should be
identified. This study presents not only the appropriate factors but also the cause and effect
relationship among the perspectives and criteria.
3.2.2 Stage of exploring the TICs measurement perspectives and criteria from
literature reviews
The extensive literature review was conducted to identify multi-attributions and
multi-dimensionalities of the TICs evaluation factors. Based on the reviews, six perspectives
and sixteen evaluation criteria were derived as shown in Table 1.
3.2.3 Stage of developing a questionnaire After obtaining the sixteen criteria and six perspectives of TICs evaluation factors from
literatures, a questionnaire was designed. A group of qualified experts reviewed and tested the
designed questionnaire to assure the content validity of questionnaire. The group of qualified
experts was consisted of three professionals from academic institutions, two from industrial
sector and one from Thai Automotive Institution. After interviewing, the questionnaire was
revised based on the experts’ aspects.
3.2.4 Stage of interviewing session Eleven experts were asked to complete the questionnaire. The experts have at least 5 years
experiences and worked in management positions in well-known Thai technology-based firms
and some of the firms were awarded as Thailand’s Most Innovative Company in 2010. After
obtaining the completed questionnaires from the experts, DEMATEL analytical technique was
used to determine the causal relations and to identify the significant perspectives and criteria.
The results of analyses were shown in the next section.
90 Detcharat Sumrit and Pongpun Anuntavoranich
3.2.5 Stage of analyzing the causal relation and identifying the evaluation
perspectives and criteria
Based on the six perspectives and sixteen criteria of TICs evaluation as stated above, this
study further employed the DEMATEL method to indicate the complex relationship and
identify the significant TICs evaluation perspectives and criteria. In this section, the
computation was divided into two parts for calculating on perspectives and criteria,
respectively. The procedure of the DEMATEL method and the results of each stage were also
presented as follows.
3.2.5.1 Applying DEMATEL method on the six perspectives
Xk showed the data gathered in terms of the six perspectives of expert k, where Xk = .
Step procedures of applying DEMATEL method were shown next.
0 3 3 3 3
4
0 2 3 3 2
4
0 3 2 2 3
4
0 2 2 3 3 3
2 0 3 3 2 2 2 0 3 3 3 3 2 0 2 3 3 0 1 0 3 3 3 3
X1= 1 1 0 2 1 1 X2= 1 2 0 2 2 0 X3= 1 3 0 1 2 1 X4= 1 2 0 0 2 2
1 3 3 0 1 3 2 4 1 0 2 2 2 3 2 0 2 1 1 3 2 0 2 2
2 1 2 3 0 3 1 2 2 1 0 3 1 2 2 2 0 4 2 2 1 3 0 3
1 2 2 1 2 0 2 1 2 3 2 0 1 2 1 1 2 0 2 1 2 2 2 0
0 2 2 0 2
3
0 2 2 3 2
4
0 2 3 2 2
3
0 1 2 3 2 3
2 0 2 3 2 2 2 0 2 3 3 3
2 0 3 3 2 0 2 0 3 3 2 2
X5= 1 2 0 2 2 1 X6= 1 2 0 2 2 1 X7= 2 3 0 2 3 2 X8= 1 2 0 2 2 2
2 3 1 0 3 2 2 3 2 0 0 1
1 4 2 0 2 1 2 4 1 0 1 2
2 1 2 2 0 3 2 2 2 3 0 2
2 1 2 2 0 3 1 2 2 1 0 3
2 1 2 2 2 0 1 2 2 2 1 0
2 1 1 1 2 0 3 1 2 1 1 0
0 1 2 3 2
4
0 2 3 2 2
3
0 2 1 1 2
3
3 0 3 3 3 2 2 0 2 3 3 0
1 0 2 3 2 2 X9=
2 3 0 2 2 2 X10=
1 2 0 2 2 2 X11=
1 2 0 2 2 1
2 4 2 0 3 2 2 3 2 0 2 3
2 3 1 0 2 2
2 2 2 0 0 3 2 2 1 1 0 3
2 1 2 2 0 2
2 1 1 1 2 0 2 1 1 2 2 0
1 1 2 2 3 0
*Corresponding author (P.Anuntavoranich). Tel: +66-2-657-6334. E-mail: [email protected], [email protected]. 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/081-103.pdf
91
(1) The computation of the average matrix Z was constructed by using Eq. (1).
0 2 2.2727 2.2727 2.2727 3.4545
1.90909 0 2.54545 3 2.5454 1.7272
Z = 1.18181 2.1818 0 1.7272 2 1.3636
1.72727 3.3636 1.72727 0 1.8181 1.9090
1.72727 1.6363 1.81818 1.8181 0 2.9090
1.72727 1.2727 1.63636 1.6363 1.9090 0
(2) The normalized initial direct-relation matrix D was calculated by using Eq. (2) to
Eq.(5).
0.0000 0.1760 0.2000 0.2000 0.2000 0.3040
0.1680 0.0000 0.2240 0.2640 0.2240 0.1520
D = 0.1040 0.1920 0.0000 0.1520 0.1760 0.1200
0.1520 0.2960 0.1520 0.0000 0.1600 0.1680
0.1520 0.1440 0.1600 0.1600 0.0000 0.2560
0.1520 0.1120 0.1440 0.1440 0.1680 0.0000
(3) The total relation matrix T was calculated by using Eq. (6) to Eq. (7) as shown below.
1.1983 1.6022 1.5638 1.6194 1.6335 1.7897
1.3147 1.4312 1.5522 1.6366 1.6189 1.6449
T = 0.9837 1.2421 1.0379 1.2130 1.2383 1.2532
1.2195 1.5575 1.4040 1.3262 1.4714 1.5434
1.1290 1.3342 1.3001 1.3464 1.2203 1.4966
0.9883 1.1431 1.1256 1.1653 1.1928 1.1104
(4) The sums of rows and columns of matrix T were calculated by using Eq. (8) to Eq. (9)
as shown in Table 2.
Table 2: The sums of given and received among six perspectives. P1 P2 P3 P4 P5 P6 ri cj (ri+ cj) (ri- cj)
P1 1.1983 1.6022* 1.5638* 1.6194* 1.6335* 1.7897* 9.4068 6.8335 16.2403 2.5733
P2 1.3147 1.4312* 1.5522* 1.6366* 1.6189* 1.6449* 9.1985 8.3103 17.5087 0.8882
P3 0.9837 1.2421 1.0379 1.2130 1.2383 1.2532 6.9683 7.9836 14.9519 -1.0153
P4 1.2195 1.5575* 1.4040* 1.3262 1.4714* 1.5434* 8.5219 8.3069 16.8288 0.2151
P5 1.1290 1.3342 1.3001 1.3464 1.2203 1.4966* 7.8266 8.3752 16.2018 -0.5486
P6 0.9883 1.1431 1.1256 1.1653 1.1928 1.1104 6.7255 8.8382 15.5637 -2.1127
92 Detcharat Sumrit and Pongpun Anuntavoranich
(5) The set up of the threshold value (α)
The threshold value was derived from the average of elements in matrix T, which was
calculated by using Eq. (10).
= .
= 1.351
(6) The construction of the cause and effect relationship diagram
The values of tij in Table 2, which were greater than α (1.351), were shown as tij*, which
presented the interaction between perspectives, e.g. the value of t12 (1.6022) > α (1.351), the
arrow in the cause and effect diagram was drawn from P1 to P2. The cause and effect diagram of
six perspectives was constructed as Figure 3.
Figure 3: The visualization of the causal relationship among perspectives of TICs.
3.2.5.2 Applying DEMATEL method on the sixteen criteria
Under each perspective, the significant criteria were determined by using the same
procedures as described in (1) to (6) above. Both direct and indirect effects of the criteria under
six perspectives were summarized in Table 3 and the cause and effect diagrams among criteria
under each perspective were shown in Figure 4 to Figure 9.
P3
P1
P2 P4
P5
P6
15.0 15.5 16.0 16.5 17.0 17.5
3
-1
-2
-3
2
1
0
r-c
r+c
Threshold Value (α) = 1.351
*Corresponding author (P.Anuntavoranich). Tel: +66-2-657-6334. E-mail: [email protected], [email protected]. 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/081-103.pdf
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4. Discussion and Results
4.1 Results on the Perspectives The important of evaluation perspectives was determined by (r+c) values. Based on Table
3, Collective Learning Capability (P2) was the most important evaluation perspective with the
largest (r+c) value = 17.5087, whereas Innovation Sourcing Capability (P3) was the least
important perspective with the smallest (r+c) value = 14.9519. Regarding to (r+c) values, the
prioritization of the importance of six evaluation perspective was P2 > P4> P1> P5> P6> P3.
Table 3: The direct and indirect effects of the criteria under each perspective. Criteria (r+c) (r-c)
The overall effects of the four criteria of Innovation Management Capability perspective
Strategic Management Capability (C1) 17.8235 2.1543
Organization Capability (C2) 19.7564 0.1836
Resource Allocation Capability (C3) 17.0986 -1.3492
Risk Management Capability (C4) 17.7238 -0.9887
The overall effects of the three criteria of Collective Learning Capability perspective
Learning Capability (C5) 9.3937 0.3263
Absorptive Capability (C6) 9.3174 1.3097
Knowledge Management Capability(C7) 9.0972 -1.6360
The overall effects of the two criteria of Innovation Sourcing capability perspective
Network Linkage Capability (C8) 6.3333 1.0000
Technology Acquisition Capability (C9) 6.3333 -1.0000
The overall effects of the three criteria of Technology Development Capability perspective
R&D Capability (C10) 71.7604 2.8446
Project Cross Functional Team Integration Capability (C11) 68.7422 1.7228
Technology Change Management Capability(C12) 65.4969 -4.5675
The overall effects of the two criteria of Robustness Product & Process Design Capability perspective
Product Structural Design and Engineering Capability (C13) 7.8000 1.0000
Process Design and Engineering Capability (C14) 7.8000 -1.0000
The overall effects of the two criteria of Technology Commercialization Capability perspective
Manufacturing Capability (C15) 21.000 1.0000
Market Capability (C16) 21.000 -1.0000
94 Detcharat Sumrit and Pongpun Anuntavoranich
Figure 5: The cause and effect diagram of the three
criteria of Collective Learning Capability
9.6
C5
C7
C6
9.0 9.2 9.4 -1
-2
2
1
0 r+c
r- c
Threshold Value (α) = 1.50
Figure 4: The cause and effect diagram of the four
criteria of Innovation Management Capability
20 17 18 19
-1
-2
2
1
0
C3
C1
C2
C4
r+c
r- c
Threshold Value (α) = 2.262
Figure 7: The cause and effect diagram of the three
criteria of Technology Development Capability
70 64 66 68 -2
-4
4
2
0
-6
6
C10 C11
C12
r+c
r- c
Threshold Value (α) = 11.444
Figure 6: The cause and effect diagram of the two
criteria of Innovation Sourcing Capability.
8.0 2.0 4.0 6.0
r+c
r- c
C9
C8
-1
-2
2
1
0
Threshold Value (α) = 1.583
10 4.0 6.0 8.0 r+c
r- c
-1
-2
2
1
0
C14
C13
Threshold Value (α) = 1.950
Figure 8: The cause and effect diagram of the two criteria
of Robustness Product & Process Design Capability
Figure 9: The cause and effect diagram of the two
criteria of Technology Commercialization Capability
r+c
r- c
24 18 20 22 -1
-2
2
1
0
C16
C15
Threshold Value (α) = 5.250
*Corresponding author (P.Anuntavoranich). Tel: +66-2-657-6334. E-mail: [email protected], [email protected]. 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/081-103.pdf
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Based on (r-c) values, the six perspectives were divided into (i) cause group and (ii) effect
group.
(i) If the value of (r-c) was positive or net cause, such perspective was classified in the
cause group, and directly affected the others. The highest (r-c) factors also had the greatest
direct impact on the others. In this study, Innovation Management Capability (P1), Collective
Learning Capability (P2), and Technology Development Capability (P4) were classified in the
cause group, having the (r-c) values of 2.5733, 0.8882, and 0.2151, respectively. It also
indicated that P1 (Innovation Management Capability) was the most critical impact factor on
the others. Based on the matrix T in Table 2, it was found that P2 (Collective Learning
Capability) and P4 (Technology Development Capability) had a mutual interaction as both the
value of t24 (1.6366) and t42 (1.5575) were greater than α (1.351).
(ii) If the value of (r-c) was negative or net receive, such perspective was classified in the
effect group, and largely influenced by the others. For this study, Technology
Commercialization Capability (P6), Innovation Sourcing Capability (P3) and Robustness
Product and Process Design Capability (P5) were categorized in the effect group, with the (r-c)
values of -2.1127, -1.0153 and -0.5484, respectively. And P6 (Technology Commercialization
Capability) was the most affected by the other factors (P1), (P2), (P4), and (P5).
4.2 Results on the Criteria According to Table 3, under Innovation Management Capability perspective (P1), this
study found that Organization Capability (C2) and Strategic Management Capability (C1) were
the two most important criteria based on first and second highest (r+c) values of 19.7564 and
17.8235, respectively. Whereas both Strategic Management Capability (C1) and Organization
Capability (C2) were in the cause group based on their positive (r-c) values of 2.1543 and
0.1836, respectively. For Resource Allocation Capability (C3) and Risk Management
Capability (C4) were in the effect group, given negative (r-c) values of -1.3492 and -0.9887,
respectively. From Figure 4, Strategic Management Capability (C1) was the most critical
criteria because it directly influenced on the other three criteria. Organization Capability (C2)
had a direct impact on Resource Allocation Capability (C3) and a mutual interaction on Risk
Management Capability (C4).
96 Detcharat Sumrit and Pongpun Anuntavoranich
For the perspective of Collective Learning Capability (P2), Learning Capability (C5) and
Absorptive Capability (C6) were the two most important criteria based on higher (r+c) values
of 9.3937 and 9.3174, respectively. They were also the net cause group with higher positive
(r-c) values of 0.3263 and 1.3097, respectively. For Knowledge Management Capability (C7)
was net receive with the (r-c) value of -1.6360. From Figure 5, Absorptive Capability (C6)
presented as the most significant criteria given impact to the other two criteria.
For the perspective of Innovation Sourcing capability (P3) in Table 3, Network Linkage
Capability (C8) and Technology Acquisition Capability (C9) showed the same importance level
of the (r+c) values 6.3333. However, based on the (r-c) value of 1.0 (Figure6), Network
Linkage Capability (C8), was a net cause and largely impacted Technology Acquisition
Capability (C9).
According to Technology Development Capability perspective (P4), R&D Capability (C10)
and Project Cross Functional Team Integration Capability (C11) were the two most important
criteria with highest (r+c) values of 71.7604, and 68.7422, respectively. Both of them were net
cause. As shown in Figure7, R&D Capability (C10) had the greatest (r-c) value of 2.8446, which
directly affected Technology Change Management Capability (C12) and had a mutual
interaction on Project Cross Functional Team Integration Capability (C11).
For the perspective of Robustness Product & Process Design capability (P5), both criteria
Product Structural Design and Engineering Capability (C13) and Process Design and
Engineering Capability (C14) had the same importance level of the (r+c) values equaling to
7.80. However, as Figure 8, Product Structural Design and Engineering Capability (C13) was
net cause with the (r-c) value of 1.0, and affected Process Design and Engineering Capability
(C14).
For the perspective of Technology Commercialization Capability (P6), there were the same
importance level of the two criteria i.e. Manufacturing Capability (C15) and Market Capability
(C16), based on their equal (r+c) values of 21.0. However, as Figure 9, Manufacturing
Capability (C15) was a net cause having the (r-c) value of 1.0 and affected Market Capability
(C16).
*Corresponding author (P.Anuntavoranich). Tel: +66-2-657-6334. E-mail: [email protected], [email protected]. 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/081-103.pdf
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5. Conclusion This study applied DEMATEL method not only to analyze the TIC evaluation perspectives
and criteria, consisting of six perspectives and sixteen criteria for Thai technology-based firms’
but also to describe the cause and effect relationship among them. The result implied that the
management should concentrate on improving the three core perspectives in the cause group
i.e. Innovation Management Capability, Collective Learning Capability, and Technology
Development Capability. The three remaining perspectives were found in the effect group i.e.
Technology Commercialization Capability, Innovation Sourcing Capability and Robustness
Product and Process Design Capability, which they were also affected by the ones in the cause
group.
By the aspect of prioritizing the importance of criteria and the cause and effect relationship
among criteria under the three core perspectives, this study found that the Strategic
Management Capability, Absorptive Capability and R&D Capability were the most critical
criteria. Therefore, in order to enhance the overall competitive advantage in term of TICs, Thai
technology-based firms should allocate more resources in these core perspectives. In the case of
having limited resources, firms should emphasize on their Strategic Management Capability
since it is the main critical criteria in the adjustment of corporate planning and would yield
highest positive results on TICs.
6. Appendix The definitions of criteria are identified in Table A:
Table A: terms and definitions of criteria used in this study Terms Definitions
Strategic Management
Capability
The firm’s ability to identify internal strengths and weaknesses and external opportunities and
threats, to formulate plans in accordance with the corporate vision and missions, and to adjust the
plans for implementation (Yam et al., 2004).
Organization Capability The firm’s ability to secure the organizational mechanism and harmony, to cultivate the organization
culture, and to adopt the better management practices (Yam et al., 2004).
Resource Allocation
Capability
The firm’s ability to acquire and to allocate appropriately capital, exercise and technology in the
innovation process (Yam et al., 2004).
Risk Management Capability The firm’s ability to assess the risk of technological innovation and to take the risk of technological
innovation adoption (Forsman, 2011).
98 Detcharat Sumrit and Pongpun Anuntavoranich
Table A: terms and definitions of criteria used in this study (continue) Terms Definitions
Learning Capability The firm’s ability to identify, to assimilate, and to exploit the knowledge from internal organization
(Yam et al., 2004).
Absorptive Capacity The firm’s ability to recognize, to assimilate, and to apply the value of new external information to
commercial ends (Cohen and Lavinthal, 1990).
Knowledge Management
Capability
The firm’s ability to accumulate critical knowledge resources and to manage its assimilation and exploitation (Miranda et al., 2011).
Network Linkage Capability The firm’s ability to transmit information, skills and technology, and to receive them from other
departments of the firm, including third parties such as the clients, the suppliers, the consultants, the
technological institutions (Shan and Jolly, 2010).
Technology Acquisition
Capability
The firm’s ability to acquire and to adopt external technology from other parties (Hemmert, 2004).
R&D Capability The firm’s ability to integrate R&D strategy, project implementation, project portfolio management,
and R&D expenditure (Yam et al., 2004).
Project Cross functional
team integration capability
The firm’s ability to coordinate and to integrate all phases of the R&D process and the inter-relations
with the functional tasks of engineering, production and marketing (Camisón and Forés, 2010).
Technology Change
Management Capability
The firm’s ability to accurately predict future technological trends and to response the technology
changes (Jansen et al., 2005).
Product Structural Design
and Engineering Capability
The firm’s ability to design product structure, to build product modularization and to make product
and process compatible (De Toni and Nassimbeni, 2001).
Process Design and
Engineering Capability
The firm’s ability to design process for supporting the manufacturing design and to design the
assembly activities (De Toni and Nassimbeni, 2001).
Manufacturing Capability The firm’s ability to transform R&D result into products, which meet the market’s need as required
design, and able to produce (Yam et al., 2004).
Market Capability The firm’s ability to sell products on the basis of the understanding of customers’ need, the
competitive environment, costs and benefits, and the acceptance of the innovation (Yam et al.,
2004).
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D. Sumrit is a Ph.D. Candidate of Technopreneurship and Innovation Management Program, Graduate School, Chulalongkorn University, Bangkok, Thailand. He received his B.Eng in Industrial Engineering from Kasetsart University, an M.Eng from Chulalongkorn University and MBA from Thammasat University.
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*Corresponding author (A. Jala). Tel/Fax: +66-2-5644440-59 Ext. 2450. E-mail address: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/105-110.pdf
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The Effect of the 2,4-Dichlorophenoxy Acetic Acid, Benzyl Adenine and Paclobutrazol, on Vegetative Tissue-Derived Somatic Embryogenesis in Turmeric (Curcuma var. Chattip)
Anchalee Jala a* a Department of Biotechnology, Faculty of Science and Technology, Thammasat University, THAILAND A R T I C L E I N F O
A B S T RA C T
Article history: Received 09 October 2012 Received in revised form 06 November 2012 Accepted 16 January 2013 Available online 22 January 2013 Keywords: Paclobutrazol; 2,4-dichlorophenoxy acetic acid (2,4-D); Benzyl adenine (BA), Somatic embryogenesis.
The nodal explants of Curcuma var. Chattip could develop callus after in vitro culturing and transplanting within 4 weeks in MS medium supplemented with 1.0 mg l-1 2,4-D, although this optimal if the media was further supplemented with 5.0 mg l-1 BA, obtaining the highest number of new shoots in 6 weeks. MS medium supplemented with 0.01 mg l-1 paclobutrazol and 15% (v/v) coconut water was found suitable for regenerating the highest number of new shoots (7.25 shoots). The number of leaves per plantlet, length of leaves, and length of petrioles were significantly reduced (p≤ 0.05) when increased concentrations of paclobutrazol and especially paclobutrazol with 15 % (v/v) coconut water. However further experimentation is required to evaluate the dose-response and the interaction between coconut water and paclobutrazol. In contrast, there were no significant difference in leaf width in all treatments.
2013 INT TRANS J ENG MANAG SCI TECH.
1. Introduction Turmeric (Curcuma var. chattip), a herbaceous plant of the Zingiberaceae (ginger) family
(Purseglove,1972), is an economically important cultivated species in Thailand being grown
for cut flowers, decoration and landscaping. Although propagated by underground rhizomes,
2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.
106 Anchalee Jala
the rate of rhizome multiplication and growth is very low, making this non-viable for large
scale economic production. Moreover, many diseases and pests, particularly soft rot caused
by Pythium spp. (Jantan et al., 2003) as well as bacterial wilts are consistently threatening
Curcuma var. chattip. This problem is compounded by its slow propagation rate which
seriously impedes on the ability to replace diseased plants quicker than infection rates.
To aid the rapid and large scale propagation of this plant, in vitro formation of storage
organs such as rhizomes that can be directly transferred to the field without any
acclimatization has been reported (Balachandran et al., 1990). However, further improvement
of the protocol to obtain larger and more vigorous plantlets is required in order to approach an
economically or logistically viable method.
The presented investigation was carried out to examine the effects of the plant growth
regulators palcolbutrazol and benzyl adenine (BA) in the presence of napthaleneacetic acid
(NAA) and coconut water, upon the formation of callus and multiply new plantlets.
2. Materials and Methods Spouted immature shoots (ca. 1 cm. long) were collected and used as explants. They
were washed thoroughly in running tap water and soaked with liquid detergent (teepol
solution) followed by rinsing in tap water for 2 min. For surface sterilization, explants are
rinsed in 10 % (v/v) Clorox solution for 10 min and 5 % (v/v) Clorox solution for 10 min and
finally soaked with sterile distilled water three times to remove traces of Clorox. Immature
shoots are trimmed to remove excess tissue. Murashige and Skoog medium (MS) fortified
with 0.25 % (w/v) and 4% (w/v) final concentration of gelrite and sucrose, respectively. MS
medium supplemented with 2,4-dichlorophenoxy acetic acid (2,4-D), BA, and coconut water
is used as basal medium for callus. The pH of medium was adjusted to 5.6 by using 0.1 N
NaOH or 0.1 HCl and autoclaving at 121 ํC for 20 min to sterilize. All cultures were
incubated under 16hours photoperiod (irradiance of 36 µmole m-2 S-1) with temperature 25 ±
1°C. We observed samples at regular intervals and scored for callus and shoot growth, with
10 independent replicates being used for each experimental treatment.
3. Statistical Analysis This experiment used CRD (Completely Randomized Design). The analysis of variance
between the means was conducted by using Duncan’s multiple range test (Duncan, 1955).
*Corresponding author (A. Jala). Tel/Fax: +66-2-5644440-59 Ext. 2450. E-mail address: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/105-110.pdf
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4. Result and Discussion After 4 weeks of in vitro culture, MS medium with 1.0 mg l-1 2,4-D and no BA showed
a remarkable degree of callus formation. Indeed, the callus obtained from in vitro culture was faster growing, delicate, mostly spongy, and white creamy in color (Table 1). In addition, with 0.5 mg l-1 2,4-D concentration, slightly more explants grew callus, and grew more callus per explants, when using MS medium supplemented with 0.5 mg l-1 and 1.0 mg l-1 BA. This studies is in broad agreement with Nayak (2000) and Hosoki andSagawa(1977) who reported the callus induction from leaf base as explants of curcuma aromatica using 1.5 mg l-1 2,4-D and 1.0 mg l-1BA. However, in this system it is yet to be evaluated if BA will enhance the highest level of callus production seen with 1.0 mg l-1 2,4-D.
Table 1: The average number of explants induced to form callus (as 5%) and
degree of callus per explants after culturing for 4 weeks. MS medium plus % of explants
induced callus Degree of Callus response
2,4-D 0.5 mg l-1 53.4 a 2,4-D 1.0 mg l-1 78.2 c 2,4-D 2.0 mg l-1 56.7 a
2,4-D 0.5mg l-1+BA 0.1mg l-1 49.86 a 2,4-D 0.5 mg l-1+BA 0.5mg l-1 66.67 b 2,4-D 0.5 mg l-1+BA 1.0mg l-1 61.34 b
a – Slight callusing, b – more callusing, and c – Profuse callusing
Callus obtained from in vitro cultivation (Table 1) were used to investigate the influence
of growth regulators on the induction of somatic embryogenesis.
Four-week old callus were used for multiplying new shoot by culturing them on MS basal
medium supplemented with 0.1 mg l-1NAA, 15% (v/v) coconut water and varying levels
(viz.1.0, 2.0,3.0, 4.0, and 5.0 mg l-1) of BA. All independent in vitro cultures suggested that
the observed effect of BA was likely to be mediated via inducing new shoots, as summarized
in Table 2. After six weeks, new shoots regenerated from callus. MS medium supplemented
with 0.1 mg l-1,15% CW and 5.0 mg l-1 BA gave the highest average levels of new shoots
attained per callus (5.1 ± 0.54) as worked of Dekker et al. (1991) did with ginger but Jala
(2011) cultured Curcuma longa in MS medium supplemented with 2 mg l-1 BA.
Callus obtained from above mentioned medium are used to investigate the influence of
the growth retardant (paclobutrazol) on the growth of somatic embryogenesis.
108 Anchalee Jala
After six weeks, callus was regenerated to new shoots on MS medium supplemented with
0.1 mg l-1 NAA and 5.0 mg l-1 BA. On this condition, callus could form somatic embryos and
germinated (Table 2) within 6 weeks, allowing this system to investigate the effect of
paclobutrazol.
Table 2: Number of new shoots regenerated from callus that are cultured on MS medium
supplemented with 0.1 mg l-1 NAA 15% (v/v) coconut water with varied BA concentrations. NAA (mg l-1) CW(%) BA(mg l-1) Number of new shoots*
0.1
15
1.0 1.2 e 2.0 1.8 d 3.0 2.5 c 4.0 3.2 b 5.0 5.1 a
* Means within columns with different letters are significantly different by using DMRT at the ( p ≤ 0.05) level.
Table 3: The effect of paclobutrazol and coconut water on somatic embryo regeneration from callus within 8 weeks of in vitro culturing.
MS+NAA0.1mgl-1, BA5.0mgl-1, 4 %sucrose (control) plus
No. of multiple shoots*
No. of leaves
per plantlet *
The leaf length (cm)*
The leaf width (cm)ns
Length of leaf petriole (cm)*
Control ( no Paclobutrazol ) 4.00 c 7.82 a 3.80ab 0.73 5.53 a Paclobutrazol 0.01 mg l-1 3.00 d 7.67 a 3.65 b 0.84 4.11 b Paclobutrazol 0. 1 mg l-1 5.00 b 6.92 ab 3.37 bc 0.78 3.36 cd Paclobutrazol 1.0 mg l-1 4.00 bc 7.08 ab 3.53 b 0.86 3.97 bc Paclobutrazol 5.0 mg l-1 3.75 c 5.86 c 3.97 a 0.75 4.40 b Paclobutrazol 10.0 mg l-1 4.50bc 7.54 a 3.25 c 0.85 3.61 c Paclobutrazol 0.01 mg l-1+ cw15% 7.25 a 4.57 c 4.33 a 0.87 3.60 c Paclobutrazol 0.1 mg l-1 + cw15% 3.75 c 5.48 c 3.66 b 0.82 3.70 c Paclobutrazol 1.0 mg l -1 +cw15% 6.25 a 5.33 c 4.08 a 0.82 3.41 cd Paclobutrazol 5.0 mg l -1 +cw15% 3.00 de 6.60 bc 3.93 a 0.79 2.51 de Paclobutrazol 10.0 mgl-1 +cw15% 2.5 e 2.65d 2.15 d 0.91 e *Means within columns with different letters are significantly different from each other (p ≤ 0.05),
ns - no significant difference for that trait across all culture conditions.
4.1 Effect of plant growth (retardant – paclobutrazol) on somatic embryo The callus were used to investigate the influence of the growth retardant (paclobutrazol)
on growth of somatic embryogenesis by cultured them in MS basal medium supplemented
with 0.1 mg l-1 NAA, 5 mg l-1 BA, with and without 15% (v/v) coconut water (CW) and
varied concentrations of paclobutrazol (viz. 0.01, 0.1, 1.0, 5.0 and 10 mg l-1) for 8 weeks.
During this time the number of multiple shoots, number of leaves, leaf length, leaf width, and
length of each leaf petriole were measured as shown in Table 3.
*Corresponding author (A. Jala). Tel/Fax: +66-2-5644440-59 Ext. 2450. E-mail address: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/105-110.pdf
109
Results indicated that paclobutrazol and 15% (v/v) CW both induced a significant
difference (p≤ 0.05) on plantlet regeneration as measured by four out of five traits (Table 3).
Only the average leaf width showed no statistically significant differences between the
different treatments. However, length of leaf petriole and length of leaf were not significantly
different. The numbers of leaves per plantlet were all broadly decreased by increasing
paclobutrazol concentrations, especially in the presence of coconut water where the broadly
similar effects at lower concentrations of paclobutrazol in the presence of 15% (v/v) coconut
water.
However, with respect to the number of adventitious shoots the situation is less clear with
no clear trend. In general, Paclobutrazol alone showed no clear if any concentration dependent effect upon the number of multiple shoots per explants but, the data for Paclobutrazol in the presence of 15% (v/v) coconut water is more erratic with the two highest average number of shoots per explants (7.25 shoots and 6.25 shoots for 0.01 and 1.0 mg/l Paclobutrazol, respectively) intermixed amongst lower values with the lowest attained with 10 mg/l Paclobutrazol. Direct somatic embryogenesis has been reported in many other plants (e.g. Nadguada et al.1978, Salvi et al. 2000, Shiqurkar et al. 2001, Yasuda et al. 1988).
5. Conclusion Immature nodal explants of Curcuma var. Chattip could develop callus after in vitro
culturing in MS medium supplemented with 1.0 mg l-1 2,4-D within 4 weeks. Subculturing callus in MS basal medium supplemented with 0.1 mg l-1 NAA, 15% coconut water and 5.0
mg l-1 BA yielded the highest number of new shoots. These could be further induced somatic embryo regeneration and leaf formation by in vitro culturing in MS medium supplemented with 0.1 mgl-1 NAA, 5.0 mgl-1 BA, 15% (v/v) coconut water and (0.01 to10.0 mg l-1) paclobutrazol. Although equivocal as complex interactions may be occurring, the data suggests that 0.01 and 1.0 mg l-1 paclobutrazol and 15% (v/v) CW were the most suitable conditions leaded to formation of the highest number of new shoots (7.25 and 6.25 shoots per explants, respectively) within 6 weeks.
6. References Balachandran S.M., Bhat S. and Chandel K. 1990. In vitro clonal multiplication of turmeric
(Curcuma spp.) and ginger (Zingiber officinale Rosc.). Plant Cell Rep, 8: 521-524.
110 Anchalee Jala
Dekkers, A.J.; Rao A.N. and Goh C.J. 1991 In vitro strorage of multiple shoot cultures of gingers of ambient temperatures of 24-29 C, Sci Hort, 47:157–168.
Hosoki, T. and Y. Sagawa. 1977. Clonal propagation of ginger (Zingiber Rosc.) Through tissue culture. Sci Hort., 12: 451–452.
Jala, A. 2011. Effects of NAA BA and Sucrose on Shoot Induction and Rapid Micropropagation by Trimming Shoot of Curcuma Longa L. INT TRANS J ENG MANAG SCI TECH, 3(2):101-109.
Jantan, I.B., Yassin M.S.M., Chin C.B., Chen L.L., Sim N.L. 2003. Antifungal activity of the essential oils of nine Zingiberaceae species. Pharmaceutical Biology, 41: 392-397.
Jen-kun Lin, Shoei-Yn and Lin-Shiau. 2000. Mechanisms of Chemoprevention by Curcumin. Proc. Natl. Sci. Counc. ROC (B), 25(2): 59 – 66.
Jitoe, A., Toshiya. Masuda, I. G. P. Tengah, Dewa N. Suprapta, I. W. Gara, Nobuji. Nakatani. 1992. Antioxidant activity of tropical ginger extracts and analysis of the contained curcuminoids. J. Agri. Food Chem., 40:1337- 1340.
Kikuzaki, H. and Nakatani, N. 1993. Antioxidant effects of some ginger constituent. J. Food Sci, 58:1407-1410.
Murashige, T. and Skoog, F. 1962. A revised medium for rapid growth and bioassays with Tobacco tissue cultures. Physiol. Plant, Compenhagem, 15:473–479.
Nadguada, R.S.Mascarenhas,A.F., Hendre, R.R. and Jagannathan, V. 1978. Rapid multiplication of turmeric (Curcuma longa L). Ind. J. Exp. Biol., 16:120-122.
Nayak, S. 2000. In vitro multiplication and microrhizome induction in Curcuma aromatica Salisb. Plant Growth Regul. 32:41-47.
Salvi, N.D., George, L. and Eapen, S. 2000. Direct regeneration of shoot from immature inflorescence culture of turmeric. Plant Cell Tiss. Org. Cult., 62:235-238.
Shigurkar, M.V., John, C.K. and Nadgauda, R.S. 2001. Factors affecting in vitro microrhizome production in turmeric. Plant Cell Tiss. Cult. 64:5-11.
Yasuda K., Tsuda T., Shimizu H., and Sugaya A. 1988. Multiplication of Curcuma species by tissue culture, Planta Medica, 54: 75-79.
Dr.Anchalee Jala is an Associate Professor in Department of Biotechnology, Faculty of Science and Technology, Thammasat University, Rangsit Campus, Pathumtani , THAILAND. Her teaching is in the areas of botany and plant tissue culture. She is also very active in plant tissue culture research.
Peer Review: This article has been internationally peer-reviewed and accepted for publication according to the guidelines given at the journal’s website.
*Corresponding author (S.Thammaniwit). Tel/Fax: +66-2-5643001 Ext.3095, 3038. E-mail address: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/111-128.pdf
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International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies
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Application of Bender’s Decomposition Solving a Feed–mix Problem among Supply and Demand Uncertainties Somsakaya Thammaniwit a*and Peerayuth Charnsethikul a
a Department of Industrial Engineering, Faculty of Engineering, Kasetsart University, THAILAND.
A R T I C L E I N F O
A B S T RA C T
Article history: Received 23 August 2012 Received in revised form 19 December 2012 Accepted 16 January 2013 Available online 24 January 2013 Keywords: Large-scale Feed-mix Problem; Bender’s Decomposition; Two-stage Stochastic Programming.
The feed-mix problem is primarily transformed into a mixing situation applying a mathematic formulation with uncertainties. These uncertainties generate the numerous expansions of alternative constraint equations. The given problem has been formulated as mathematic models which correspond to a large-scale Stochastic Programming that cannot be solved by the most popular ordinary calculation method, Simplex Method: LINPROG. This research aims to investigate effective methodology to reveal the optimal solution. The authors have examined the method of Bender’s decomposition: BENDER and developed both methods into MATLAB® program and calculated comparatively. The results revealed that the nearest optimal solutions can be determined by means of a Two-stage Stochastic Programing incorporated with Bender’s decomposition at the most intensive number of uncertainties and take less calculation time than by LINPROG.
2013 INT TRANS J ENG MANAG SCI TECH.
1 Introduction
Many animal food mixing industries are confronted with a decision making problem on
an appropriate recipe. That is to say that the determination of raw materials which contain
various kinds of feed ingredients added to the process are influenced by expectations of
2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.
112 Somsakaya Thammaniwit, and Peerayuth Charnsethikul
obtaining a product with lower costs, standardization, or surpass nutritive requirement. Such
a feed mix problem is complex and cannot be solved by traditional calculation methods.
There are many approaches which have been applied, such as Pearson’s square method which
is very suit for only two-feed ingredients to be mixed [9] and Trial and Error which is one of
the most popular means for feed formulation but it consumes a lot of time for calculation
[4],[9]. Classical Linear Programming (LP) is widely used for modeling the animal feed
problem. The normal objective in formulating the feed mix is to minimize cost subject to
adequate nutrient ingredients (input raw materials) and the required nutrient constraints
(output nutrient values) [1]. However, due to the various constraints that need to be
conserved, the problem has been extended to become a large-scale problem with
uncertainties. Therefore, when using LP method, it is difficult to determine a good balance of
nutrients in the final solution. The constraints in LP are also rigid leading to an infeasible
solution [1], [2]. However, LP has a positive highlight as a deterministic approach, because it
can provide the best solution of hundreds of equations simultaneously [14]. By Stochastic
approaches, there are also various methods that have been applied for such a complex
problem e. g: Chance Constraint (CCP) and Quadratic Programming (QP), Risk Formulation,
and Genetic Algorithm (GA). In addition to these mentioned methods, there are also some
methods with other possible algorithms such as Integrated LP and Dynamic Programming
(DP), Integrated LP and Fuzzy, Integrated GA and Fuzzy, Integrated GA and Monte Carlo
Simulation. All of these methods are arranged as Integrated approaches [13].
This research does not take into consideration all the above mentioned issues, but aims to
investigate another new effective calculation method for the feed mix problem and proposes
the application of Two-stage Stochastic Linear Programming (TSL) incorporated with the
method of Bender’s Decomposition (BENDER). Hence, this paper describes the preliminary
stage of mathematic formulation, the setup of matrix systems and program development,
MATLAB@ program, and represents the optimization results of a case study.
2 Problem Analysis and Methodology
The classical diet problem is considered as a Linear Programming problem with general
LP matrix: TMin Z = C X, Subject to AX = b, and X 0 for all.≥ Because of the limitation of
the calculation devices, the prior results were revealed without regard for some variables with
*Corresponding author (S.Thammaniwit). Tel/Fax: +66-2-5643001 Ext.3095, 3038. E-mail address: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/111-128.pdf
113
high variance constraint coefficients. Nowadays, because of the higher performance of
computational calculation, the development of the mentioned LP model when the system
uncertainties are taken into account can be written as T T TMin Z = C X + g U + h V
subject to AX + U - V = b x , u, v 0, ≥
(1)
where TC X represents the main cost and T Tg U + h V the additional corrective costs
of materials supplied subject to AX + U - V = b , where A is the coefficient of the
decision variable X (material quantity), U and V stand for the least and the excess mixed
output quantities respectively. Awareness of nutrition values contained in U and V have an
effect on the RHS as well. This issue will be discussed in the subtopic 2.3 later. Meanwhile
the Two-stages Linear Programming Model [6], [7], [10], [11], [16] can be written as:
Maximize Qk n
z = E [c ] x + P [ c x ]qj j qj qjq=1j=1 j=k+1∑ ∑ ∑ (2)
Subject to
1st.Stage k
a x = b for i = 1, 2,...,ij j ij=1r∑ (3)
2nd.Stage k k
a x + a x = bqij j qij qj qij=1 j=1∑ ∑ (4)
i = r +1,...m for q = 1,2,...,Q x 0; x 0,j qj≥ ≥
where Pq probability of occurrences of scenerio q (q=1,2,…,Q) xqj extent variable in the 2nd.stage at constraint j by event q
Notation 1. The value of each random element is independent of the levels of all x j
2. The levels of x j for j = 1, 2 ,…, k ≤ n must be fixed at the 1st. stage
3. The constraint i = 1, 2… r contains only the 1st.stage variables, and the associated aij
114 Somsakaya Thammaniwit, and Peerayuth Charnsethikul
and bi are known with certainty.
4. The variables of xqj in the 2nd. Stage, where i = r +1, 2, …, m and q = 1, 2, …, Q
5. The values of cqj , aqij and f bqi , for i = r+1, 2, …, m and j = 1, 2 ,…, n are
represented by the set of ( cqj , aqij , bqi ) with probability Pq , for q = 1, 2, …, Q
2.1 The problem analysis The animal food mixing as shown below in Table 1 was discussed by a production team.
The problem is to determine the optimal quantities of the three main input raw materials to be
added to the mixing process.
Table 1: The input ingredient amounts represented as x1, x2, and x3 were unknown. Protein (%) Calcium (%) cost / unit
x1 Crushed dried fish 51-53 10-11 70Baht x2 Tapioca 2-3 5-7 40Baht x3 Sorghum - 8-9 23Baht m Market required between19-20 Not less than 8 Note: Baht is the currency used in Thailand (As of January 2013, 30Baht = US$1).
The market demand d = 1.000 ± 0.001 unit weigh t
Let x1, x2 and x3 be the non-negative quantities of the crushed dried fish, tapioca and
sorghum respectively. They are mixed to yield 1 unit of the minimum cost diet that satisfies
all the specified nutritional requirements m =2 prescribed as following:
protein not in excess of 20 % (Upper Boundary)
not less than 19 % (Lower Boundary)
calcium not less than 8 %
Incremental corrective action cost of nutrient value, respectively
FEXD = fexd = 7 Baht/ Unit of protein
FLES = fles = 5 Baht/ Unit of protein
FEXD = fexd = 7 Baht/ Unit of calcium
FLES = fles = 4 Baht/ Unit of calcium
Incremental corrective action cost of ingredient (raw material), respectively
FEXDD = fexdd = 30 Baht/ kg
FLESD = flesd = 5000 Baht/ kg
*Corresponding author (S.Thammaniwit). Tel/Fax: +66-2-5643001 Ext.3095, 3038. E-mail address: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/111-128.pdf
115
2.2 Methodology Such a problem is a typical large-scale stochastic linear programming with full system
uncertainties (tolerances). The decision values of variables xj are decided by the coefficient
of a ± tol, a ± tol, ... , a ± toln1 2 , right-hand-side ( RHS) parameter vector, the nutrition values
bi of b ±tol, b ±tol, ... , b ±tolm1 2 and moreover, the size of the market demand d ±tol as
Figure1 below:
Figure 1: The problem type: A-B-D Uncertainty [15].
2.3 Model Formulation To formulate the given problem in the form of TSL_ model and to allocate the
calculation matrix system, some occurrence possibilities were assumed as follows:
Assumptions A):
PN the occurrence possibility for each nutrient constraint
PD the occurrence possibility for each demand constraint
PN = PD = P (Point) for this case study, the distributions of the probability of PN and PD are
assumed to be uniform distributions. Thus, the possibilities PN and PD will be
equal and also equal to P (Point) where the P (Point) is the initial input number
for allocating the division number of all system uncertainty intervals.
E incremental event step, for this study, E = PN x PD
Constr Constraint, C = m x Event + PD
Var Variable, Var = n + 2Constr
116 Somsakaya Thammaniwit, and Peerayuth Charnsethikul
Assumptions B):
The lower and upper boundaries of all constraint variables are allocated from the middle
point of their tolerances:
(mid) (tol) (mid) (tol)ij ij ij ij ij
(mid) (tol) (mid) (tol)i i i i i
(mid) (tol) (mid) (tol)k
(mid) (tol) (mid) (tol) (mid) (tol)ij ij i i
a a - a ; a + a
b b - b ; b + b
d d - d ; d + d
a ; a ; b ; b ; d and d R
⎡ ⎤∈ ⎣ ⎦⎡ ⎤∈ ⎣ ⎦⎡ ⎤∈ ⎣ ⎦
∈
(5)
Referring to the previous notations (1), (2), (3), (4) and (5) including analyzing the above
given diet problem in Table 1, the calculation models were developed to be (6), (7) and (8).
The objective function of the given problem was to minimize the total cost z min. which is the
sum of the raw material unit cost cj multiplied by the amount of xj, plus the sum of all
necessary incremental corrective action costs for both qualitative and quantitative values not
meeting the specification, Equation (6). The sum of the product of the quantity xj and its
uncertain coefficients ija l including the sum of nutritive slack ui kl and surplus vi kl of each
calculation scenario have to be balanced to the right-hand-side RHS , i.e. the vectors of
required nutritive value of ib l multiplied by the demand d , in Equation (7). Simultaneously,
the LHS, the sum of xj and addition of the slacks uk and surplus vk has to be associated with
the quantity requirement of d. In practice, the demand d cannot be exactly equal to 1 unit, but
rather it comprises an allowance of ± 0.001 in unit weight (0.1% error).
Minimize z n m P P P
c x + (g u + h v ) + (g u + h v )j j i k i k i k i k k k k k=1j=1 i=1k=1 k=1′ ′∑ ∑ ∑ ∑ ∑l l l ll
(6)
Subject to
n
a × x + u - v = b × dij j i k i k i kj=1∑ l l l l ; i k∀ l (7)
k k k
nx + u v = djj=1
′ ′−∑ ; k∀ (8)
x ; u ; v ; u ; v 0j i k i k k k′ ′ ≥l l ; i, j, k∀
Where
*Corresponding author (S.Thammaniwit). Tel/Fax: +66-2-5643001 Ext.3095, 3038. E-mail address: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/111-128.pdf
117
k denotes the constraint alternatives
j denotes the type of ingredient to be input to mixing process ( j=1,2,…,n ).
i denotes the type of nutrient composition which the market needs ( i=1,2,…,m)
cj denotes the cost factor of raw material j- type (cost/ unit weight = fx )
xj denotes the quantity of raw material j- type (weight unit = kg ).
aij denotes the nutrient value type i in material type j
bi denotes the nutrient value (of product) type i /unit weight.
ui kl denotes the nutrient value (of product) i , at the event l which misses (slack )
the target in the alternative k
vi kl denotes the nutrient value (of product) i , at the event which exceeds
(surplus ) the target in the alternative k
i kg l denotes the expected cost of nutrient value i / unit which misses the target
alternative k . FLES = fles
i kh l denotes the expected cost of nutrient value i / unit which exceeds the target
alternative k . FEXD = fexd
gk denotes the expected cost of ku′ (by lack of demand) = FLESD
hk denotes the expected cost of kv′ (by exceeding demand) = FEXDD
ku′ denotes the lower quantity of raw material in the alternative k
kv′ denotes the excess quantity of raw material in the alternative k
dk denotes the market demand 1.000 weight unit with the standardized allowance
of ± 0.001 in unit weight (0.1 % error) for animal food production.
2.4 Minimum Cost Calculation Model Minimize z =
mFLES FEXD FLESD FEXDD
P P P(fx x + fx x + fx x ) + ( u + v ) + ( u + v )1 1 2 2 3 3 i k i k k k=1i=1 k=1 k=1
′ ′⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅∑ ∑ ∑ ∑l ll (9)
Subject to
n
a × x + u - v = b × dij j i k i k i kj=1∑ l l l l ; i k∀ l
l
118 Somsakaya Thammaniwit, and Peerayuth Charnsethikul
k k k
nx + u - v = djj=1
′ ′∑ ; i,k∀
x ; u ; v ; u ; v 0j i k i k k k′ ′ ≥l l ; i, j, k∀
Substitute the given data from Table 1 into the minimal cost calculation model above
( )
2 P P(70x + 40x + 23x ) + ((5,4) u + (7,7) v ) +1 2 3 i k i k=1i=1 k=1P
5000 u + 30 vk kk=1
Minimize Z ⋅ ⋅∑ ∑ ∑
′ ′⋅ ⋅∑
= l ll
Subject to
[51--53]x + [2--3]x + [0]x +u - v = [19--20] ,k1 2 3 1 k 1 k[10--11]x + [5--7]x + [8--9]x + u - v = 8 ,k1 2 3 2 k 2 k
x + x + x + u - v = 1±0.001 k1 2 3 k k' 'x , x , x 0, u , v 0 i, ,k1 2 3 i k i k
∀
∀
′ ′ ∀
≥ ≥ ∀
ll l
ll l
ll l
1st.Event; for 1, =1, k =1i = l (at lower boundary)
51x + 2x + 0x +u v = 19 (0.999)1 2 3 111 11110x + 5x + 8x + u - v = 8 (0.999)1 2 3 211 211x + x + x + u - v = 0.9991 2 3 1 1
− ⋅
⋅
′ ′
2nd.Event; for 2, = 2, k = 2i = l (at middle value)
52x + 2.5x + 0x +u - v = 19.5 (1.000)1 2 3 122 12210.5x + 6x + 8.5x + u - v = 8 (1.000)1 2 3 222 222x + x + x + u - v = 1.0001 2 3 2 2
⋅
⋅
′ ′
3rd.Event; for 3, = 3, k = 3i = l (at upper boundary)
53x + 3x + 0x + u - v = 20 × (1.001)1 2 3 133 13311x + 7x + 9x + u - v = 8 × (1.001)1 2 3 233 233x + x + x + u - v = 1.0011 2 3 3 3′ ′
The above formulations just demonstrate how to set up only three events (l): lower
boundary (LB), middle value, and upper boundary (UB). But, in this research, the calculation
models were planned to be set up throughout the tolerance intervals of all relevant variables
*Corresponding author (S.Thammaniwit). Tel/Fax: +66-2-5643001 Ext.3095, 3038. E-mail address: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/111-128.pdf
119
and divide those with the same number of P, by which the resolution (res), through all
uncertainty intervals can be definable. One must beware of the LB and UB of each event
which correspond with the given tolerances.
2.5 The TSL incorporated with Bender’s Decomposition The concept of the Bender’s decomposition is to predict the second-stage costs by a
scalar θ and replace the second-stage constraints by cuts, which are necessary conditions
expressed in terms of the first-stage variables x and θ [6], [10], [12]. The initial model can be
modified, and written as shown below:
The 1st.stage: Primal problem
T T TMin Z = C X + g U + h V
subject to AX + U - V = b
x , u, v 0, ≥ (10)
The 2nd.stage: The Dual Bender’s Decomposition
( )
( )
( )
U - V = b - AX
b - AX 0T TMax ω = b - AX Y + C X
Tθ = b - AX
T T Tcond = cond; θ+ Y A-C X b Y
≥
∑
≥⎡ ⎤⎣ ⎦
(11)
( )Min θ+X;
T T TSubject to θ + Y A-C X b Y≥ (12)
Stop condition Check θ = ? No Get 1X, 2X,.....
Yes Get θ and X Min Z = Max ω Stop
Xnewω → →
→ ⇒ ⇒
The selected algorithm to attain a satisfactory solution was the integration between
TSL and Bender’s decomposition. As shown in Figure 2, the main concept of Bender’s
decomposition is to split the original problem into a master problem and a sub-problem,
which in turn decomposes into a series of independent sub problems, one for each. The
latter are used to generate cuts. The X initial is to be randomly selected to substitute in terms of
120 Somsakaya Thammaniwit, and Peerayuth Charnsethikul
the constraints inequality equation. If the result, by substitution of X initial, is equal to or
greater than 0, then iy = gi. If the result (substitution of X initial) is less than 0, then iy = - h.
The xinitial and the selected yi are substituted in the Dual equation and in the inequality (to
generate the optimal cut) to attain the maximum value of ω and θ respectively. By
minimization of the + 0Xnew ; subject to newT T Tθ (y A-c ) x b y+ ⋅ ≥ , Equation (12) renders
θ and x new . If the obtained and are equal at the step of convergence test, and are
to be approximately equivalent to zDual which can be obtained as an optimal solution.
Figure 2: TSL incorporated with Bender’s decomposition [6], [10], [11], [15].
2.6 Calculation Tools
The mathematical calculation tool, MATLAB® Program was selected to solve this
problem. The MATLAB®_Software / Verion.2006a and a HP_Pavillion_IntelCore_2Qurd
Inside, No.: 016-120610000, personal computer, at the Department of Industrial Engineering,
θ
ω θ ω θ
*Corresponding author (S.Thammaniwit). Tel/Fax: +66-2-5643001 Ext.3095, 3038. E-mail address: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/111-128.pdf
121
Thammasat University, Pathumtani were used. The calculation steps were as follows
Step1. In accordance with the mathematic formulation, previous topic 2.4, the matrices
systems were established according to the ordinary simplex method. The initial input
variable matrix was expressed in two possible substitutions: randomization and dual-
simplex algorithm.
Step2. Model building according to the method of Bender’s decomposition followed the
programming flowchart as shown in Figure 2.
Step3. Preparation of MATLAB® programming named: lstart, start, setupmodel, solvemodel;
linMAT, bender, dataABD_Uncertainty.mat, result, pline, selfcat, speye, and vspace
Step4. Creation of data file: a, a_tol, b, b_tol, d, d_tol, fx, fles, fexd, flesd and fexdd
Step5. Program execution by the program named: lstart (input parameter is P-Number).
2.7 Computational Calculation To solve the above formulated problem, the primal-dual Simplex method /LINPROG,
Equations (1) was applied to compare with two-stage stochastic linear programming
incorporated with the method of Bender’s Decomposition, Equations (9), (10), (11), (12)
referred to Equations (2), (3), (4), (6), (7), and (8).
Assumptions:
1. The quantity of each nutrient value in each type of raw material is a continuous
event. The values, which are independent of each other, are uncertain.
However, the values intervals are recognized to be uniform distributions.
2. All scenarios are also uniformly distributed and independent on each other.
As the solving tools for comparative calculation, the results of both selected method can
be collected after the calculation iteration has terminated. The programs for primal-dual
simplex and Bender’s decomposition were developed. The starting program referred to as
‘Start.m’ constitutes the main application to perform the execution of all relevant functions.
The matrix systems corresponding with Equations (9), (10), (11), (12) are established. The
program referred to as ‘linMAT.m’ is a matrix to receive all loaded input data whereas the
122 Somsakaya Thammaniwit, and Peerayuth Charnsethikul
program name: ‘Speye.m’ serves to construct a large identity matrix system with the
‘Selfcat.m’ application to await the parameter patterns modification prior to the continuation
of the Bender’s decomposition program ‘Bender.m’ [15].
Table 2: Lower section cut off at the uncertainty number of P=2:2:50.
P Event Constr Var x1_BEN x2_BEN x3_BEN Sx_BEN Z_BEN Ti_BEN x1_LIN x2_LIN x3_LIN Sx_LIN Z_LIN Ti_LIN2 4 10 23 0.3631 0.2420 0.3949 1.0000 48.61 0.4924 0.3631 0.2420 0.3949 1.0000 48.61 2.33994 16 36 75 0.3633 0.2422 0.3946 1.0000 48.60 0.1767 0.3633 0.2422 0.3946 1.0000 48.60 0.05586 36 78 159 0.3632 0.2422 0.3947 1.0000 48.59 0.1995 0.3632 0.2422 0.3947 1.0000 48.59 0.08688 64 136 275 0.3617 0.2761 0.3622 1.0000 48.57 0.1284 0.3617 0.2762 0.3621 1.0000 48.57 0.2207
10 100 210 423 0.3617 0.2768 0.3615 1.0000 48.54 0.1818 0.3617 0.2766 0.3617 1.0000 48.54 0.362012 144 300 603 0.3617 0.2769 0.3614 1.0000 48.52 0.2010 0.3617 0.2769 0.3615 1.0000 48.52 0.877414 196 406 815 0.3618 0.2739 0.3643 1.0000 48.52 0.1472 0.3618 0.2739 0.3643 1.0000 48.52 1.467016 256 528 1059 0.3617 0.2762 0.3621 1.0000 48.51 0.1660 0.3617 0.2762 0.3621 1.0000 48.51 1.192018 324 666 1335 0.3617 0.2761 0.3621 1.0000 48.51 0.1456 0.3617 0.2765 0.3618 1.0000 48.51 0.950120 400 820 1643 0.3617 0.2766 0.3617 1.0000 48.51 0.1393 0.3617 0.2766 0.3617 1.0000 48.51 0.260722 484 990 1983 0.3617 0.2764 0.3619 1.0000 48.50 0.1514 0.3617 0.2768 0.3616 1.0000 48.50 0.361124 576 1176 2355 0.3617 0.2768 0.3615 1.0000 48.50 0.1820 0.3617 0.2769 0.3614 1.0000 48.50 0.704526 676 1378 2759 0.3617 0.2763 0.362 1.0000 48.50 0.1695 0.3617 0.2763 0.362 1.0000 48.50 1.548028 784 1596 3195 0.3617 0.2763 0.362 1.0000 48.50 0.1490 0.3617 0.2765 0.3618 1.0000 48.50 0.627930 900 1830 3663 0.3617 0.2763 0.362 1.0000 48.50 0.1589 0.3617 0.2766 0.3617 1.0000 48.50 0.717232 1024 2080 4163 0.3617 0.2769 0.3614 1.0000 48.49 0.1537 0.3617 0.2767 0.3616 1.0000 48.49 0.502334 1156 2346 4695 0.3617 0.2767 0.3616 1.0000 48.49 0.2377 0.3617 0.2768 0.3615 1.0000 48.49 0.661336 1296 2628 5259 0.3617 0.2769 0.3614 1.0000 48.49 0.1867 0.3617 0.2769 0.3614 1.0000 48.49 2.270738 1444 2926 5855 0.3617 0.2765 0.3618 1.0000 48.49 0.1819 0.3617 0.2765 0.3618 1.0000 48.49 1.272740 1600 3240 6483 0.3617 0.2770 0.3614 1.0000 48.49 0.1650 0.3617 0.2766 0.3617 1.0000 48.49 0.864842 1764 3570 7143 0.3617 0.2764 0.3619 1.0000 48.49 0.1649 0.3617 0.2767 0.3616 1.0000 48.49 1.172444 1936 3916 7835 0.3617 0.2767 0.3616 1.0000 48.49 0.1892 0.3617 0.2768 0.3615 1.0000 48.49 0.841146 2116 4278 8559 0.3617 0.2768 0.3615 1.0000 48.49 0.1923 0.3617 0.2768 0.3615 1.0000 48.49 3.180748 2304 4656 9315 0.3617 0.2768 0.3615 1.0000 48.49 0.1462 0.3617 0.2769 0.3614 1.0000 48.49 3.491950 2500 5050 10103 0.3617 0.2767 0.3616 1.0000 48.49 0.1504 0.3617 0.2766 0.3617 1.0000 48.49 2.8000
Figure 3: The congruence of x1_BEN vs. x1_LIN; x2_BEN vs. x2_LIN and x3_BEN vs.
x3_LIN, x3_LI
*Corresponding author (S.Thammaniwit). Tel/Fax: +66-2-5643001 Ext.3095, 3038. E-mail address: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/111-128.pdf
123
3 Results and Discussion
The calculation results were enumerated to check the calculation efficiencies of both the
applied methodologies and the programming development. To be discussed in this research
paper are the expected values of all response factors and their calculation times on a limited
set of personal computers. Hence, there are three sections discussed as follow:
3.1 The result at the lower section (P = 2:2:50) P = 2:2:50 denotes for the value of P starting at 2: increasing step 2: and ending at 50.
According to assumption a) on page 6, the incremental event step E = P2 for uniform
distribution and constraint number C = m × Event + PD. The values of x1_BEN, x1_LIN;
x2_BEN, x2_LIN; x3_BEN, x3_LIN are congruent and consistent variants as represented in
Table 2 and in Figure 3. The results of Zmin_BEN and Zmin_LIN represent their respective
congruencies and at P= 32 (in Figure 4, at 16 on the axis) . However, consideration of the
series plot of Ti_BEN and Ti_LIN in Figure 5 shows the calculation time fluctuations of
Ti_LIN, but not for Ti_BEN.
Number of point(P=2:2:50) divided in all uncertainties intervals
Calc
ulat
ion
tim
e_ T
i
24222018161412108642
4
3
2
1
0
VariableTi_BENTI_LIN
Time Series Plot of Ti_BEN, TI_LIN
Figure 4: Zmin_BEN and Zmin_LIN Figure 5: Ti_BEN and Ti_LIN
3.2 The results at the middle section (P = 50:2:106) At the intensive computational events, the values of x1_BEN, x3 _BEN, z_BEN and
x1_LIN, x3 _LIN, z_LIN are stable congruent with a very small consistent variance as
reported in Table 3 below:
124 Somsakaya Thammaniwit, and Peerayuth Charnsethikul
Table 3: Upper section cut off at the uncertainty number P = 50:2:106. P Event Constr Var x1_BEN x2_BEN x3_BEN Sx_BEN Zmin_BEN Ti_BEN x1_LIN x2_LIN x3_LIN Sx_LIN Zmin_LIN Ti_LIN50 2500 5050 10103 0.3617 0.2767 0.3616 1.0000 48.49 0.2884 0.3617 0.2766 0.3617 1.0000 48.49 3.373752 2704 5460 10923 0.3617 0.2766 0.3617 1.0000 48.49 0.1726 0.3617 0.2767 0.3616 1.0000 48.49 1.256954 2916 5886 11775 0.3617 0.2772 0.3611 1.0000 48.49 0.2258 0.3617 0.2767 0.3616 1.0000 48.49 1.769956 3136 6328 12659 0.3617 0.2771 0.3612 1.0000 48.49 0.1420 0.3617 0.2768 0.3615 1.0000 48.49 1.322958 3364 6786 13575 0.3617 0.2768 0.3615 1.0000 48.48 0.1641 0.3617 0.2768 0.3615 1.0000 48.48 2.875560 3600 7260 14523 0.3617 0.2771 0.3612 1.0000 48.48 0.2000 0.3617 0.2769 0.3614 1.0000 48.48 2.325462 3844 7750 15503 0.3617 0.2768 0.3615 1.0000 48.48 0.2491 0.3617 0.2767 0.3616 1.0000 48.48 5.546664 4096 8256 16515 0.3617 0.2767 0.3616 1.0000 48.48 0.2022 0.3617 0.2767 0.3616 1.0000 48.48 1.580366 4356 8778 17559 0.3617 0.2766 0.3617 1.0000 48.48 0.2676 0.3617 0.2768 0.3615 1.0000 48.48 2.535368 4624 9316 18635 0.3617 0.2768 0.3604 0.9989 48.48 0.1525 0.3617 0.2766 0.3607 0.9990 48.48 3.006070 4900 9870 19743 0.3617 0.2764 0.3567 0.9947 48.47 0.2050 0.3617 0.2762 0.3568 0.9947 48.47 6.688472 5184 10440 20883 0.3742 0.0000 0.5166 0.8908 48.39 0.1865 0.3741 0.0000 0.5166 0.8908 48.39 3.082474 5476 11026 22055 0.3742 0.0000 0.5141 0.8882 48.18 0.1798 0.3742 0.0000 0.5140 0.8882 48.18 2.044076 5776 11628 23259 0.3742 0.0000 0.5116 0.8858 47.98 0.1804 0.3742 0.0000 0.5116 0.8858 47.98 1.480678 6084 12246 24495 0.3742 0.0000 0.5094 0.8835 47.78 0.2126 0.3742 0.0000 0.5094 0.8835 47.78 2.000980 6400 12880 25763 0.3742 0.0000 0.5085 0.8827 47.60 0.1892 0.3742 0.0000 0.5085 0.8827 47.60 1.509482 6724 13530 27063 0.3742 0.0000 0.5062 0.8804 47.42 0.1766 0.3742 0.0000 0.5064 0.8806 47.42 1.455084 7056 14196 28395 0.3742 0.0000 0.5045 0.8787 47.24 0.1828 0.3742 0.0000 0.5045 0.8787 47.24 3.014286 7396 14878 29759 0.3742 0.0000 0.5026 0.8768 47.07 0.1794 0.3742 0.0000 0.5026 0.8768 47.07 6.036188 7744 15576 31155 0.3742 0.0000 0.5009 0.8750 46.91 0.1692 0.3742 0.0000 0.5008 0.8750 46.91 5.982990 8100 16290 32583 0.3742 0.0000 0.4991 0.8733 46.75 0.1697 0.3742 0.0000 0.4991 0.8733 46.75 4.676992 8464 17020 34043 0.3742 0.0000 0.4987 0.8729 46.59 0.1991 0.3742 0.0000 0.4987 0.8729 46.59 1.961594 8836 17766 35535 0.3742 0.0000 0.4971 0.8713 46.45 0.1917 0.3742 0.0000 0.4971 0.8713 46.45 4.871796 9216 18528 37059 0.3742 0.0000 0.4956 0.8698 46.30 0.1981 0.3742 0.0000 0.4956 0.8698 46.30 10.819798 9604 19306 38615 0.3742 0.0000 0.4942 0.8684 46.16 0.2047 0.3742 0.0000 0.4942 0.8684 46.16 4.3436
100 10000 20100 40203 0.3742 0.0000 0.4929 0.8671 46.03 0.2016 0.3742 0.0000 0.4929 0.8670 46.03 3.0834102 10404 20910 41823 0.3742 0.0000 0.4925 0.8668 45.90 0.2233 0.3742 0.0000 0.4925 0.8667 45.90 3.4999104 10816 21736 43475 0.3742 0.0000 0.4913 0.8655 45.77 0.1791 0.3742 0.0000 0.4913 0.8655 45.77 7.3899106 11236 22578 45159 0.3742 0.0000 0.4901 0.8643 45.65 0.1778 0.3742 0.0000 0.4901 0.8643 45.65 3.6450
According to the numerical consideration shown in Table 3, the values of x1_BEN ,
x1_LIN; x2_BEN, x2_LIN ; x3_BEN, x3_LIN are congruent and consistent variants. The
optimal values of Zmin_BEN and Zmin_LIN are also congruent and have tendencies to
converge to the most optimal solution. By contrast with this, the calculation times Ti_LIN
and Ti_BEN are very different. The ratio of them is 2.5353:0.2676 = 9.474:1 at P = 66. The
sums of Sx_BEN and Sx_LIN start to decrease after P = 66 as do the Zmin values also.
Figure 6, 7 and 8 show that the components of x3_BEN and x3_LIN were selected , but not
x2_LIN and x2_BEN at P=72.
In an actual factory, the producers can make a decision at this stage with Sx = 0.8908 unit
weight and Zmin = 48.39. If they want to obtain the sum Sx = 1.000 unit weight to meet
demand, they can select the status of P = 66 with the optimal Zmin = 48.48 (higher cost). A
further perspective, Figure 9 represents the series plot of Ti_BEN and Ti_LIN with major
fluctuations. the calculation times of Ti_LIN are extremely variable, whereas the calculation
time Ti_BEN tends to be constant.
*Corresponding author (S.Thammaniwit). Tel/Fax: +66-2-5643001 Ext.3095, 3038. E-mail address: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/111-128.pdf
125
Numer of Point [50:2:106] divided in uncertainty intervals
Mar
ket
dem
and
[Un
it W
eigh
t]
272421181512963
1.000
0.975
0.950
0.925
0.900
0.875
0.850
VariableSx_BENSx_LIN
Time Series Plot of Sx_BEN, Sx_LIN
Numer of Point [50:2:106] divided in uncertainty intervals
Min
imum
cos
t Z
[Un
it C
urre
ncy]
272421181512963
48.5
48.0
47.5
47.0
46.5
46.0
45.5
VariableZmin_BENZmin_LIN
Time Series Plot of Zmin_BEN, Zmin_LIN
Figure 6: Sx_BEN and Sx_LIN Figure 7: Min.cost Zmin_BEN and Zmin_LIN
Numer of Point [50:2:106] divided in uncertainty intervalsSele
cted
x1_
LIN,
x1_
BEN,
x3_
LIN,
x3_
BEN
[Uni
t w
eigh
t]
272421181512963
0.525
0.500
0.475
0.450
0.425
0.400
0.375
0.350
Variable
x1_LINx3_LIN
x1_BENx3_BEN
Time Series Plot of x1_BEN, x3_BEN, x1_LIN, x3_LIN
Number of point(P=50:2:106) divided in all uncertainties intervals
Calc
ulat
ion
tim
e_Ti
272421181512963
12
10
8
6
4
2
0
VariableTi_BENTI_LIN
Time Series Plot of Ti_BEN, TI_LIN
Figure 8: The congruence of x1_BEN, Figure 9: Ti_LIN and Ti_BEN.
x3_BEN,x1_LIN, x1_BEN
3.3 The results at the upper section Continuing the computational calculation by the Bender’s decomposition method with
P = (106:2:1584) until the calculation terminated (out of memory on the HP_Pavillion_
IntelCore_2Quard Inside, No.: 016-120610000, personal computer), the nearest optimal
solution can be accepted at P=1584 corresponding to Zmin of 38.63Baht and Sx_BEN of
0.8185 unit weight. Sx_BEN is not equal to 1 as the market demand. It depends upon the cost
factors of flesd and fexdd . If flesd is less than fexdd, it will be reasonable to produce the
mixed product with lower amount from the demand. But, the optimization can reveal the
lowest value of the Zmin as shown in Table 4 below.
126 Somsakaya Thammaniwit, and Peerayuth Charnsethikul
Table 4: The upper section cut off at the uncertainty number P = (1560:2:1584) P Events Constr Var x1_BEN x2_BEN x3_BEN Sx_BEN Z_BEN Ti_BEN1560 2433600 4868760 9737523 0.3743 0 0.4445 0.8187 38.64 10.95341562 2439844 4881250 9762503 0.3743 0.0002 0.4442 0.8186 38.64 10.29981564 2446096 4893756 9787515 0.3743 0 0.4442 0.8185 38.64 10.38931566 2452356 4906278 9812559 0.3743 0 0.4442 0.8185 38.64 10.50751568 2458624 4918816 9837635 0.3743 0 0.4453 0.8197 38.63 10.48421570 2464900 4931370 9862743 0.3743 0.0001 0.4444 0.8187 38.63 11.0561572 2471184 4943940 9887883 0.3743 0 0.4442 0.8185 38.63 10.07221574 2477476 4956526 9913055 0.3743 0 0.4445 0.8188 38.63 11.05311576 2483776 4969128 9938259 0.3743 0.0002 0.4445 0.8188 38.63 11.04381578 2490084 4981746 9963495 0.3743 0.0011 0.4446 0.8189 38.63 11.3731580 2496400 4994380 9988763 0.3743 0.0055 0.4441 0.8185 38.63 10.5431582 2502724 5007030 1E+07 0.3743 0 0.4445 0.8187 38.63 11.70561584 2509056 5019696 1E+07 0.3743 0 0.4442 0.8185 38.63 10.5942
OUT OF MEMORY
4 Conclusions
At the first start with a lower division number of points P, the results obtained from the
simplex method-LINPROG and Bender’s decomposition were consistently equivalent. The
calculation results were almost identical. These two algorithms are very suitable for small-
scale problems but when increasing the division numbers (point P) there is, a rise of
uncertainties numbers, the consequence of the enlargement of the constraint numbers. Some
of response factors were found to deviate from the target and thus failed in condition.
Nevertheless, the calculation by both methods can be performed. The LINPROG method is
extensive in calculation time and thus requires a large memory storage. On the personal
computer, the calculation failed to determine the results at the earlier stage.
Conversely, the Bender’s decomposition method can quickly and consistently obtain the
nearest optimal solution up to the calculation termination due to being out of memory. The
same problem and the same calculation tool, MATLAB® program, were also performed on a
high performance computer. The results showed the enormous effect of the system
uncertainties mainly influencing the calculation times. It is noteworthy that the ratio of the
mean time consumption of the LINPROG : BENDER is approximately equal to 232.77 :1 at
P=2:2:500 on a general PC, whereas the results of the other response factors can be
congruent.
*Corresponding author (S.Thammaniwit). Tel/Fax: +66-2-5643001 Ext.3095, 3038. E-mail address: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/111-128.pdf
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The authors hope that this calculation method, the integration of Two-stage stochastic linear programming incorporated with the Bender’s decomposition method, compacted in
general form of a MATLAB® programming, can contribute to supporting decision making
in other operations research areas as a low cost effective calculation tool. For future research, this program is to be developed in form of a Graphic User Interface for convenience of use.
5 Acknowledgements
Many thanks go to National Electronics and Computer Technology Center (NECTECH), THAILAND for kindly supporting access into their HPC-calculating system and the TechSourch Co.Ltd. (Thailand) for their research cooperation. Furthermore, the authors thank Mr. Rattaprom Promkham from Mathematics Department, Rachamonkala University of Technology Thanyaburi for programming suggestions.
6 References
[1] A. E. Chappell, (1974): “Linear programming cuts costs in production of animal feeds”, Operation Research quarterly, 25(1): 19-26.
[2] A. G. Munford, (1996): “The use of iterative linear programming in practical applications of animal diet formulation”, Mathematics and computers in Simulation, 42: 255-261.
[3] E. Engelbrecht, (2008): “Optimising animal diets at the Johannesburg zoo”. University of Pretoria, Pretoria.
[4] D. M. Forsyth, (1995): “Chapter 5: Computer programming of beef cattle diet”, in Beef cattle feeding and Nutrition, 2nd ed., T. W. Perry and M. J. Cecava, Academic Press, Inc, pp. 68.
[5] I. Katzman, (1956): Solving Feed Problems through Linear Programming, Journal of Farm Economics, 38(2) (May, 1956): 420-429.
[6] G.Infanger, George B. Danzig, (1993): Planning under uncertainty-Solving Large-Scale Stochastic Linear Programs, Stanford University.
[7] G. Ausiello, P. Crescenzi, G.Gambosi, V.Kann, A. M. Spacecamda, (1998): Complexity and Approximation, Springer, Chapter 2.
[8] Michel X. Goemans, David, P. William son, (1997): The primal-dual method for approximations algorithms and its application to network design problems, PWS Publishing Co., Chapter 4.
[9] M. O. Afolayon and M. Afolayon, (2008) “Nigeria oriented poultry feed formulation software requirements”, Journal of Applied Sciences Research, 4(11): 1596-1602.
128 Somsakaya Thammaniwit, and Peerayuth Charnsethikul
[10] P. Charnsethikul, (2009): Theory of Primal/Dual and Benders’ Decomposition, Lecture notes Department of Industrial Engineering, Kasetsart University, Thailand.
[11] P. Charnsethikul, (1996): Linear Programming with Constraint Coefficient Tolerances, IE Network Conference, Industrial Engineering in AD. 2000, 24-25 October 1996, Pattaya, Thailand: 287-298.
[12] Robert M. Freund, (2004): Benders’ Decomposition Methods for Structured Optimization, including Stochastic Optimization, Massachusetts Institute of Technology.
[13] Rosshairy Abd Rahman, Chooi-Leng Ang, and Razamin Ramli, (2010): Investigating Feed Mix Problem Approaches: An Overview and Potential Solution, World Academy of Science, Engineering and Technology.
[14] S. Babu and P. Sanyal, (2009): Food Security, Poverty and Nutrition Policy Analysis: Statistical Methods and Applications. Washington, DC, USA: Academic Press. p.304.
[15] S. Thammaniwit, (2013): A Stochastic Linear Programming Method for the Diet Problem under Uncertainties, Doctoral Thesis, Submitted to the Senate of Kasetsat University, Bangkhen, Bangkok, Thailand, and December 2013.
[16] Wagner, H. M., 1977. Principles of operations research, with applications to managerial decisions, 2nd Ed., Introduction to Stochastic Programming Models, pp.651-699.
S.Thammaniwit is an Assistant Professor of Industrial Engineering Department at Thammasat University, Thailand. He received his Dipl.Ing. (Konstruktionstechnik: Werkzeugsmaschinen) from The University of Applied Science of Cologne, Germany in 1982. He worked and received on-the-job training with at least 14 German companies before working in the government sector in his country. He earned his Master’s degree in Manufacturing System Engineering under Chula/Warwick corporation program at Chulalongkorn University, Bangkok in 1992. Most of his research is involved with tools, machine tools design and construction as well as Engineering Management. Currently, he is pursuing a doctoral degree at Kasetsart University, Bangkhen, Bangkok, Thailand.
Dr.P. Charnsethikul is an Associate Professor of Industrial Engineering Department, Kasetsart University, Bangkok, Thailand. He received his M.S, PhD. (Industrial Engineering) from Texas Technical University, USA. His research interests are in the area of Optimization, Operations Research, Numerical Mathematics & Statistics, Management Science, Production & Operations, Numerical Methods and Analysis with Applications in Safety Engineering. Since 2006 he has been acting as Deputy Dean of the Faculty of Engineering at Kasetsart University, Bangkhen, Bangkok, Thailand.
Peer Review: This article has been internationally peer-reviewed and accepted for publication according to the guidelines given at the journal’s website.
*Corresponding author (David Kuria). Tel: +254-727-399208. E-mail addresses: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/129-143.pdf
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International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies
http://TuEngr.com
Securing Bank Loans and Mortgages Using Real Estate Information Aided by Geospatial Technologies David Kuriaa*, Moses Gacharia, Patroba Oderab and Rogers Mvuriac
a Department of Geomatic Engineering and Geospatial Information Science, Dedan Kimathi University of Technology, KENYA b Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology, KENYA c Lutheran World Federation, Department for World Service, KENYA A R T I C L E I N F O
A B S T R A C T
Article history: Received 10 November 2012 Received in revised form 25 January 2013 Accepted 28 January 2013 Available online 29 January 2013 Keywords: Banking; GIS; Loan appraisal; Information technology; Mortgage.
Due to liberalization within the financial market, there has been increased cash flow in banks. This has resulted in increased competition among banks to secure and increase their customer base, in an effort to remain profitable. Banks are foregoing the multitude of checks that used to be conducted before granting any mortgage facility to customers, in an effort to remain competitive. This has led to a drastic increase in the number of credit card and loan defaulters, leading to increased operation costs and reduction in profit margins. This research proposes an integrated GIS approach enabling banks locate defaulting real estate properties used as collateral. Using data provided by Kenya Commercial Bank (KCB) for a locality in Kenya, a geodatabase was developed and a custom application developed for the bank loan appraiser to use. This application retrieves property information about a client based on his/her bank account information. Based on a series of spatially driven queries embedded in the solution, the appraiser can prepare a detailed appraisal for the client in a very short time, thereby satisfying the client, while not prejudicing the banks position.
2013 INT TRANS J ENG MANAG SCI TECH.
2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.
130 David Kuria, Moses Gachari, Patroba Odera and Rogers Mvuria
1 Introduction
Geographic Information Systems (GIS) have been defined and conceptualized in a number
of different but related ways. de Man (1988), Goodchild (1992), and Burrough (1986) argue
that GIS is a special type of information system that handles spatial data. Dickinson and Calkins
(1988) take a component view of GIS, arguing that a GIS has three elements: technology
(hardware and software), a database, and infrastructure (staff, facilities, etc.). GIS technology
has a great deal to offer the mortgage finance industry because geographic location and spatial
relationships have a central role in housing and mortgage market outcomes. Housing is fixed in
its location and is durable. As a result, a home’s location relative to employment opportunities,
mortgage finance and housing market intermediaries, public services, and amenities exerts a
strong influence on its price. The location of a home influences the choices and opportunities of
its residents and of those seeking to own it and location is thus a strong influence on mortgage
markets. The location of mortgage suppliers defines the availability of mortgage credit. The
segregation of residential space into discrete geographic submarkets influences the pattern and
nature of mortgage product demand.
The highly segregated nature of residential space demands that mortgage lenders target
their products, services, and marketing efforts to the specific character of different demographic
groups that occupy different market areas Because the nature of the competition faced by
participants in the mortgage finance process varies across different areas, they must develop
different competitive strategies to suit the character of their competition in different areas. GIS
technology has the potential to support a wide range of business applications in mortgage
finance (Alberts and Douglas, 1992). At the most elemental level, it can provide mapping
capabilities to help decision makers visualize the spatial distribution of variables that affect
their business. At a higher level, it can be used to combine multiple variables such as the racial
and income composition of neighborhoods with the location of recent mortgage originations. It
can be used, for example, to select the optimal number, location, and size of branch offices to
service a market area given some decision rules about how far any share of potential borrowers
can be from a branch office (Birkin and Clarke 1998).
In Kenya, the use of GIS is rather limited but growing steadily with the mainstreaming of
GIS in many curricula in the Universities. Acquisition of georeferenced data is also an
expansive undertaking including the data management and dissemination. Another limitation is
*Corresponding author (David Kuria). Tel: +254-727-399208. E-mail addresses: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/129-143.pdf
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poor consumer awareness which means less demand for the products and services of GIS.
Regardless of these limitations, GIS has been used in Kenya for several projects with good
result, for instance, in compiling the National Water master plan (Republic of Kenya, 1992).
The Kenya Wildlife Services (Kariuki, 1992) used GIS for managing the large volumes of data
they acquire relating to wildlife census, vegetation and land use dynamics, infrastructure,
security and planning of operations. The department of resource surveys and remote sensing
makes use of GIS and Remote Sensing in the mapping of natural resources (Ottichilo, 1986).
An integrated GIS and remote sensing system has been developed for water resources
management in Kitui county (Kuria, et al, 2012), while Mulaku and Nyadimo (2010) used GIS
in the mapping of schools. Kuria, et al (2011) developed a GIS tool for enhancing efficiency in
distribution of national examinations. GIS has also been used to prepare the National
Environment Action Plan (Ministry of Environment and Natural Resources, 1994) and to
monitor a development programmed in Laikpia District (Hoesli, 1995).
In recent years, the banking industry in Kenya has been undergoing drastic changes
reflecting a number of underlying developments. Significant advancement in surveying,
architectural and information technology (IT) has accelerated and broadened the dissemination
of real property information and financial services and also increased the complexity. Another
key impetus for change has been the increasing competition among a broad range of domestic
and foreign institutions in providing bank loans, mortgages and other related services.
Regulations and computer technology advancement are forcing mortgage institutions to adopt
better operational strategies and upgrade their skills, throwing more challenges to banking
sector.
One of the most tedious tasks in banking is providing mortgage services for their clients.
Banks offer this service to enhance their accessibility to the customers. To cushion themselves,
banks need collateral such as tangible (fixed) property or business assets etc. In order for a bank
to sanction a mortgage, the property is analyzed. Some of these analyses include distance from
the main road or size of the plot, verification of the actual owner of the plot, current land
valuation etc. Currently in Kenya, banks accomplish these analyses manually by going to the
sites or contacting third party to do the tasks. This process at the least takes 30 days to complete
and involves a substantial expenditure in funds while following this due diligence. In case the
same plot is being used by a client to request for other loans to the same bank or different banks
132 David Kuria, Moses Gachari, Patroba Odera and Rogers Mvuria
the analyses have to be done for each application cascading the problem.
A GIS simplifies the process by easily accomplishing the following: (i) does the spatial analysis
easily on the computer from the office, (ii) calculates the area or distance from main road or
other features of interest without having to visit the site/plot, (iii) calculates the land value of the
area by analyzing the surrounding area, (iv) creating a centralized or distributed database
system that can be used to detect and prevent multiple loan applications utilizing the same plots,
and (v) perform auction procedures and instructions without much difficulty.
The objective of this study is to develop a pilot database for real property information
within parts of Kilimani, Lavington and Kileleshwa estates that can be used to secure bank
loans and mortgages. To accomplish this objective a geodatabase is developed with capabilities
of analyses, and modeling of (i) present land use (ii) present land value (iii) information of the
plot e.g. past owner, case, previous loan taking condition etc (iv) distance from the main road
and (v) adjacent road width
2 The study area
The area chosen for study (Figure 1) covers extensive parts of Kileleshwa, Kilimani and
Lavington estates. It lies between (9857500m N, 242000m E) and (9859000m N, 254000m E).
It covers an area approximately 3,000,000 m2. The area has approximately 1500 plots and over
1800 permanent buildings. Semi-permanent buildings were however not considered.
Lavington is a suburb green haven lying halfway between the busier areas of Hurlingham
and Westlands. Here the few modern apartment blocks are unobtrusive and the lanes linking busier roads are lined with large houses and bungalows set in an acre or less of well-tended land. Many have swimming pools. Some roads are gated with security staff screening all visitors. Lavington is popular with expatriate families. Kilimani is bounded to the north by Dennis Pritt Road and to the south by Ngong Road, bisected by Argwings Kodhek Road. T hese busy areas host many of Nairobi’s newly built apartment blocks. These are replacing the expansive 1950s and 1960s bungalows which once sat back from the wide streets bordered by high green hedges. Few of the new blocks are taller than five story and many enjoy balconies, communal swimming pools and 24-hour security staff. Kileleshwa is quieter and greener than Kilimani, Hurlingham or Ngong Road and more of the 1950s and 1960s bungalows, set in large mature gardens, have survived the property developers. However, there are some apartments
*Corresponding author (David Kuria). Tel: +254-727-399208. E-mail addresses: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/129-143.pdf
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blocks here too.
Figure 1: The study area.
2.1 Mortgage financing The term mortgage refers to the process by which an individual or a business can purchase
either a residential or a commercial property without having to pay the total value upfront. Mortgage is defined as a loan to an individual or a business for purchasing a real estate. In this case the real estate also acts as collateral for the loan. The mortgage contains two parts: (i) mortgage that is also the pledge and (ii) the promissory note which is the promise for repayment.
Virtually all mortgage financing arrangements require one to put in some equity, with the
financing institution funding a portion of the value of one’s new home. Most will require that one saves up for the required down payment with them, which is usually 15% to 30% of the purchase price. Paying more up-front works better in the long run, reducing the costs of the mortgage. One may also pay term deposits and various statutory fees for any transfers, charges and/or registrations to take place. As a rule of thumb, the combined legal and stamp duties work out to about 10% of the purchase price of the new home. Monthly repayments will also typically include the cost of insurance policies on the property offered as security for the loan and on mortgagor’s life.
134 David Kuria, Moses Gachari, Patroba Odera and Rogers Mvuria
3 Methodology
The study area was chosen on the basis that it has undergone drastic development changes
for the past few years due to changing zoning ordinances and building regulations. Emergence
of apartments clearly indicates that many people are buying residential houses in this area. The
prices of such apartments range from Ksh. 6,000,000 and some even over Ksh. 10,000,000. It is
thus obvious that mortgage market will continue to be of great concern in this region. The
banking sector thus needs to collect and maintain a spatial database of all spatially referenced
information about the buildings that are on mortgage and all land parcels that acting as
collaterals.
3.1 Data collection In order to have a clear view of the study area some aerial photographs were used. These
had the advantage of depicting the land uses as they appear and brought about clarity.
Figure 2: Digitized property map.
A topo-cadastral map (map showing land subdivisions and the topography) of the area was
scanned at a resolution of 600 dpi (dots per inch) and saved. The target areas of Kilimani,
Lavington and Kileleshwa estates acted as the sample size of the project. The features
considered are (i) transport network, (ii) cadastral subdivision, (iii) existing buildings, and (iv)
*Corresponding author (David Kuria). Tel: +254-727-399208. E-mail addresses: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/129-143.pdf
135
land use. Figure 2 shows the land parcels in the study area, which can serve as collateral for
mortgages.
Data capture was carried out in the following ways: (i) digitizing by scanning hardcopy
maps(ii) onscreen digitizing and (iii) attribute entries. The topographic maps, survey plans
and building plans were scanned using a flatbed scanner. The resulting images are a digital
copy of the original paper based map or plan. Onscreen digitizing was done with great care
since a deviation in parcel areas would mean a lot in the final analysis. This involved
digitization of line, point or area features. Once all the required features had been digitized
and stored in their corresponding layers, attribute entry was done. Buildings’ photographs and
where possible floor layout plans were incorporated into the database in order to provide as
much information as possible. In the following diagram, one can that the selected building is cut
through by the boundary on the right. From visualization alone several conclusions can be
drawn; number of floors, boundary case, building type, value, floor area, nearest road etc.
3.2 The conceptual system A customer-centric business model can help address these challenges. A mortgage
institution’s primary function is to deliver financial services and products to their customers. In
the modern world they need to be market driven and market responsive. The success of such
an institution depends on its approach to data management, customer relation management.
Such institutions manage a bulk of information about customers, customer profiles and much
more. By incorporating ‘geographical location information of real property’ into their
database, long range planning mixed with geographical modeling will yield tangible benefits to
the mortgage finance institutions community.
Figure 3 shows the mortgage decision making process as conceptualized in this work. A
GIS plays a central role tying the bank’s non-spatial data to spatial data. Utilizing the bank’s
non-spatial data, the financial health of a client’s account can be easily obtained. Using the
spatial data, property information can be retrieved and analyzed based on various attributes and
spatial relationships with other features. From these analyses, the mortgage or loan value can be
determined. Information about encumbrances and other restrictions on a property can also be
retrieved. Since all these bits of information have been stored in a single centralized or
distributed database, a quick decision (in a matter of minutes) can be arrived at on whether to
136 David Kuria, Moses Gachari, Patroba Odera and Rogers Mvuria
grant or deny the application. To aid in this decision making process, the logic captured in
Figure 3 has been coded into an extension that calls up the geodabase and based on the
parameters of interest (such as particular parcel, client, mortgage remaining, transaction
histories) a set of decision pathways are proposed and presented to the appraiser using the
system.
Figure 3: Conceptual model of the mortgage decision making process.
4 Results and Discussion
A pilot GIS database for real property information in Kileleshwa, Kilimani and Lavington
has been developed. The database contains information on plots, plot numbers, plot sizes, plot
values. Layers of related geographic features e.g. buildings, access roads and rivers are also
incorporated in this database. Figure 4 shows an example of information retrieved for one land
parcel and the building it contains. Managing such a bulk of geographical data including
*Corresponding author (David Kuria). Tel: +254-727-399208. E-mail addresses: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/129-143.pdf
137
customer’s bank records is hectic and cumbersome. Prior to the incorporation of the GIS,
decision making required some time and even a third party consultation. Now with the GIS in
place, some of the checks and decisions can be made quite easily and in a cost effective fashion.
Figure 4: Retrieved geodatabase results for parcel and contained building.
Figure 5: Accessibility levels.
In Figure 4, parcels with boundary disputes can be easily identified from the database,
encroaching parcels, existing encumbrances, e.g. court orders, caveat, pending cases and other
restrictions can also be effortlessly retrieved. It is evident that the distance from the main road
138 David Kuria, Moses Gachari, Patroba Odera and Rogers Mvuria
affects the value of any property; the further the property is from a main road, the lesser the
value.
Figure 5 shows the accessibility levels. These levels were determined through a 15m
buffering on either side of the road. Parcels in light green color had a high proximity to tarmac
road, other parcels in light blue had a lower proximity to the road. They however had minor
access roads linking to the tarmac roads. For land value computations purposes, the study area
was assumed to have a high accessibility level in the interest of simplicity. Eighteen properties
were sold out recently in the area; these were used to make buffers at distances of 5 km.
Figure 6: Property value distribution.
Buffers are rings drawn around features at specified distance from the features. These buffers
were meant to determine the value of all land parcels in the study area. It was concluded that the
land values were homogeneous throughout the study area. The statistics of these plot values in
shillings and areas in square meters were generated. From frequency distribution analysis of
these recently sold out properties, a mean value of approximately KShs. 14,790,000.00 per acre
was obtained. A similarly obtained mean area of the properties was found as 4990 m2. The
following formulation (eq. 1) was used to obtain the prevailing value for a property.
*Corresponding author (David Kuria). Tel: +254-727-399208. E-mail addresses: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/129-143.pdf
139
AcACC ××= 4047 (1)
Where Ac = property area (in acres) A = property area (m2) and C = value of property with
the over-bar referring to mean value, with the value 4047 being the conversion factor (from
acres to m2). It is worth mentioning that in the study area, errors in area computation can have
serious value implications since 1 m2 costs KShs. 3,000.00. Based on these computations, a
property value distribution map was prepared (Figure 6). It gives a clear view of prevailing
land values. It is therefore easy to predict the range of any plot value at a glance. The most
expensive parcels belong to Lavington Primary school and Kenton College – dark brown. The
value depends on the size of the property, the larger the size the more expensive.
Figure 7 provides insight to the bank on the distribution of property that the bank needs to
subject to auction – in red. The plots in purple show mortgaged property. Tracking such
property with the use of Global Position System (GPS) is easy and cost effective since their
positional information can be obtained from the map and imported into the GPS devices for
field crews.
Figure 7: Mortgages approved and defaulted.
140 David Kuria, Moses Gachari, Patroba Odera and Rogers Mvuria
An ArcGIS extension was developed that can be used to bring out a user interface to
non-GIS appraisers. This was programmed in Visual basic and linked to the GIS database. The
extension generated the interface shown in Figure 8. An account number input generated all
other information related to that parcel. The database end user (bank employee) is only
expected to input the account number for any customer and the program automatically retrieves
all other parcel related information. All this is achieved within a GIS environment with the
map of attributes in the background.
Figure 8: User interface for querying database.
Based on the amount of mortgage applied for and the credit history of a client, the
application is able to flag the credit worthiness of the applicant. A healthy client is flagged with
a green status, while one who either has encumbrances on the property or whose value of
collateral is insufficient to guarantee the mortgage is flagged with a red status. In the case of one
whose case needs further clarification an orange status is presented.
*Corresponding author (David Kuria). Tel: +254-727-399208. E-mail addresses: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/129-143.pdf
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5 Conclusion A GIS database for real property information within the study area has been developed.
Attributes related to real estate were input from existing maps, with some being generated by
the software such as areas and prices of land parcels. The geodatabase retrieves geospatial
information from the mortgage appraiser. Performance of analysis, statistics and updating
yielded the desired results. Storage and retrieval of spatial data was convenient, without a large
storage capacity demands. Using the proposed system, a bank can effortlessly and efficiently
manage the administration its financial products touching on spatial elements. This system has
demonstrated capability of identification and tracking of defaulters, multiple loan or mortgage
applications tied to the same property. It is able to process the credit worthiness of any client
applying for a mortgage.
While this study has demonstrated the potential of GIS in the banking industry, banks still
need to decide on the utility of GIS with respect to simplifying their loan and mortgage
processing. The rapid emergence of apartments in high residential areas shows that mortgage
market will continue to thrive and hence handle more geographic data. Such data needs a
central storage with fast digital map retrieval capability; which is accompanied by any other
related non-spatial data. This work considered data from one bank which was not linked to any
other bank’s data and it is imperative that a centralized system for the banking sector can help
deal with rogue defaulters from other banks. Sharing of these or any spatial data among bank
branches can improve efficiency and thus reduce costs.
6 Acknowledgement
The authors would like to acknowledge the Kenya Commercial Bank for providing
valuable information on the loan procurement procedures and data used in this work.
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142 David Kuria, Moses Gachari, Patroba Odera and Rogers Mvuria
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Maguire, D. J., (1991). “An overview and definition of GIS”, In Geographical Information Systems Principles and Applications, edited by D. J. Maguire, M. F. Goodchild, and D. W. Rhind. (New York: Longman Scientific and Technical; John Wiley and Sons, Inc.), pp. 9-20.
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Ministry of Environment and Natural Resources (1994). The Kenya National Environment Action Plan. Summary. Nairobi, Kenya.
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Kariuki, A. (1992). “Applications of geographic information systems in the management of the wildlife resource.” In Applications of geographical information systems for efficient data storage and handling in Kenya. Okoth, P.F. (ed.). Proceedings of a symposium. Kenya Soil Survey, Nairobi. Pp.10-19.
Kuria, D. N., Gachari, M. K., Macharia, M. W. and Mungai, E., (2012). “Mapping groundwater potential in Kitui District using geospatial technologies”. International Journal of Water Resources and Environmental Engineering, 4(1), pp. 15 – 22.
Kuria, D. N., Ngigi, M. M., Wanjiku, J. W. and Kasumuni, R. K., (2011). “Managing distribution of national examinations using geospatial technologies: A case study of Pumwani and Central divisions” International Journal of Computer Engineering Research. 2(5), pp. 82 – 92.
*Corresponding author (David Kuria). Tel: +254-727-399208. E-mail addresses: [email protected]. 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/129-143.pdf
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Dr. David Kuria is a Senior Lecturer in the department of Geomatic Engineering and Geospatial Information Science of the Dedan Kimathi University of Technology. He holds a B. Sc (Surveying) with Honors from the University of Nairobi (Kenya), an M. Sc (Photogrammetry and Geoinformatics) from the Stuttgart University of Applied Sciences (Germany) and a PhD from the University of Tokyo (Japan). Dr. Kuria’s current interests are in web mapping, climate research and geospatial application development.
Dr. Moses Gachari is an Associate Professor in the Department of Geomatic Engineering and Geospatial Information Science of the Dedan Kimathi University of Technology. He holds a B.Sc (Surveying and Photogrametry) with Honors from the University of Nairobi (Kenya), an M.Sc., and a PhD degrees from the University of Oxford (UK). Prof. Gachari has research interests in geospatial applications in development and environment, geodesy and surveying in general.
Dr. Patroba Odera is a lecturer in the Department of Geomatic Engineering and Geospatial Information System of Jomo Kenyatta University. He holds a B. Sc. in Surveying with Honors and an M. Sc. in Surveying from the University of Nairobi (Kenya) and a PhD from the Kyoto University (Japan). Dr. Odera’s research interests are in gravity determination, geodesy and geospatial technologies.
Rogers Mvuria is a Geomatics Engineer and GIS analyst with the Lutheran World Federation. He holds a B.Sc. in Geomatic Engineering and Geospatial Information Systems with Honors from the Jomo Kenyatta University of Agriculture and Technology. Mr. Mvuria’s research interests are in development of geospatial applications and surveying.
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*Corresponding author (B. Wanno). Tel/Fax: +66-43-754246. E-mail address: [email protected] 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/145-156.pdf
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International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies
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Theoretical Investigation of Hetero–Diels–Alder Functionalizations on SWCNT and Their Reaction Properties
Danai Pankhao a, Nongnit Morakot a, Somchai Keawwangchai a, and Banchob Wanno a,b*
a Supramolecular Chemistry Research Unit, Department of Chemistry, Faculty of Science,
Mahasarakham University, THAILAND b The Center of Excellence for Innovation in Chemistry (PERCH-CIC), THAILAND A R T I C L E I N F O
A B S T RA C T
Article history: Received December 2012 Received in revised form 28 January 2013 Accepted 04 February 2013 Available online 07 February 2013 Keywords: DFT; Hetero–Diels–Alder reaction; Nitrosoalkene; ONIOM; SWCNT; Thionitrosoalkene.
Two–layered ONIOM method at the ONIOM(B3LYP/6–31G(d,p):AM1) theoretical level was applied to investigate the hetero Diels-Alder reaction functionalization of various nitrosoalkenes (NAs) and thionitrosoalkenes (TNAs) onto side–wall (5,5) armchair SWCNT. The results indicated that SWCNT can be functionalized with NAs and TNAs. The energy barriers of TNAs funtionalized SWCNT were lower than those of NAs. This implied that TNAs are easier to react with SWCNT than those of NAs. In addition, electronic properties and density of states of SWCNT were modified by the Diels-Alder functionalizations of NAs and TNAs.
2013 INT TRANS J ENG MANAG SCI TECH.
1 Introduction During the last decade, many researches have been focused on functionalizations of
single–walled carbon nanotube (SWCNT) to make fascinating new physical and chemical
properties for practical applications (Meyyappan, 2005). Generally, chemical
functionalizations to SWCNT were achieved by covalent functionlizations onto the sidewall of
tube at the sp2 carbon system. The experimental functionlizations using fluorine (Chamssedine
2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.
146 D. Pankhao, N. Morakot, S. Keawwangchai, and B. Wanno
et al., 2011), diazonium salt (Bahr et al., 2001), and fuming nitric acid (Kitamura et al., 2011),
and theoretical functionalizations using azomethine ylides (Cho et al., 2008), nitrene (Zhang et
al., 2006), ozone (Yim & Johnson, 2009), ethene (Lawson & Walker, 2012), alanine and
alanine radical (Rajarajeswari et al., 2012), and diazomethyl aromatic compound (Raksaparm
et al., 2012), pyrazinamide (Saikia & Deka, 2010) on SWCNTs were successful studied and
reported. Chemical cycloaddition on buckminsterfullerene (C60) was reviewed by Yurovskaya
and Trushhov (Yurovskaya, & Trushkov, 2002).
Diels–Alder reaction is well known to occur between a conjugated diene and a dieneophile
and is particularly useful chemical modification to construct cyclic compounds. This reaction
has also been explored on the functionalizations of the C60 (Ohno et al., 1993; Ohno et al.,
1995; Yang et al., 2006; Nakahodo et al., 2008; Yang et al., 2009). Interestingly, the
Diels–Alder cycloadditions on the sidewall SWNT were successful studied by experimental
(Delgado et al., 2004; Ménard-Moyon et al., 2006) and theoretical (Lu et al., 2002) methods.
Nitrosoalkenes (Tahdi et al., 2002; Gallos et al., 2003) and thionitrosoalkenes (Bryce et al.,
1994; Reed & Zhang, 2001) are a class of hetero dienes. In principle, the SWCNTs should be
traceable to these reactions with hetero compounds such as nitrosoalkenes (NAs) and
thionitrosoalkenes (TNAs). However, experimental and theoretical studies of the side–wall
addition of nitrosoalkene and thionitrosoalkene to SWCNT have not yet appeared to the best of
our knowledge. In the present work, the hetero-Diels–Alder reactions of nitrosoalkene and
thionitrosoalkene compounds on armchair (5,5) SWCNT have been investigated by using the
quantum calculation.
2 Computational Details
Two-layered ONIOM method at the ONIOM(B3LYP/6–31G(d,p):AM1) theoretical level
was applied to geometry optimizations of all species of cycloaddition functionalization onto
side-wall (5,5) armchair SWCNT. The model of SWCNT (C70H20 model) was chosen with
open ends and the hydrogen atoms were used to saturate the carbon atoms at the two terminated
ends of the tube (Figure 1). The ball atoms of a pyrene (C16) model cluster and those belonging
to nitrosoalkene and thionitrosoalkene molecules were treated at the higher B3LYP/6–31G(d,p)
level, and the remaining SWCNT atoms were treated with the AM1 method. Based on the
two-layered ONIOM approach, a pyrene molecule shown as ball atoms of SWCNT was
selected to be the high level layer. The hetero-Diels–Alder functionalizations were assigned to
*Corresponding author (B. Wanno). Tel/Fax: +66-43-754246. E-mail address: [email protected] 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/145-156.pdf
147
occur at the C1–C2 bond of SWCNT as shown in Figure 1. All of the structures of reactants,
transition states and products were located by the ONIOM(B3LYP/6–31G(d,p):AM1) model
achieved without any symmetry constraints. All transition states were characterized by single
imaginary frequency.
The vibration frequency computations were performed at 298.15 K and the standard
pressure as applied in our previous works (Wanno & Ruangpornvisuti, 2006). The highest
occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO)
energy gaps and density of states (DOSs) were also been determined. All calculations were
performed with the GAUSSIAN 03 program (Frisch et al., 2008). The molecular graphics of all
related species were generated with the MOLEKEL 4.3 program (Flükiger et al., 2000).
Figure 1: The optimized structures of single walled carbon nanotube (SWCNT),
nitrosoalkene (NA) and thionitrosoalkene (TNA) reactants.
3 Results and discussion
The structural optimizations of (5,5) armchair SWCNT, nitrosoalkene and
thionitrosoalkene, their product and transition state structures were carried out at the
ONIOM(B3LYP/6–31G(d,p):AM1) level of theory. The optimized structures of SWCNT,
nitrosoalkenes, and thionitrosoalkenes are displayed in Figure 1. The selected bond distances
and bond angles of the optimized structures are considered and discussed. The average C1–C2
bond distance of SWCNT was 1.380 Å which is in good agreement with the previous reports
(Raksaparm et al., 2012). For the nitrosoalkene reactants, the C3–C4 bond distances were
148 D. Pankhao, N. Morakot, S. Keawwangchai, and B. Wanno
1.335, 1.343, and 1.342 Å, respectively whereas the N–O bond distances are 1.220, 1.215, and
1.213 Å. The C3–C4–N bond angles were 123.7, 119.0, and 119.3°, respectively whereas the
C4–N–O bond angles were 114.5, 115.5, and 115.3° for NA, PhNA, and NO2PhNA,
respectively. The C3–C4 bond distances were 1.346, 1.353, and 1.352 Å, respectively whereas
the N–S bond distances were 1.602, 1.596, and 1.594 Å. Moreover, the C3–C4–N bond angles
were 128.2, 122.8, and 123.2°, respectively whereas the C4–N–S bond angle were 121.8, 123.8,
and 123.6° for TNA, PhTNA, and NO2PhTNA, respectively.
Figure 2: The ONIOM(B3LYP/6–31G(d,p):AM1)–optimized transition state structures for
nitrosoalkenes (above) and thionitrosoalkenes (bottom). Imaginary frequencies (in cm–1) are also presented.
Considering the transition state as show in Figure 2, the reaction started from nitrosoalkene
or thionitrosoalkene molecules reacted to C=C bond of SWCNT via the transition state to form
the functionalized SWCNT products. For the transition state structures of nitrosoalkenes, the
*Corresponding author (B. Wanno). Tel/Fax: +66-43-754246. E-mail address: [email protected] 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/145-156.pdf
149
Figure 3: The ONIOM(B3LYP/6–31G(d,p):AM1) – optimized product structures for
nitrosoalkene (above) and thionitrosoalkene (bottom) functionalizations.
C1–O and C2–C3 bonds were formed at the sidewall of SWCNT, the C3–C4 and N–O bond
distances were then elongated when comparing with its corresponding reactant structures. The
150 D. Pankhao, N. Morakot, S. Keawwangchai, and B. Wanno
C1–O bond distances were 2.268, 1.939, and 1.969 Å for NA, PhNA, and NO2PhNA,
respectively, whereas the C2–C3 bond distances were 2.059, 2.176 and 2.168 Å for NA, PhNA,
and NO2PhNA, respectively. For the transition state structures of thionitrosoalkenes, when the
C1–S and C2–C3 bonds were formed at the sidewall of SWCNT which the C3–C4 and N–S
bond distances were also elongated. The C1–S bond distances were found to be 2.649, 2.498,
and 2.675 Å for TNA, PhTNA, and NO2PhTNA respectively, whereas the C2–C3 bond
distances were 2.239, 2.088, and 2.221 Å for TNA, PhTNA, and NO2PhTNA, respectively.
Geometrical structures of products are displayed in Figure 3 in which the products were
represented the formation of the newly six-member ring of functionalized SWCNTs. The C1–O
bond distances were 1.485, 1.486, and 1.493 Å for NA, PhNA, and NO2PhNA, respectively,
while the C2–C3 bond distances were 1.583, 1.583, and 1.583 Å for NA, PhNA, and
NO2PhNA, respectively, and C1–S bond distances were 1.928, 1.927, and 1.930 Å for TNA,
PhTNA, and NO2PhTNA, respectively, while the C2–C3 bond distances were 1.583, 1.582, and
1.581 Å for TNA, PhTNA, and NO2PhTNA, respectively. After the functionalization
completed each of C1 and C2 atoms formed 4 chemical bonds with neighboring atoms. This
indicated that hybridizations of C1 and C2 atoms were completely changed from sp2 to sp3.
3.1 Reaction Energies and Energy Profiles Energy profiles based on the ONIOM(B3LYP/6–31G(d,p):AM1) computation for the
hetero-Diels-Alder functionalizations of nitrosoalkenes and thionitrosoalkenes onto SWCNT
are displayed in Figure 4 and the reaction energies, reaction energy barriers, and imaginary
frequencies of the functionalizations are listed in Table 1. The reaction profiles with initial
reactants (R), transition state and reaction products (P) are also represented in Figure 4. The
relative energy profiles showed that the energy barriers for nitrosoalkene functionalizations
were 21.88, 24.76, and 27.64 kcal/mol for the NA, PhNA, and NO2PhNA, respectively. It
should be noted here that the NA addition showed the lowest in the activation barrier. In
addition, the energy barriers of the thionitrosoalkene functionalizations were 15.01, 13.16, and
12.32 kcal/mol for the TNA, PhTNA, and NO2PhTNA, respectively, in which the NO2PhTNA
addition showed the lowest in the activation barrier. Clearly, all of functionalizations were
occurred via exothermic process. In both system, these energy barriers are strongly dependent
on the nature of the heteroatoms and the molecular geometries presented on the reaction.
*Corresponding author (B. Wanno). Tel/Fax: +66-43-754246. E-mail address: [email protected] 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/145-156.pdf
151
Figure 4: The reaction profiles and relative energy profiles (in kcal/mol) of (a) nitrosoalkene and (b) thionitrosoalkene functionalizations. Where R is reactants, TS is transition state and
P is reaction products.
Table 1: Reaction energies (ΔE), reaction barriers (ΔE ≠) and the imaginary frequencies (vi) for the transition state of hetero Diels-Alder functionalizations computed at the
ONIOM(B3LYP/6–31G(d,p):AM1) level of theory. Reactions ΔE a ΔE ≠,a vi
b
Nitrosoalkene addition SWCNT+NA NA/SWCNT -0.11 21.88 561.4iSWCNT+PhNA PhNA/SWCNT -2.05 24.76 519.8iSWCNT+NO2PhNA NO2PhNA/SWCNT -2.60 27.64 536.8iThionitrosoalkene addition SWCNT+TNA TNA/SWCNT -8.55 15.01 498.5iSWCNT+PhTNA PhTNA/SWCNT -12.34 13.59 450.5iSWCNT+NO2PhTNA NO2PhTNA/SWCNT -12.95 12.32 507.6ia In kcal/mol. b Imaginary frequencies (cm–1).
Table 2: The ELUMO and EHOMO energies and Egap of tube and its adduct complexes computed
at the ONIOM(B3LYP/6–31G(d,p):AM1) level of theory Species ELUMO
a EHOMOa Egap
a, b
SWCNT –2.28 –4.45 2.17 [2.20] c NA/SWCNT –2.31 –4.47 2.16 PhNA/SWCNT –2.29 –4.45 2.16 NO2PhNA/SWCNT –2.61 –4.60 1.99 TNA/SWCNT –2.35 –4.48 2.13 PhTNA/SWCNT –2.33 –4.45 2.12 NO2PhTNA/SWCNT –2.58 –4.61 2.03
a In eV. b Egap = ELUMO – EHOMO. c Computed at B3LYP/6–31G* level (reported by Zhou et al. 2004)
152 D. Pankhao, N. Morakot, S. Keawwangchai, and B. Wanno
Figure 5: The density of states of the SWCNT,
compared with (a) nitrosoalkene and (b) thionitrosoalkene complexes.
3.2 Electronic properties and density of state The ELUMO and EHOMO energies and frontier molecular orbital energy gaps (Egap) of
SWCNT and its adduct complexes computed at the ONIOM(B3LYP/6–31G(d,p):AM1) level
are displayed in Table 2. The results showed that, Egap for the pure SWCNT was 2.17 eV which
is in good agreement with the previous results (2.20 eV) reported by Zhou et al. (2004). For
NA, TNA, PhNA, and PhTNA complexes with SWCNT, the Egap were slightly different from
SWCNT. On the other hand, for the NO2PhNA and NO2PhTNA complexes with SWCNT, their
Egap values were 1.99 and 2.03 eV, respectively which was different from the other products.
*Corresponding author (B. Wanno). Tel/Fax: +66-43-754246. E-mail address: [email protected] 2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/145-156.pdf
153
Plots of the density of states of the NA, PhNA, and NO2PhNA and TNA, PhTNA, and
NO2PhTNA functionalized SWCNTs, compared with the armchair (5,5) SWCNT are displayed
in Figure 5. It was shown that electronic structure of the SWCNT was sensitive to the hetero
Diels–Alder functionalizations. The band gaps of SWCNT near Fermi level become narrower,
which suggested that the conductivity of SWCNT was modified by nitrosoalkene and
thionitrosoalkene functionalizations.
4 Conclusion
The hetero-Diels-Alder functionalizations of various nitrosoalkenes (NAs) and
thionitrosoalkenes (TNAs) onto side-wall (5,5) armchair SWCNT were investigated by using
the two-layered ONIOM method at the ONIOM(B3LYP/6-31G(d.p):AM) theoretical level.
The results indicated that SWCNT can be functionalized with NAs and TNAs. The energy
barriers of TNAs funtionalized SWCNT were lower than those of NAs. This implied that TNAs
are easier to react with SWCNT than those of NAs. In addition, hetero-Diels-Alder
functionalizations affected to electronic properties and density of states of SWCNT.
5 Acknowledgements
The authors appreciate the Research Affairs, Tungmanee School, Ubonratchathani, for
partial support of this research and the facility provided by Supramolecular Chemistry Research
Unit, Department of Chemistry, Faculty of Science, Mahasarakham University. The Institute
for the Promotion of Teaching Science and Technology, THAILAND, for financial support is
also gratefully acknowledged. The authors are also grateful to Dr. Wandee Rakrai and Dr.
Chanukorn Tabtimsai for their helps.
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Danai Pankhao is an M.Sc. student at the Department of Chemistry, Faculty of Science, Mahasarakham University, THAILAND. He received a B.Sc. in Chemistry from Ubon Ratchathani Rajabhat University, THAILAND.
Dr.Nongnit Morakot is an Associate Professor of Department of Chemistry at Mahasarakham University. She received a B.Sc. and M.Sc. from Chiang Mai University, THAILAND. She holds a Ph.D. from Chulalongkorn University, THAILAND. Associate Professor Dr. Morakot is interested in Supramolecular Chemistry.
Dr.Somchai Keawwangchai is working in the Department of Chemistry at Mahasarakham University. He received a B.Sc. in Chemistry from Mahasarakham University, THAILAND, He holds his Ph.D. from Chulalongkorn University, THAILAND. Dr. Keawwangchai’s research fields are supramolecular investigations and host–guest investigations, reaction mechanism investigations under non-catalytic and catalytic reactions of olefins on zeolite, organometallic catalysts.
Dr.Banchob Wanno is working in the Department of Chemistry at Mahasarakham University. He received a B.Sc. in Chemistry from Mahasarakham University, THAILAND, and M.Sc. in Physical Chemistry from Mahidol University, THAILAND. He holds his Ph.D. from Chulalongkorn University, THAILAND. Dr. Wanno’s research fields are nanomaterials and nanosensors, reaction mechanism investigations and host–guest complex investigations.
Peer Review: This article has been internationally peer-reviewed and accepted for publication according to the guidelines given at the journal’s website.
*Corresponding author (A. Siswanto). Tel. +60173167312 Email address: [email protected] 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/157-170.pdf
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International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies
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The Phenomenology of Lamban Tuha: The Local Wisdom of South Sumatra Traditional Architecture Ari Siswantoa,b*, Azizah Salim Binti Syed Salimb, Nur Dalilah Dahlanb,
Ahmad Harizac a Faculty of Engineering, University of Sriwijaya, INDONESIA b Faculty of Design and Architecture, Universiti Putra Malaysia, MALAYSIA c Faculty of Human Ecology, Universiti Putra Malaysia, MALAYSIA A R T I C L E I N F O
A B S T RA C T
Article history: Received 20 July 2012 Received in revised form 23 January 2013 Accepted 08 February 2013 Available online 14 February 2013 Keywords: Phenomenology; Local wisdom; Traditional architecture; Lamban tuha, Earthquake resistant structures.
Local wisdom of traditional architecture is towards extinction along with the existence an increasingly neglected traditional house, including the one who understands it reduced drastically. Lamban Tuha in South Sumatra has demonstrated the ability to adapt to its environment and able to withstand natural catastrophes. The study used phenomenological method to reveal information from the first person who is considered experts on the local wisdom of Lamban Tuha. This study shows the construction of kalindang provide an excellence effect of providing high flexibility in case of earthquakes. The separation structure between lower, middle and upper parts is done to give building more flexible. Local wisdom is reflections of valuable experience which can be utilized as the concept of a sustainable housing development in the context of anticipate natural disasters. The existence of Lamban Tuha is an interesting experience that can be used as thoughts on designing earthquake resistant buildings.
2013 INT TRANS J ENG MANAG SCI TECH.
1. Introduction South Sumatra has a rich history of diverse culture that is very stunning in architectural
treasures. Culture is an expression of society in adapting to an environment adapted to the
2013 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.
158 Ari Siswanto, Azizah Salim Binti Syed Salim, Nur Dalilah Dahlan and Ahmad Hariza
necessities of life. One of the cultural heritages in architecture is traditional ulu house type
called lamban tuha. Lamban tuha which means ancient house reflects the traditional house
which resist to earthquake. Typical houses in Surabaya village are ulu house and gudang
house. Ulu house is the common term for a traditional house outside the city of Palembang
while gudang house types can be found in all areas in South Sumatra (Barendregt, 2003;
Siregar and Abu, 1985). Ulu house recognized by the local community and are classified as
lamban tuha, currently amounted two houses. The existence of lamban tuha 1 very impressive
considering the house has been occupied for 11 generations. According to the heirs, lamban
tuha 1 was founded first in the hamlet of Canti (now a forest), then it moved to Surabaya Talang
village and finally lamban tuha moved to Surabaya village in Banding Agung sub district
which closes to Lake Ranau.
One exceptional of lamban tuhas are the elastic ability of those traditional buildings
against earthquake that happen in Liwa, Lampung province in 1933. Both lamban tuhas are
the only building that remained standing despite the devastating earthquake in 1933, while the
other buildings in the village of Surabaya collapsed and mostly flattened to the ground. Typical
system of traditional construction similar to lamban tuha is only about four houses including
the new ones.
Lack of attention from the public and local government and the financial inability of
lamban tuha’s owners will caused the loss of assets in term of local wisdom (Oliver, 2006).
Traditional houses in South Sumatra have demonstrated exceptional indigenous knowledge of
our ancestors in shaping the quality of their lives. This indigenous knowledge will regain its
meaning and value in the society, we should aware of the glory of the inherited tradition. The
bearers of indigenous knowledge might be developed in recent and future for sustainable
housing development.
2. Methodology Phenomenological approach is an attempt to reveal a phenomenology of the experience
from a person in everyday life in the context of the time, place and consciousness (Creswell,
1998). Context of time has to do with history, important events, technology and character.
Context of place has to do with users, objects, physical space, the atmosphere and the
*Corresponding author (A. Siswanto). Tel. +60173167312 Email address: [email protected] 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/157-170.pdf
159
environment of human life. While the context of feeling have to do with experience, awareness
and knowledge visible and invisible. Based on the objectives, this research used
phenomenological method. In addition to in-depth interviews were carried out against the
respondents, this research will also see the relevance of the information provided with the
environmental conditions around it, the existence of traditional houses and history of the
houses. The first information obtained by previous research, community leaders, the owner or
tenant on the basis of their advices, and then traced the people who have a relationship with
traditional houses such as local builders, carpenters, local leaders and experts (Satori and
Komariah, 2009). Data collection will be primary data which consists of in-depth interview,
physical traditional houses and secondary data which consists of literature, journal and
research.
3. Analysis Three analyses are used in this research: a description, a comparison, and an evaluation.
The description is about architecture style, system structure and detail of structures which are
related to the environment, philosophy and their indigenous techniques. Interpretation of local
wisdom of traditional architecture would be conducted as a part of description with sources of
the owners / users, local community leaders, experts and local carpenters. The comparison is
between the people experiences in applying local wisdom.
4. Discussion
4.1 Physical Characteristic of Lamban Tuha Traditional knowledge, indigenous knowledge, and local knowledge refer to the
long-standing traditions and practices of certain regional, indigenous, or local communities.
Therefore, traditional knowledge also encompasses the wisdom, knowledge, and teachings of
these communities. Traditional knowledge has been orally accepted for generations from
person to person. The wisdom in creating natural system of thermal comfort is often found in
traditional architecture (Hardiman, 2000). Slightly different, lamban tuha has shown evidence
of local wisdom in the traditional house in anticipation of natural disasters such as earthquake.
Meanwhile, result from collective local wisdom of the contextual been able to adjust over time
and was attuned with nature and local lifestyle (Limthongsakul et al, 2005).
160 Ari Siswanto, Azizah Salim Binti Syed Salim, Nur Dalilah Dahlan and Ahmad Hariza
Vernacular that related to the process of designed and built it usually close relation
between the form and the culture. Vernacular architecture has limitation in delivering a variety
of expression however, at the same time in accordance with the characteristics of different
situations can create their respective places (Rapoport, 1969). Similar to the traditional
buildings in most parts of Indonesia, the South Sumatra traditional house shows characteristics
of timber buildings on stilts in different system structure based on the geography while others is
a kind of raft house. Due to different environment and culture, indigenous knowledge creates
traditional architecture which is adaptive with their environments. South Sumatra traditional
houses could be dismantled and rebuild in another location with mostly reusing of origin
housing materials. The typical construction of traditional house is with flexible nail-less joints,
and non-load bearing walls.
Lamban tuha has saddle-shaped roof that rise high and put the tiber angin (gable end) on
front and rear parts of the roof (Figure 1). The high rise roof has rake cross at the top as other ulu
house type. Distinctive roof form, relatively high and in accordance with the dimensions of the
house can create the beauty that is easily recognizable from a distance (Zumthor, 1998). The
construction of roof related to large span, wind and rain in specific areas. In different
geography, dwellings including roof, reflect the local knowledge, local technology and
environment (Ohno and Xihui, 2008). It explained very detail about roof structure, roof layer
construction and support systems for pitched roof. Conventional construction systems of
pitched roof in many countries always related to environmental conditions, cultural aspects and
local knowledge, it is typically seen in traditional houses such as lamban tuha. In general, the
roof truss structure of lamban tuha is very simple. Minimized the weight of steep roof of
traditional houses is an important issue for smart construction (Gruber and Herbig, 2007).
Expenses due to own weight, the wind and earthquake can reduce the risk of severe damage to
the roof.
Lower construction part of lamban tuha is a series of pillars that have stone footings
combined with a pile of round logs in rectangular shaped without finishing. Stacks of logs with
a square form support the building load known as the kalindang. Kalindang which has 7 – 11
layers of logs uses the notch on each layer as connection (Figure 5). However, not all parts of
the house supported by kalindang. Lamban tuha has a stair for entrance on the front side to
toward the garang/porch and the other in the rear for services. Porch is a transition space before
*Corresponding author (A. Siswanto). Tel. +60173167312 Email address: [email protected] 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/157-170.pdf
161
entering the house and serves for guests or a place to sit on an informal basis. The floor surface
in different rooms in lamban tuha has no height difference.
Figure 1: Lamban tuha 1 (left) and lamban tuha 2 (right).
The composition of rooms in lamban tuha is very simple and tends to be symmetrical.
Arrangement of rooms on lamban tuha is as follows:
a. Garang (porch), a transition space.
b. Lapang unggak (living room)
c. Lapang doh (dining room)
d. Lapang tengah (bedroom)
e. Kebik (front porch)
f. Parogan (side porch), storage for goods or old coconuts.
g. Dapo (kitchen)
h. Pagu hantu (attic), a place to store the heirlooms and spears sacred objects.
162 Ari Siswanto, Azizah Salim Binti Syed Salim, Nur Dalilah Dahlan and Ahmad Hariza
Figure 2: Floor plan of lamban tuha 1(left) and changing orientation of lamban tuha 2 (right).
Lamban tuha 1 is still the original shape as before; there has been no change in orientation
and buildings addition (Figure 2). In contrast to the lamban tuha 1, lamban tuha 2 has changed
the orientation of the building and built new stair due to consider the access road (Figure 2). At
first, lamban tuha 2 facing Qibla (west), then converted facing east because the road
consideration. As a result, the main entrance at the western is cut and moved to the east by
making entrance door facing the north.
4.2 Local Wisdom of Lamban Tuha Acting as Earthquake Resistant Based on the identification of floor plan, indigenous building materials and timber
construction system, lamban tuha has local wisdom that can be proved by experience that is
quite convincing during occupied by 11 generations (lamban tuha 1) and 6 generations (lamban
tuha 2) until today. During that time, lamban tuha 1 has moved for three times and hit by a
severe earthquake in 1933 which had destroyed all the buildings except lamban tuhas in the
village of Surabaya. The site selection to establish lamban tuha based on the land that has good
carrying capacity, far from the possibility of landslides or flooding. While the orientation of
*Corresponding author (A. Siswanto). Tel. +60173167312 Email address: [email protected] 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/157-170.pdf
163
lamban tuhas face the Qibla, or in this case is the west. Until know, lamban tuhas never hit by
floods and landslides struck.
According to the informants, people who sleep in lamban tuha should put the head and feet
directed west to the east, may not sleep in addition to that direction. They believe there are
certain rules of superstitious that apply to lamban tuhas (Fishwick and Vinning, 1982). Attic is
an important part of the house is believed to be a sacred place, so this place is respected and
used to store the heirlooms of their ancestral heritage. Believe in a supernatural or who has the
power associated with the presence of the house is something that is common in the past.
Lamban tuha has a simple floor plan without a rigid division of rooms and tends to
symmetrical. This indicates if the relationship between family members is very close, open and
has the nature of togetherness. Social life and communication between family members are
very close and communal. The simplicity of the floor plan and symmetrical shape is very
precise from that anticipates the influence of the earthquake. Symmetrical shapes can create a
balance of construction in every corner of the house when rocked by an earthquake.
The effect of earthquake was the collapse buildings because of bad reinforcement
structures, unreinforced masonry walls and brick walls. On the other hand, timber houses
performed relatively well compare to brick house during the earthquakes (Maidiawati. and
Sanada, 2008). Traditional houses still stand usually because of using timber structure, lighter
building material, and applying flexibility of structure. Furthermore, materials and structures
that are used in traditional houses have been made to reduce the effects that occur in the event of
earthquake (Audefroy, 2011). Lamban tuha, a typical traditional house in South Sumatra has
identifiable timber structure which resist to earthquake. Most of the sufferers of the
earthquake are the victims of collapsed concrete structures (Gruber, 2007). Building of
traditional architecture has a symmetrical shape and express in the form of floor plan and
facade. The concept of the design through the axis of symmetry generally implies a balance of
organization and function space with macro cosmos. Emphasizes a balance by referring to the
axis is the most elementary concepts of earthquake resistant buildings. Floor plan of lamban
tuha is a simple open plan design while the physical form of the house tends to be symmetrical
164 Ari Siswanto, Azizah Salim Binti Syed Salim, Nur Dalilah Dahlan and Ahmad Hariza
and proportional. The horizontal load displacement characteristics of a traditional timber house
can be simulated fairly well by adapting a mud wall and hanging wall models. This model is
embodiment of Japanese culture that has so concern about their familiar natural disaster such as
earthquake and typhoon (Fujita et al, 2004.). Most traditional houses in South Sumatra are
timber houses, only a small part of the house with bamboo or a combination of both.
Figure 3: Fundamental timber construction of press, pivot, pinch and pull.
When lamban tuha 1 was built, indigenous building materials were collected in advance by
soaking in Lake Ranau. After the perceived amount sufficient, then the house was built with no
nails, just using timber connection that is fundamental of press, pivot, pinch, and pull (Figure
3). The construction of lamban tuha could be dismantled because it has nail-less timber
construction (Figure 4). This typical construction can provide excellent flexibility in case of
oscillation due to earthquakes.
Figure 4: Nail-less timber construction of lamban tuha.
*Corresponding author (A. Siswanto). Tel. +60173167312 Email address: [email protected] 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/157-170.pdf
165
In addition to separating the construction of the house with lower construction, the
separation is also reinforced by the provision of ijuk (palm fibres) on the stone footing between
stilts with beams, kalindang with beams, kalindang with stone footing and stack wood blocks
on kalindang (Figure 7). Interestingly, the fibres can be seen by its presence at the bottom
construction of lamban tuha 1 (Figure 7). The informant strongly believes that the palm fibres
can serve as a sort of bearing on the structure in anticipation of earthquake. Lamban tuha 2 does
not use palm fibres in separation between structures. This difference indicate if an
understanding of fibres function of bearing structures have not understood more as a local
wisdom.
Figure 5: Kalindang of lamban tuha 1 (9 layers) and kalindang lamban tuha 2 (11 layers).
Lamban tuha has three important parts of construction that is the bottom, the middle and
the upper (Figure 6). Construction of the bottom part is the poles and kalindangs, construction
of the middle part is the framework of the house while construction of the upper part is the roof
truss. Further information mentioned that the owner of lamban tuha 1 had planned a strong and
sturdy timber construction system but also can be flexible during an earthquake. The area
around Lake Ranau is prone to earthquake disaster. Therefore, the construction lamban tuha 1’s
body is separated by lower structure, the construction of the house just rested on the structure;
this gives the effect of high flexibility. In the context of house construction, there are interesting
things, piles on the outer wall are not in a straight line with stilts or in other words, the outer
wall is cantilever, about 30 cm from the composition of stilts.
166 Ari Siswanto, Azizah Salim Binti Syed Salim, Nur Dalilah Dahlan and Ahmad Hariza
Earthquakes give bigger impact to reinforced concrete building than traditional building
(Dogangun et al, 2006). Relatively, most type of traditional building performed well during
earthquakes. Some tribes in Sumatera have local wisdom about timber construction which
resist to earthquake. Some type of traditional timber house construction which resist to
earthquake is not found in other areas such as traditional nias houses in North Sumatra, gadang
house in West Sumatra and lamban tuha in South Sumatra. Typical wood construction shows in
understanding the specific geographical conditions to adapt and survive. Lamban tuha 1 has
kalindang at four points while the lamban tuha 2 only has kalindang at two points. The number
of kalindang adapted to the dimensions of the house, the more spacious houses more kalindang
required.
Figure 6: The separation of structure into three parts (lower, middle and upper).
Lower construction of lamban tuha consists of stilts and wooden blocks shaped square
called kalindang (Figure 6). Poles and kalindangs as a whole bear lamban tuha. Poles and
kalindangs rested on stone footing, it also provides high flexibility in case of shaking during
earthquakes (Rautela and Joshi, 2008). This condition also can keep the timber from moisture
and termites influence.
*Corresponding author (A. Siswanto). Tel. +60173167312 Email address: [email protected] 2013. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 4 No.2 ISSN 2228-9860 eISSN 1906-9642. Online Available at http://TuEngr.com/V04/157-170.pdf
167
Based on the information, the main strength of the lamban tuha 1 is four pillars as main
structure inside the house that bear the beams, these beams become the basis for pillars in the
attic. Construction of lamban tuha 1 does not have a constant pillar intact from the bottom up to
the roof.
Figure 7: The lower structure of lamban tuha (kalindang and stone footings)
Traditional houses in Nias are based on the structure of vertical and slanted posts structures
placed on a stone footing. Vertical posts and X and V are strengthens the element of this
substructure. A three-dimensional structure offers greater resistance and has the elasticity
required for not sticking in the ground (Gruber, 2007). Based on the experience of local
communities, kalindang construction on the lamban tuha has a big role in anticipating the
effects of earthquakes.
South Sumatra traditional architecture belongs to the grand tradition and requires special
skills and expertise in indigenous knowledge. Traditional architecture is not only beautiful and
elegant but also has flexible nail-less construction that has been proven to be earthquake
resistant buildings. This technique adds to the flexibility of the house. Indigenous knowledge
there is representing local wisdom that people have developed for centuries. It is based on long
experience, adapted to local culture and environment.
168 Ari Siswanto, Azizah Salim Binti Syed Salim, Nur Dalilah Dahlan and Ahmad Hariza
5. Conclusion
In principle, lamban tuha have different lower construction system with other traditional
houses in South Sumatra. A series of stilts and kalindangs worked as a system that supports
the load of the house. The building is only supported by wooden pillars and beams as a
foundation and located above stone footing and the pile of timber logs (kalindang).
Besides kalindang, lamban tuha which has connections and details of timber without nails
believed to be powerful force able to withstand earthquake shaking. The timber connections are
very appropriate considering the tensile strength and shear caused by the earthquake. Placement
of kalindang to support the weight of the house is symmetrical and synergize with stilts resting
on stone footings. Overall, the timber connection practices form of press, pivot, pinch, and
pull with reinforced by the dowel.
Local wisdom is reflections of valuable experience from the South Sumatra traditional
architecture which can be utilized as the concept of a sustainable housing development in the
context of anticipate natural disasters such as floods, landslides and earthquakes. The
existence of lamban tuha is an interesting experience of our ancestors that can be used as
thoughts on designing earthquake resistant buildings.
6. Acknowledgements The authors would like to show gratitude to the respondents who took time and patience to
share their experience of phenomenology. Also the authors deliver high appreciation to the
University of Sriwijaya, Indonesia for providing financial support for field data collection.
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Ari Siswanto is a Ph.D. Student at the Department of Architecture, Faculty of Design and Architecture, University Putra Malaysia. MALAYSIA. He is a lecturer at the Department of Architecture, Faculty of Engineering, University of Sriwijaya. INDONESIA. He received a B.Sc. in Architecture from 10th November Surabaya Institute of Technology, INDONESIA and Master of City & Regional Planning from The Ohio State University, USA.
Dr. Azizah Salim is an Associate Professor of Department of Architecture at Universiti Putra Malaysia. She received a B.Sc. in Architecture from Robert Gordon’s Institute of Technology, Aberdeen, SCOTLAND and M.sc. from University College London. U.K. She holds a Ph.D. from University Newcastle-upon-Tyne in U.K. Her interest is in research related to housing and development policies
Dr. Ahmad Hariza is an Associate Professor of Department of Architecture at Universiti Putra Malaysia. MALAYSIA. He received a B.Sc. In Human Development from University Pertanian Malaysia. MALAYSIA. He received M.Sc. and holds a Ph.D. from The University of Birmingham, U.K. His research in housing involves person and environment relationship and housing studies.
Dr. Nur Dalilah Dahlan is a senior lecturer at the Department of Architecture at Universiti Putra Malaysia. She received a B.Sc. in Architecture from University of Malaysia, MALAYSIA and M.sc. In Architecture from Universiti Putra Malaysia. MALAYSIA. She holds a Ph.D. from Cardiff University. U.K. Her interest is in studies how people behave in response to their sensual perceptions when exposed to architectural ambiances developed using passive design approaches.
Peer Review: This article has been internationally peer-reviewed and accepted for publication according to the guidelines given at the journal’s website. Note: The original of this article was accepted and presented at the 2nd International Conference-Workshop on Sustainable Architecture and Urban Design (ICWSAUD) organized by School of Housing, Building & Planning, Universiti Sains Malaysia, Penang, Malaysia from March 3rd -5th, 2012.
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