A framework for integrating fuzzy set theory and discrete event simulation in construction engineering

Discrete event simulation is a powerful modeling tool that has been utilized to model numerous construction engineering operations. The role of experts is very important in designing and defining many of the simulation model parameters. Current simulation modeling practices do not have a methodology for integrating experts' knowledge and opinion. This thesis presents a methodology to incorporate experts' knowledge and opinion within the simulation framework in construction engineering applications, in order to enhance discrete event simulation modeling capabilities. The concepts of fuzzy set theory are adopted to incorporate the experts' knowledge within the simulation framework, because fuzzy set theory is capable of modeling experts' way of thinking and can easily capture their decision-making processes. The components of fuzzy modeling framework is proposed and integrated within the simulation modeling framework. Three main applications of the fuzzy modeling framework are identified. The first application is utilizing fuzzy numbers in modeling cost range estimating as compared to probabilistic range estimating. The second application is utilizing fuzzy expert system tools in predicting activity behavior within the simulation framework, using the tunnel boring machine (TBM) penetration rate prediction as a case study. The third application is utilizing fuzzy expert systems in the decision-making process within the simulation framework, using the prioritization of modules awaiting assembly in a module assembly yard as a case study. The integrated fuzzy modeling and discrete event simulation framework has proven to be very promising in enhancing the modeling capabilities of discrete event simulation. Integrated fuzzy and discrete event simulation modeling is capable of explicitly and more confidently predicting the behavior of an activity within the simulation framework and incorporating the experts' decisions while simultaneously accounting for the uncertainty embedded within the decision making process.