Hybrid fuzzy-Bayesian decision support tool for dynamic project scheduling and control under uncertainty

Abstract

Construction projects are complex and highly uncertain due to their special characteristics. Considering the incapability of current project scheduling methods in addressing the associated uncertainty, there is an emerging need for a dynamic scheduling and monitoring tool during project implementation. This paper addresses this need by proposing a dynamic decision support tool which employs fuzzy set theory to assess the combinatory effect of multiple risk factors on activities duration and Bayesian network to control and predict productivity under uncertainty. Advantages of proposed decision support tool comparing to existing approaches are: (1) calculating the fuzzy weighted average through an exact analytical solution which avoids creating incorrect fuzzy membership functions; (2) employing Type-2 interval-valued fuzzy numbers to model the uncertainty in expert evaluations, and (3) utilizing the advantages of Bayesian network in updating prior probability distributions of duration and predict productivity with actual activity data. Proposed model contributes to knowledge that could help minimize the schedule overrun and improve overall project performance. To elaborate the methodology, modelling and updating procedures of a group of activities in a simulated bridge project is presented as a case study.