Hybrid Fuzzy-Bayesian Dynamic Decision Support Tool for Resource-Based Scheduling of Construction Projects
Abstract
This dissertation proposes a flexible and intelligent decision support tool for scheduling
and resource allocation of construction projects. A hybrid Fuzzy-Bayesian scheduling network
and a new optimization model and solution approach have been developed to assess the
combinatory effect of different risk factors on scheduling and optimize the time-cost tradeoff.
Developed decision support tool employs interval-valued fuzzy numbers and Bayesian
networks to dynamically quantify uncertainty and predict project performance during its make
span. Using interval-valued fuzzy numbers makes the model more flexible and intelligent
comparing to conventional fuzzy risk assessment models through incorporating the decision
makers` confidence degree. The linguistic assessments of experts regarding the likelihood and
severity of increase or decrease in task duration and cost when influenced by different risk
factors are used to generate a set of duration and cost prior-probability distributions.
A learning dynamic Bayesian scheduling network is developed to probabilistically
combine the prior-probability distributions with initial activity duration estimates and update
them as new evidence in form of actual activity data feed into the network. This model also
predicts project performance at any point of time during its execution. Optimization model
explicitly considers variation of time-cost tradeoff relationship during project execution and
complex payment terms to maximize the project net present value (NPV). A sequential solution
approach is proposed to combine a procedure for updating time-cost tradeoff data, and mixed
integer linear programming (MILP) methods to obtain optimal project crashing and scheduling
solutions that is adaptive to the current project status and crew productivity. Capability of
proposed model in quantifying uncertainty at initial phases of project where project
performance data are scarce, learning from data and predicting project performance,
considering financial aspects of scheduling through optimal resource allocation and providing
useful and clear advice to managers are advantages of developed decision support tool over
already existing approaches.
Table of Contents
Introduction -- Literature review -- Methodology -- Case study and model validation -- Conclusion and recommendations -- Appendix. Detailed Fuzzy Weighted Average Calculations for a-cut = 0 Based on the Max-Min
Paired Elimination Algoritm
Degree
Ph.D.