BIO-INSPIRED AND MUSICAL-HARMONY APPROACHES FOR MACHINEALLOCATION OPTIMIZATION IN FLEXIBLE MANUFACTURING SYSTEM

UMI KALSOM BINTI YUSOF

UNIVERSITI TEKNOLOGI MALAYSIA

BIO-INSPIRED AND MUSICAL-HARMONY APPROACHES FOR MACHINEALLOCATION OPTIMIZATION IN FLEXIBLE MANUFACTURING SYSTEM

UMI KALSOM BINTI YUSOF

A thesis submitted in fulfilment of therequirements for the award of the degree ofDoctor of Philosophy (Computer Science)

Faculty of ComputingUniversiti Teknologi Malaysia

FEBRUARY 2013

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Dengan Nama Allah yang Maha Pemurah dan Maha Penyayang

Khas buat ayah dan bonda

Teristimewa buat suami tercinta

Untuk anak-anak penyejuk mata dan penawar hati

Ya Allah! Sesungguhnya aku memohon kepadaMu

ilmu yang bermanfaat,

rezeki yang halal

dan amal yang diterima.

iv

ACKNOWLEDGEMENT

”In the name of Allah, most Gracious, most Compassionate”

Though I owe my gratitude to all those people who have made this thesispossible, it is impossible to acknowledge every individual’s contribution here. Aboveall, I am deeply indebted to my supervisors Prof. Dr. Safaai Deris of UniversitiTeknologi Malaysia and Prof. Dr Rahmat Budianto of Universiti Utara Malaysia fortheir unconditional support and encouragement throughout the duration of this study.

I am most grateful to Mr Abdul Latif Mohamad, a Production Control Managerof a well-known semiconductor manufacturing company, who, apart from providingthe necessary information and data, is most responsible for helping me understandhow capacity planning and machine allocation work especially in semiconductormanufacturing. In addition, he is also instrumental in sharpening my writing skills,which is most beneficial to the completion of this thesis. I am also indebted to PuanWan Nadiah Wan Abdullah for proof-reading the manuscript. Thank you very muchfor your dedicated precious time.

I would also like to thank all the members of the Software EngineeringDepartment and Artificial Intelligence and Bioinformatics Research Group (AIBIG)for their continuous support in many aspects of this research. Many friends have helpedme stay strong throughout these challenging years. I greatly value their friendship andI deeply appreciate their support and care towards me.

Most importantly, none of this would have been possible without the love andpatience from my family who has been a constant source of love, concern, support andstrength all these years: Thank you and I love you all. A special thank you too, to myhusband and my children for constantly reminding me that my research should alwaysbe useful and beneficial for all humankind. Last but not least, I thank my parents fortheir unequivocal support, their patience and prayers in making me what I am today.

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ABSTRACT

Manufacturing industries need to constantly adjust to the rapid pace of

change in the market. Many of the manufactured products often have a very short

life cycle. These scenarios imply the need to improve the efficiency of capacity

planning, an important aspect of machine allocation plan that is known for its

complexity. Two common approaches to solve the machine allocation problem

include optimization-based methods and heuristic oriented methods. Although

optimization-based methods are robust in their applicability, they tend to become

impractical when the problem size increases, while heuristic approaches are mainly

dependent on rules and constraints of an individual problem. Due to this, heuristic

approaches always face difficulties to estimate results in a changed environment.

The use of new and innovative meta-heuristic searching techniques of population-

based algorithms in this research can overcome these limitations. The objectives of

this research are to minimize the system unbalance and machine makespan

utilization, and to increase throughput taking into consideration of the technological

constraints. Population-based algorithms that consist of constraint-chromosome

genetic algorithm (CCGA), constraint-vector harmony search (CVHS) and hybrid of

constraint-chromosome genetic algorithm and harmony search (H-CCGaHs) were

adopted. To ensure the feasibility of the results and to promote for a faster

convergence, the right mapping chromosome or harmony memory representation

was applied to the domain problem in all the three algorithms. Genetic algorithm is

known for its exploitative ability, whereas harmony search is recognized for its

explorative capability. H-CCGaHs combines these strengths to produce a more

effective algorithm where both aspects will be optimized and helps avoid getting

trapped in local minima. These three algorithms (CCGA, CVHS and H-CCGaHs)

were tested on both benchmark data (10 datasets) and industrial data (5 datasets).

The results indicated that the proposed H-CCGaHs achieves better results, with

faster convergence and a reasonable time to run the algorithm.

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ABSTRAK

Syarikat pengeluar sentiasa memerlukan adaptasi untuk menghadapi

perubahan pasaran. Kebanyakan daripada produk pengeluar mempunyai kitaran

jangka hayat yang pendek. Senario ini membawa kepada keperluan untuk

memperbaiki kelicinan perancangan kapasiti, satu aspek penting yang mana

perancangan pengagihan mesin yang terkenal dengan kekompleksan. Dua

pendekatan lazim untuk menyelesaikan masalah pengagihan mesin termasuklah

kaedah berdasarkan optimum dan kaedah berorientasikan heuristik. Walaupun

kaedah-kaedah berdasarkan optimum adalah teguh dalam aplikasinya, ia

berkecenderungan menjadi tidak praktikal apabila saiz masalah bertambah,

sementara pendekatan heuristik bergantung kepada peraturan dan kekangan bagi

setiap masalah. Oleh sebab itu, pendekatan heuristik selalu berdepan dengan

masalah untuk menganggarkan hasilan apabila persekitaran berubah. Keterbatasan

ini boleh diatasi dengan penggunaan teknik algoritma carian meta-heuristik

berdasarkan populasi yang baru dan berinovasi dalam kajian terkini. Objektif kajian

ini adalah untuk meminimakan ketidakseimbangan sistem dan penggunaan rentang

buatan (makespan) mesin, dan untuk meningkatkan pengeluaran sambil

mengambilkira kekangan teknologi. Algoritma berdasarkan-populasi yang

mengandungi algoritma genetik berkekangan-kromosom (CCGA), algoritma carian

harmoni berkekangan-vektor (CVHS) dan hibrid algoritma genetik berkekangan-

kromosom dan algoritma carian harmoni (H-CCGaHs) diadaptasikan. Untuk

memastikan kelaksanaan hasilan dan untuk mempromosikan pertembungan yang

lebih cepat, perwakilan pemantauan kromosom atau ingatan harmoni yang betul

diterapkan pada masalah domain dalam ketiga-tiga algoritma tersebut. Algoritma

genetik terkenal dengan kebolehan ekplotatif, manakala carian harmoni terkenal

dengan kebolehan ekploratif. H-CCGaHs menggabungkan kekuatan-kekuatan ini

untuk menghasilkan algoritma yang lebih efektif yang mana kedua-dua aspek

tersebut akan dioptimakan dan membantu untuk mengelakkan daripada

terperangkap dalam minima lokal. Ketiga-tiga algoritma (CCGA, CVHS and H-

CCGaHs) telah diuji ke atas data tanda aras (10 set data) dan data industri (5 set

data). Keputusan menunjukkan bahawa H-CCGaHs mampu mencapai hasilan yang

lebih baik dan pertembungan yang lebih cepat, juga mengambil masa yang

munasabah untuk menjana algoritma.

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TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION iiDEDICATION iiiACKNOWLEDGEMENT ivABSTRACT vABSTRAK viTABLE OF CONTENTS viiLIST OF TABLES xiiLIST OF FIGURES xviLIST OF ABBREVIATIONS xixLIST OF APPENDICES xx

1 INTRODUCTION 11.1 Problem Background 11.2 Challenges of the Flexible Manufacturing System

Dynamic Machine Allocation Problem 41.3 Problem Statement 71.4 Research Goal and Objectives 71.5 Research Scopes and Significance 81.6 Structure of the Thesis 10

2 LITERATURE REVIEW 132.1 Introduction 132.2 Demand Forecast Uncertainty, and Customer Order

and Specification 142.3 Capacity Planning 16

2.3.1 Capacity Planning in Uncertain Environ-ment 18

2.4 Flexible Manufacturing System 202.5 FMS Machine Allocation Problem 23

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2.6 FMS Dynamic Machine Allocation Approaches 312.6.1 Machine Grouping 322.6.2 Machine Allocation With the Presence of

Machine Breakdown 332.7 Population-based Algorithms 34

2.7.1 Genetic Algorithm 352.7.2 Harmony Search Algorithm 372.7.3 Hybrid of Genetic Algorithm and Har-

mony Search 402.8 Trends and Directions 422.9 Summary 43

3 RESEARCH METHODOLOGY 453.1 Introduction 453.2 The Research Framework 453.3 Problem Descriptions 483.4 Datasets and Case Study 51

3.4.1 Benchmark Datasets 513.4.2 Case Study Data 52

3.5 Instrumentation and Result Analysis 543.5.1 Hardware and Software Requirements 543.5.2 Experiments and Analysis 54

3.6 Performance Measures 553.6.1 Notations and Parameters 563.6.2 Efficiency Measures 573.6.3 Productivity Measures 583.6.4 Combined Objective Functions 583.6.5 Constraints 59

3.7 Summary 60

4 MACHINE ALLOCATION OPTIMIZATION USINGBIO-INSPIRED AND MUSICAL-HARMONYAPPROACHES 624.1 Introduction 624.2 The Proposed Constraint-chromosome Genetic

Algorithm (CCGA) 634.2.1 Chromosome Representation and Initial-

ization 66

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4.2.2 Fitness Evaluation 684.2.3 Selection 684.2.4 Crossover 694.2.5 Mutation 704.2.6 Termination Condition 71

4.3 The Proposed Constraint-vector Harmony SearchAlgorithm (CVHS) 734.3.1 Procedure of the Harmony Search Algo-

rithm 734.3.2 The Harmony Search and Machine Allo-

cation Parameters Initialization 754.3.3 The HM With Feasible Machine Alloca-

tion Solutions Initialization 794.3.4 New Harmony Solution Improvisation 794.3.5 Update of Harmony Memory and Fitness

Evaluation 814.4 Experimental Results and Discussions 81

4.4.1 Experimental Results for CCGA 824.4.2 Experimental Results for CVHS 864.4.3 Results Comparisons Between CCGA,

CVHS Algorithms and Other Heuristics 894.5 Summary 93

5 MACHINE ALLOCATION OPTIMIZATION USINGA HYBRID OF BIO-INSPIRED AND MUSICAL-HARMONY APPROACHES 955.1 Introduction 955.2 The Proposed Hybrid of Constraint-chromosome

Genetic Algorithm and Harmony Search Algo-rithms 965.2.1 Parameters Initialization 965.2.2 Population Initialization 965.2.3 Fitness Evaluation 985.2.4 Population Splitting and Swapping 985.2.5 Genetic Algorithm Operators 995.2.6 Harmony Search Algorithm Operators 995.2.7 Termination Criteria 99

5.3 Experimental Results and Discussions 995.3.1 Results Comparisons 101

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5.4 Summary 111

6 INDUSTRIAL MACHINE ALLOCATION CASESTUDY USING BIO-INSPIRED AND MUSICAL-HARMONY APPROACHES 1126.1 Introduction 1126.2 Industrial Machine Allocation 1136.3 Product Loading and Machine Resources Alloca-

tion 1166.4 Randomly Permuting Array for Part Type Sequence 1176.5 Application of Constraint-chromosome genetic al-

gorithm (CCGA) to Industrial Machine Allocation 1206.5.1 Parameters Initialization 1206.5.2 Constraint-chromosome Population Ini-

tialization 1226.5.3 Fitness Evaluation 1256.5.4 Selection 1256.5.5 Genetic Algorithm Operators 126

6.6 Application of Constraint-vector Harmony Search(CVHS) to the Industrial Machine Allocation 1276.6.1 Parameters Initialization 1276.6.2 Harmony Memory Initialization 1296.6.3 Update of Harmony Memory and Fitness

Evaluation 1296.6.4 Harmony Search Operators 129

6.7 Application of Hybrid of Constraint-chromosomeGenetic Algorithm and Harmony Search (H-CCGaHs) to the Industrial Machine Allocation 1306.7.1 Parameters Initialization 1306.7.2 Population Splitting and Swapping 1326.7.3 H-CCGaHs operators 132

6.8 Experimental Results and Discussions 1326.8.1 Constraint-Chromosome Genetic Algo-

rithm Results 1336.8.2 Constraint-vector Harmony Search Re-

sults 1366.8.3 Hybrid of CCGA and HS (H-CCGaHs)

Results 1426.9 Results comparisons and Discussions 142

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6.10 Summary 157

7 INDUSTRIAL DYNAMIC MACHINE ALLOCATIONCASE STUDY USING BIO-INSPIRED AND MUSICAL-HARMONY APPROACHES 1597.1 Introduction 1597.2 Industrial Dynamic Machine Allocation Problem 160

7.2.1 Preventive Maintenance Machine Break-down 162

7.2.2 Stochastic Machine Breakdown 1647.3 Model Formulation 165

7.3.1 Subscripts, Parameters and Notations 1657.3.2 Decision Variables 1657.3.3 Objective Functions 1667.3.4 Assumptions 169

7.4 Solutions for the Dynamic Machine AllocationProblem using Bio-inspired and Musical-harmonyApproaches 1707.4.1 Product Loading and Machine Resources

Allocation 1707.4.2 Applications of CCGA, CVHS and H-

CCGaHs to Dynamic Machine Allocation 1717.4.3 Parameters Initialization 171

7.5 Experimental Results and Discussions 1767.6 General Discussions 1917.7 Summary 196

8 CONCLUSIONS 1988.1 Concluding Remarks 1988.2 Research Contributions 2008.3 Future Works 2038.4 Closing 204

REFERENCES 205

Appendices A – C 217 – 225

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LIST OF TABLES

TABLE NO. TITLE PAGE

2.1 Demand forecast characteristics (Cakanyildirim, 1999) 142.2 Capacity planning tasks and objectives (Chou et al.,

2007) 162.3 Related works on FMS (dynamic) machine allocation

problems 272.4 Related works with their tested problems 282.5 Option Comparison between Musician and Optimization 393.1 Characteristics of the 10-problem datasets from

Mukhopadhyay et al. (1992) 524.1 Description of the dataset 1 (Mukhopadhyay et al.

(1992)) 654.2 Machine pool for dataset 1 654.3 Description of the dataset 8 (adapted from Mukhopad-

hyay et al. (1992)) 774.4 Machine pool for dataset 8 774.5 Machine properties 824.6 Control parameters for the CCGA algorithm 834.7 Detailed results obtained from the CCGA experiment 844.8 Summary of best results of the proposed CCGA 854.9 Detailed allocation for dataset 1 854.10 Control parameters for the CVHS algorithm 864.11 Detailed results obtained from the CVHS experiment 874.12 Summary of best results of the proposed Constraint-

Vector Harmony Search (CVHS) 884.13 Detailed allocation for dataset 8 884.14 Comparison of the proposed CCGA and CVHS with

other heuristics 904.15 Convergence and duration taken to run comparison

between CCGA and CVHS 91

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5.1 Description of the dataset 8 (Mukhopadhyay et al.

(1992)) 1005.2 Control Parameters of the proposed algorithm and

overall Result 1015.3 Detailed results of proposed H-CCGaHs 1025.4 The summary of best results of the proposed H-CCGaHs 1035.5 Detailed allocation for dataset 5 1035.6 Comparison of the proposed H-CCGaHs with proposed

CCGA, CVHS and other heuristics 1045.7 Operation-machine allocation combination for the

assigned part sequence 1055.8 Convergence and duration taken to run comparison

between CCGA, CVHS and H-CCGaHs 1065.9 CIICOF and CIITHMax comparisons with other

heuristics 1096.1 Description of the industrial datasets 1166.2 Machine resource summary 1176.3 Machine capacity table based on operation, machine type

and package type 1186.4 Plan loading summary 1186.5 Comparison on average processing time between con-

ventional randomize and randomized-in-place method 1206.6 Parameters for CCGA 1226.7 Machine pool for dataset production plan (CP) 1246.8 Parameters for CVHS 1276.9 Parameters for H-CCGaHs 1326.10 Results of CCGA for generation 50 - selection of roulette

wheel 1346.11 Results of CCGA for generation 50 - selection of

tournament 1356.12 Results of CCGA for generation 75 - Selection: Roulette

Wheel 1376.13 Results of CCGA for generation 75 - Selection:

Tournament 1386.14 Results of CVHS for generation 50 1406.15 Results of CVHS for generation 75 1416.16 Results of H-CCGaHs for generation 50 - selection of

roulette wheel) 144

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6.17 Results of H-CCGaHs for generation 50 - selection oftournament) 145

6.18 Results of H-CCGaHs for generation 75 - selection ofroulette wheel) 146

6.19 Results of H-CCGaHs for generation 75 - selection oftournament) 147

6.20 Results comparison based on number of generations 1486.21 Results comparison between CCGA and H-CCGaHs

algorithms on the selection of roulette wheel andtournament based on number of generations 148

6.22 Results comparison based on size of population andgeneration 149

6.23 Results on industrial machine allocation problem basedon the best option 151

6.24 Results on industrial machine allocation problem basedon best COF on each dataset 151

6.25 Assigned and unassigned part types versus COF valuesfor CCGA, CVHS and H-CCGaHs 151

6.26 Operation-machine allocation combination for theassigned and unassigned part types sequence 152

6.27 Convergence and duration taken to run for all datasets 1526.28 Detail allocation for dataset production plan (CP) 1556.29 Results comparison between industrial and benchmark

datasets 1577.1 Plan loading summary 1727.2 Machine Resource Summary 1727.3 Machine capacity table based on operation, machine type

and product type 1737.4 Control Parameters for all algorithms studied: (a)

CCGA, (b) CVHS (c) H-CCGaHs 1747.5 Results on dynamic machine allocation problem based

on the best option average 1797.6 Results on dynamic machine allocation problem based

on best COF 1797.7 Results comparison for dataset CP with and without

machine downtime 1817.8 Results comparison for dataset WP1 with and without

machine downtime 181

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7.9 Assigned and unassigned part types versus the fitness forall datasets 183

7.10 Operation-machine allocation combination for theassigned/unassigned part sequence for dataset CP 184

7.11 Quantity to be processed versus quantity assigned fordataset CP 185

7.12 Allocation detail for dataset CP 1867.13 Convergence and duration taken to run 1877.14 Summary of the results 191A.1 List of Related Publications 217B.1 Dataset 1 220B.2 Dataset 2 221B.3 Dataset 3 221B.4 Dataset 4 221B.5 Dataset 5 222B.6 Dataset 6 222B.7 Dataset 7 222B.8 Dataset 8 223B.9 Dataset 9 223B.10 Dataset 10 224C.1 Industrial dataset - CP 225C.2 Industrial dataset - WP1 225C.3 Industrial dataset - WP2 227C.4 Industrial dataset - WP3 228C.5 Industrial dataset - WP4 230

xvi

LIST OF FIGURES

FIGURE NO. TITLE PAGE

1.1 A lean manufacturing machine allocation scenario 41.2 Factors leading to the complex Flexible Manufacturing

System (FMS) machine allocation problem 61.3 Scenario of the problem and research goal 91.4 Structure of the thesis 112.1 The content structure of Chapter 2 132.2 Tool delivery lead time (Cakanyildirim, 1999) 172.3 Growth in capacity and output of a semiconductor

manufacturer (Chou et al., 2007) 192.4 Capacity leading demand (capacity supply surplus) 212.5 Capacity lagging demand (capacity demand surplus) 212.6 Capacity tracking demand 222.7 Relationship between demand forecast, customer order

and capacity planning 222.8 Machine group (Hood et al., 2003) 322.9 Three grouping configurations for four-machine case

(Lee and Kim, 2000) 332.10 Analogy between music improvisation and optimization

process (Geem et al., 2001) 382.11 The harmony memory structure (modified from Al-Betar

and Khader (2012)) 403.1 Research framework 463.2 Contributions of the research 483.3 Derivation of machine allocation problem from produc-

tion plan and WIP 493.4 Domain model class diagram for machine allocation 533.5 The performance measures 564.1 A sample of part sequence 674.2 (a) Possible value of part-operation chromosome. (b)

Another possible value of part-operation chromosome 67

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4.3 Ordered chromosome crossover 704.4 Mutation by reciprocal exchange 714.5 The computational procedure of the basic harmony

search algorithm 764.6 The illustration of a new harmony vector 804.7 Convergence comparison between CCGA and CVHS 914.8 Duration taken to run for CCGA and CVHS 925.1 Population swapping between GA and HS 985.2 Convergence comparison between CCGA, CVHS and H-

CCGaHs 1065.3 Duration taken to run for CCGA, CVHS and H-CCGaHs 1075.4 Maximum throughput comparisons 1086.1 Overview of the semiconductor process: (a) The four

phases of the process and (b) processes involved in theassembly area 114

6.2 A sample of part type sequence 1226.3 a) A possible value of part-chromosome (b) Another

possible value of part-chromosome 1256.4 CCGA - Parameter options 1396.5 CCGA - Run time comparison for (a) generation 50 and

(b) generation 75 1396.6 CVHS - Parameter options 1436.7 CVHS - Run time comparison for (a) generation 50 and

(b) generation 75 1436.8 Convergence versus duration comparisons of CCGA,

CVHS and H-CCGaHs for each dataset (CP, WP1, WP2,WP3 and WP4) 153

6.9 Convergence versus duration comparisons of individualalgorithm for all datasets 154

7.1 Failure potential versus processing time 1637.2 COFs comparisons based on the parameters option for

the datasets studied (Details of the parameter option isshown in Table 7.4) 177

7.3 Throughput comparisons for datasets CP, WP1, WP2,WP3 and WP4 using proposed algorithms (CCGA,CVHS and H-CCGaHs) 180

7.4 Number of assigned part types comparisons 182

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7.5 Convergence versus COF comparisons for the proposedalgorithms (CCGA, CVHS and H-CCGaHs) usingdatasets CP, WP1, WP2, WP3 and WP4 188

7.6 Convergence versus duration and COF versus THcomparisons for the proposed algorithms (CCGA,CVHS and H-CCGaHs) using datasets CP, WP1, WP2,WP3 and WP4 189

8.1 Research summary 201

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LIST OF ABBREVIATIONS

ACO – Ant Colony Optimization

CCGA – Constraint-chromosome Genetic Algorithm

COF – Combined Objective Function

CR – Crossover Rate

CVHS – Constraint-vector Harmony Search

FMS – Flexible Manufacturing System

GA – Genetic Algorithm

HS – Harmony Search

HMS – Harmony Memory Size

HMCR – Harmony Memory Consideration Rate

H-CCGaHs – Hybrid of Constraint-chromosome Genetic Algorithm andHarmony Search

MS – Makespan

MR – Mutation Rate

NI – Number of improvisation

PAR – Pitch Adjustment Rate

PSO – Particle Swarm Optimization

SU – System Unbalance

TH – Throughput

TPT – Throughput Time

WIP – Work-in-process

–

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LIST OF APPENDICES

APPENDIX TITLE PAGE

A LIST OF PUBLICATIONS 217B 10 BENCHMARK DATASETS FROM MUKHOPAD-

HYAY ET AL. (1992) 220C INDUSTRIAL DATASETS 225

CHAPTER 1

INTRODUCTION

1.1 Problem Background

In manufacturing industry, many critical decisions are based on demandforecast (Cakanyildirim, 2002). The demand forecast which is usually projected on6-months rolling forecast, nevertheless, is always subjected to error when it comes toa actual demand. Due to the various factors, the actual demand from the customerswhich is within 2 - 4 months lead-time, is always different from the demand forecast.Since most of the important and risky decisions such as machines or tools purchasesare made based on this unreliable forecast, it is always a goal for the manufacturingindustries to find a method to reduce this risk.

In spite of demand market volatility, manufacturing companies need to adapt astrategy that makes them able to meet the expected demand. One of the approaches isto keep their resources as lean as possible and put the emphasis on producing productsbased on customer’s order (make-to-order). As a result of this growing requirementof customized production environment, many companies are adopting the FlexibleManufacturing System (FMS) to effectively and efficiently optimize available capacityresources through machine allocation with the objective of producing high qualityproducts with a shorter leadtime.

FMS is a manufacturing system in which there is some amount of flexibilitythat allows the system to react to any changes (Chunwei and Zhiming, 2001). It can beclassified as static or dynamic based on how the orders from the customers are beinghandled, allocated and released to the production floor (Saravanan, 2006). In a staticmachine allocation environment, the parts that were allocated are known beforehand;while in a dynamic environment, which is the real manufacturing environment, theallocation of the resources has to consider resource unavailability (machine) over time,

2

such as machine break down or the unexpected demand that can adversely affect theutilization level and efficiency of FMS.

Demand forecast is rarely accurate. Therefore, capacity planning with a goodstrategy plays a very important role in sizing the company in order to meet the currentand future demand from the customers (Olhager et al., 2001). The strategy may includesetting up new facilities, new equipment purchases or optimizing the current availableresources in the facility. It may also include machine upgrading and adjusting theresources to overcome the constraints due to product varieties.

Machine allocation in an advanced manufacturing system such as FMS isconsidered dynamic. Machines break down constantly especially when they are aged.When this occurs, decision needs to be made either to wait for the machine to berepaired or move to another machine. Typical in a hard down situation, the affectedpart type will be moved to another machine to meet to the customer’s requirementdate. Frequent machine breakdowns may result in shop floor nervousness due toinability and lack of continuity in the current shop floor plan because the allocationis exposed to frequent and huge amounts of deviation (Wang et al., 2007). In addition,machine breakdown is one of the major undesirable inputs as it can cause additionalmaintenance cost. Therefore, the ability to quickly reallocate the unfinished part typesto another machine without jerking or causing interruption to the shop floor is the mostdesirable goal of any companies. At the same time, it can minimize the adverse impactof the failures on the objective measures of the machine allocation problem, so theproduction goals can be achieved (Mandal et al., 2010).

The productivity is crucial especially when costly equipments and materials areinvolved in the production because any deviation from the original plan may increasethe production costs. The ’pain’ will be even felt greater in advanced manufacturingplant where the cost of initial investment is huge, the yearly machines and toolsprocurement shoot up to several millions due to high technology equipment base andfast product turn-over. Therefore, it is important to increase the productivity throughmaximizing the overall throughput as well as to balance the works-in-process (WIP)where the resources are highly utilized. In the meantime, to improve the efficiency,it aims on minimizing the system unbalance, makespan and machine hours during thepart allocation. System unbalance is a summation of remaining time (idle time) on allavailable machines. By minimizing system unbalance, utilization level of machinescan be increased. Meanwhile, makespan can be defined as the maximum value ofworking time over cumulative processing time of machines in the given planning

3

horizon. This objective will balance the work-load among machines. Machine hoursdenotes the number of hours allocated to process the parts for given operations, withthe consideration of the machine technological constraints. This scenario is depictedin Figure 1.1.

The machine allocation problems have been extensively researched overthe years, and many findings and contributions have been reported. Neverthelesshitherto, all the studies on machine allocation problem are performed based on theassumption of deterministic environment, where the theories have been a little usedin real manufacturing environment. In the real world, FMS operates in a dynamicenvironment where interruptions such as machine breakdown and reallocation of parttypes can adversely affect the utilization level and efficiency of FMS. There are alot more to offer from research done on the machine allocation to manufacturingindustries, but more work is needed to address the gap between theories and practicein machine allocation.

One of the common assumptions on machine allocation theories, which isunlikely to take place in practice is that the machine allocation environment is static.Most of the machine allocation or loading researches have been focused on providinga good loading plan from deterministic requirements. Very few studies are doneon machine allocation problem that deals with machine’s interruption and controlpolicies; which imitates the real environment of FMS. Recently, Mandal et al. (2010)proposed to include the machine breakdowns on machine allocation model in aneffort to minimize the effect of the breakdowns so that profitability can be boosted.Consequently, dynamic machine allocation process is vital in order to improve theperformance of the allocation plan due to the dynamic problem that is inherited by theaforementioned factors.

In the real industrial practice, dynamic machine allocation problem is handledmanually by human schedulers who observe the potential problems and revise theallocation based on their knowledge and experiences. However, the combinatorialcomplexity of the machine allocation problem tends to overburden them and leads topoor allocation performances. Mandal et al. (2010) proposed a model that combinesthe online monitoring scheme, where the machines are continuously monitored tomeasure the failure potential and the actions are determined beforehand to avoid apotential breakdown. In addition, manual monitoring ensures the action is taken assoon as possible to minimize the impact due to the sudden breakdown.

4

Figure 1.1: A lean manufacturing machine allocation scenario

1.2 Challenges of the Flexible Manufacturing System Dynamic MachineAllocation Problem

Most of manufacturing industries make a practice of preparing the demandforecast planning for every five to ten years ahead. This is important to project thecompany’s growth, to prepare for any facilities expansion or to procure additionalmachines and tools with better capabilities. The plan is constantly reviewed andadjusted, usually on quarterly or half yearly basis.

In any manufacturing industry, there always is a bottle neck area that themanagement would need to review. They have to decide either to expand the capacityor replace the older machines with newer technology so that the company can continueto grow. The machines and tools delivery leadtime typically range from 6-12 months.Hence, machine procurement plan need to be based on at least a year demand forecast(Cakanyildirim, 2002) which is hardly accurate. It will be more difficult to plan orto set up capacity requirement for a new product line that involves new machinesportfolio.

5

Balancing capital investment against the customer demands in a volatileenvironment has always been a challenge for any industry. Machines are purchasedwell in advance but the actual production build will start only when orders arereceived. With rapidly changing technology, many companies are adopting build-to-order concept. In other word, there is no product built ahead for inventory, waitingfor the customers’ order. This scenario is very much apparent especially in high-technology industries.

Resource allocation and capacity adjustment is another obstacle faced by themanufacturing industries. Most of the time, the orders from customers are differentfrom the demand forecasted earlier. Some orders are totally in a different productmix. Product mix changes can have a big impact on capacity. This requires the plantto adjust the machines allocation, raw materials and manpower in order to meet thisconstantly changing customers demand.

On top of it, each product has its own unique requirements and may requirea different set of technological processes. It is common in a big manufacturing plantto have a mix between the old and new machines, with different capabilities. Newermachines have better capabilities and are faster as compared to the older machines.It will be a challenge to a planner to schedule and allocate machines that suit tothe requirements of the products that need to be built. It may in the process, createbottleneck areas in one process and idling stations in another process, thus hinderingthe plant from optimizing the throughput and profit.

Unexpected events may occur while processing or running products.Customer’s pull-in the orders or machine breakdowns are common occurrence in anymanufacturing plant and this requires the planner to make adjustments on the allocationof the available resources. These constraints require that the manufacturing plants arebe able to maximize the machine resources when there are machine breakdowns ormachines scheduled for preventive maintenance. The inability to adjust to suit to theactual demand may put the company into loss of business opportunities and may havedirect impact on the bottom-line performance (profit and loss) of a company. In short,a company with the ability to allocate resources to meet to the actual demand wouldnot only survive but also thrive in this competitive market.

The challenges create a dynamic machine allocation scenario that is morecomplex and difficult than the conventional machine allocation, as signified in Figure1.2. The complexities intensify as versatile machine configuration makes the machine

6

Demand ForecastCustomer Order and Specification

Resource Allocation and Capacity Adjustment

Stochastic Demand Scenario

Machine Technological

Constraint

Bottleneck Process

Wide Variety of Products

Change of Product Requirement

Complex FMS(Dynamic) Machine Allocation

Due To

Result In

Machine Breakdown

Produce

Figure 1.2: Factors leading to the complex Flexible Manufacturing System (FMS)machine allocation problem

flexible to perform different operations; hence rendering many allocation options. Thisscenario creates a large scale of number of machines with a variety of products thatincrease the combinatorial complexity; added with the variability of parameters (batchsize, processing times, unit per hour of the machine (UPH), etc.) and constraints(machine, resources capabilities).

In addition, widely studied intelligent methods such as genetic algorithm (GA)can be used to solve the machine allocation problem. GA has been researched for manyyears and it is one of the most common methods reported in the literature, mainly dueto its ability to provide good performance solutions. It has the capability of mimicthe whole problem to be solved, and easily adjusts the variability of FMS parametersand constraints that are faced in the real manufacturing problem. In addition, a newintelligent method, harmony search (HS), is also among the promising meta-heuristicalgorithms. Although it is not yet performed on machine allocation domain area, the

7

results that have been shown in many literatures in other domain areas promise a goodcomparative result.

1.3 Problem Statement

In order for the manufacturing industries to response effectively to thechallenges in a volatile manufacturing environment, an effective and practical approachis needed to address the real FMS machine allocation problem. The approach shouldbe able to optimize the current available resources in considering change of customersrequirements and machine breakdowns.

Thus, the main research question of this study is:

How to practically and effectively optimize the machine resources due to

the change of customers’ requirements and machine breakdowns in optimizing the

productivity and efficiency of the machine allocation in FMS?

1.4 Research Goal and Objectives

The goal of this research is a practical and effective dynamic machineallocation approach for FMS. In order to be practical, it has to consider the realFMS environment as well as the desired manufacturing objectives so as to providean acceptable solution with satisfactory performance. Likewise, it also have to providehigh quality solutions not only with respect to the efficiency that improves quality andreduces the production time and makespan, but also maximize the resource utilizationand throughput.

The general objectives would be to design and evaluate population-basedalgorithms to maximise throughput, and minimize the system unbalance andmakespan. More specifically, the objectives of the study are:

(i) To design and evaluate two population-based algorithms, i.e. constraint-chromosome genetic algorithm (CCGA) and constraint-vector harmony

8

search (CVHS) for maximizing throughput and minimizing systemunbalance.

(ii) To design and evaluate a hybrid of the population-based algorithms, ie H-CCGaHs for further improvement of the system in terms of throughput(maximized) and system unbalance (minimized).

(iii) To design and evaluate machine allocation model that in incorporating withand without machine breakdowns in actual manufacturing industry andevaluate the performance by using population-based algorithms (CCGA,CVHS and H-CCGaHs).

Figure 1.3 summarizes the scenarios leading to the problem and the goal of thisstudy. There are three main issues that contribute to the dynamic machine allocationproblem. The first one is the gap between forecasted demand and actual demand of thecustomer; second is the change of customer requirement and specification; and third isthe disruption of the machine resources due to machine breakdown.

1.5 Research Scopes and Significance

As the FMS machine allocation involves some level of flexibilities, variousparameters, constraints and uncertainties that further complicate the problem; somescopes and limitations have been made in order to make it tractable. The scopes andlimitations of this research are as follows:

(i) This study considers only discrete manufacturing system and therefore,production is referred to as parts production;

(ii) Non-splitting of part type - this implies that a part type undertaken forprocessing is to be completed for all its operations before considering a newpart type; Production requirements of part types cannot be split among themachines. This means, if an operation of a part type is assigned to a machine,all requirements of that part should be processed on the same machine.

(iii) Unique part type routing - although flexibility exists in the selection of amachine for optional operation, the operation must be completed on the samemachine once a machine is selected.

(iv) Sharing of tool slots is not considered.

9

In order for the manufacturing industries to response effectively to the challenges in a volatile manufacturing

environment, an effective and practical approach is needed to address the real FMS machine allocation

problem. The approach should be able to optimize the current available resources in considering change of customers’ requirements and machine breakdowns.

Problem Statement

A practical and effective dynamic machine allocation approach for flexible manufacturing system

Research Goal

Customer Order and Specifications

Demand Forecast

����������������������������������������������������������������������������������������������������������������

Efficiency

Productivity

Minimum system unbalance

Balanced WIP

����������������������������������������������������������������������������������������������������������������

Maximum throughput

Minimum makespan

Minimum machine hours

Dynamic Machine Allocation

Machine breakdowns

Figure 1.3: Scenario of the problem and research goal

(v) Parts are readily available. The resources such as pallets, fixtures, etc., usedin the system are sufficient and readily available.

(vi) Material handling time between machines is negligible.

(vii) Machines required for an operation are determined.

(viii) The number of machines slots needed for each type is given.

(ix) Processing times are deterministic and given in advance.

(x) Machine life and the number of available copies for each machine type aregiven in advance.

(xi) Dedicated machines for certain part types are determined in advance.

(xii) The real FMS data used in this study are provided by the industrialcollaborator.

10

This research is considered significant as it tends to solve (dynamic) machineallocation problems due to the constraint of the resources (machines) as well asdue to machine breakdown(s) that commonly happens in all lean manufacturingcompanies. The proposed algorithms; constraint-based genetic algorithm (CCGA),harmony search (HS) and a hybrid of these two algorithms will provide an alternativeto the decision makers to achieve near optimal solutions with less computation cost andtime. In addition, the proposed dynamic machine allocation strategy will accommodatethe remaining part types with a minimum number of deviations to the current loading,thus providing a promising approach to the decision makers to cater to shop floornervousness.

The performance measures considered in this research are concerned about theproductivity of the short-term planning and work-in-process (WIP), and the customersatisfaction. This is in tandem with one of the aims of lean manufacturing to improvethe quality, and reduce the production time and cost. Therefore, the success of thisresearch will support the betterment of the lean manufacturing performance. Theimprovement can be achieved through the maximization of throughput, minimizationof system unbalance, and minimization of machine make span utilized while satisfyingthe technological constraints such as machine time availability and tool slots.Furthermore, this research will also suggest the manufacturing approach on machineallocation strategy.

1.6 Structure of the Thesis

This thesis is organized into eight chapters. Figure 1.4 shows the structure ofthe thesis.

Brief descriptions of the contents of each chapter are given as follows:

(i) The thesis begins with discussions on some problem background, goal,objectives, scopes and significance of this research as featured in Chapter1.

(ii) Chapter 2 reviews some related works in the area as well as related domainsthat would help in understanding the rest of the thesis.

(iii) Chapter 3 describes the research methodology employed in this researchincluding the research framework, data sources, instrumentation, problem

11

Figure 1.4: Structure of the thesis

description, performance measures, experiment and analysis used in thethesis.

(iv) Then, Chapter 4 discusses on how the machine allocation is being handled inthe machine allocation problem using two population algorithms; constraint-chromosome genetic algorithm (CCGA) and harmony search (HS). It alsocompares the performance of proposed algorithms with current literatures aswell as discusses on the strength of these algorithms;

(v) Chapter 5 discusses on the development of hybrid of constraint-chromosomegenetic algorithm and harmony search called H-CCGaHs and evaluatedagainst the current literatures and the two-algorithms previously developed.

12

(vi) Then, Chapter 6 discusses on the applications of the three algorithms (CCGA,HS and H-CCGaHs) on industry problem datasets, taking into considerationthe product specification and machine resource technological constraints.The results from three algorithms are compared.

(vii) Further on, Chapter 7 discusses the design of the dynamic machine allocationframework and approach in real industrial data taking into considerationthe machine breakdowns. The performance of the proposed approach isevaluated using CCGA, HS and H-CCGaHs.

(viii) Finally, Chapter 8 concludes the findings, contributions and potential futureresearch to be conducted as derived from this study.

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