DECISION SUPPORT SYSTEM
FOR INFRASTRUCTURE MAINTENANCE USING COMBINED GRAPH THEORY AND HOUSE OF QUALITY
1
Dr. Moustafa Kassab
Infrastructure Asset /Challenges
Decision Making: Maintenance/ Rehabilitation/ Replacement
Graph Theory
House of Quality Deployment Function
Proposed Decision Support System for Infrastructure Maintenance
Case Study
Conclusion and Future work
2
Agenda
3
Objective
- Develop flexible MCDM Based DSS for by utilizing the graph theory
and House of Quality for infrastructure related decisions in
maintenance projects that meet customer’ requirements.
- Validate the decision support tools by applying to case studies in
infrastructure contracts (i.e. Transportation/ water systems )
Ageing infrastructure
Importance to Economy and social Developments
Increasing population
Budget Cuts: for Health / Education Priorities
Sustainability
Government Investment and relaxing Regulations
Infrastructure Asset /Challenges
Build New facilities
Maintain & Expand Existing
Rehabilitate Old facilities
5
House of Quality (HoQ)
It Is a set of matrices which contains
the maintenance requirements "
what's" and the detailed information to
achieve these requirements (How's)
Stakeholders groups (based on project
goal, previous data-base, and
experiences) fill in the matrices based
on their project goal and its priorities
A key to the HoQ is making sure each
group answer the same question about
the same relationship (What vs. How)
Projects Delay and Failure
Projects delay & Failure: HoQ Advantages
MCDM Process for Infrastructure Projects
Identify the Problem
Identify the Alternatives
Evaluate the decision
Establish Evaluation Criteria
Evaluate Alternatives
Make a decision
Sensitivity Analysis
User
(Municipality)
Interface
- Past Experience
- Survey
Technical Req’d
Criteria
VOC
Customers Req’d
- On-line survey
- Expert
- Graph Theory
a)
b)
c)
OptimumDecision
Proposed DSS Framework
DSS
Components
Characteristics of
Infrastructure'|
Maintenance Project
House of Quality
Proposed DSS Framework
Point Scores
Pre-Qualification Score for “What”s
Pre-qualification Score for “How”s
Pre-qualification Score for each contractor
Pre-qualification Index for Each contractor
Mathematical background
Proposed DSS Framework
- Infrastructure Maintenance Project ( Transportation / Water network)
- The Municipally want to call contractors for bidding
- Municipality revised the contractors list and considered short list of 6 contractors with close bidding price
- In selecting the suitable contractor : the owner ( municipality listed its requirements (what's) as follow:
Delivery on Time and schedule
Deliver with planned expenditures
Follow Code and technical specifications
Accountability and warrantees
Honesty and professional Management
Adopting safety and sustainability
- In addition, the Municipality review the contactors ability based on the following attributes (How's):
WE= Work Experience
M = Management
FS= Financial stability
R = Reputation
Q = Quality
TE = Technology and equipment
CI = Creativity and innovation
S = Sustainability
- The Municipality (Decision makers) will rank the contractors and make its awarding decision accordingly
Example Case Study
Proposed DSS Framework Case Study
ص
ص
ص
ص
ص
ص
WE
ص
ص
ص
ص
ص
ص
M
ص
ص
ص
ص
ص
ص
FS
ص
ص
ص
ص
ص
ص
R
ص
ص
ص
ص
ص
ص
Q
ص
ص
ص
ص
ص
ص
TE
ص
ص
ص
ص
ص
ص
CI
ص
ص
ص
ص
ص
ص
S
Step:1
Using the convenience of Graph theory and its nodes (Hows) and interrelations ( arrows)
among the required atributes
Proposed DSS Framework
For each contractor and based on the characteristics of the project , owner (DM) assigns weight of
importance for each “How” and weight of importance for each “What “
Case StudyStep: 2 Data Matrix
status of each “What”
Proposed DSS Framework
Owner ( Municipality) assign weighting scores for interrelation between the
various criteria (How) of the in the hose of quality ( Roof -matrix)
Case StudyStep:3 Roof Matrix
Work
Experience
(WE)
Management
(M)
Creavity
Innovation
(CI)
– 0.665 0.665
0.59 0.745 0.745
0.335 – 0.5
0.5 0.665 0.665
0.5 0.745 0.745
0.335 0.5
0.335 0.5 0.5
0.335 0.5
Relative Important of Attributes
Reputation
(R)
M
Attributes
Technology &
Equipments
(TE)
Sustainability
(S)
Financial
stability
(FS)
Quality
(Q)
R 0.225 0.335 0.225 0.5
0.225 0.335 0.255 0.5 0.5
0.665
TE – 0.59 0.5 0.745 0.745
WE 0.41 0.5 0.5 0.665
0.5
S 0.41 – 0.41 0.665 0.665
0.255 0.335 0.255 0.5
–
–
0.745
Q 0.225 0.335 0.255 – 0.5
FS 0.5 0.59 – 0.745
–
CR
Relative importance of attributes scale
Class descriptionRelative
Importance
One attribute is exceptionally
less important over the
other
0.045
One attribute is extremely less
important over the other
0.135
One attribute is very less
important over the other
0.255
One attribute is less important
over the other
0.335
One attribute is slightly less
important over the other
0.410
Two attributes are equally
important over the other
0.500
One attribute is slightly more
important over the other
0.590
One attribute is more important
over the other
0.665
One attribute is very more
important over the other
0.745
One attribute is extremely more
important over the other
0.865
One attribute is exceptionally
more important over the other
0.955
Proposed DSS Framework
For each contractor and based on the
characteristics of the project , owner
assign weight of importance for each
“How” and weight of importance for
each “what “
Case StudyStep:4
weight of important for each How
Strength of interrelation between
each “what” and “How”
Proposed DSS Framework
For each contractor: calculate PSWi and then Max_PSW
Step: 5 Process Matrix
Case Study
PSW of each requirement (what) with respect to the contactor ability (How) = 20x0.45x0.6x1=5.4
status of each How
PS
Pre
qu
ali
fic
ati
on
sc
ore
fo
r s
pe
cif
ic H
ow
Prequalification score for specific How
Proposed DSS Framework
For each contractor Pre-qualification Index : calculate CPI = PSWi / Max_PSW
Contractor # 1: 1008.8/ 176.8 = 57 %
Step: 6 Ranking contractors and Making Decision
Case Study
First
Last
Ageing infrastructure/ Increasing population
Infrastructure maintenance projects: Municipalities challenges
Complicated maintenance projects => Need for effective DSS
DSS : Combined Graph theory and House of Quality is an effective system
Reduce changes in Design, planning and procedure
Good for communication decision making and planning
Allow for a lot of information in small time and space
Future work Integrating AI : Computerized Neural-networks, and Expert system
Conclusion and Future work
Thank You
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