Applying Multiobjective Bilevel Optimization under Fuzzy Random Environment to Traffic Assignment Problem: Case Study of a Large-Scale Construction Project

Abstract

This article presents an optimization method for solving a material flow traffic assignment problem (TAP) in a large-scale construction project considering a hierarchical structure with fuzzy random variables. A multiobjective bilevel decision-making model is established in which the transportation time and cost in each arc are considered as fuzzy random variables. The construction contractor, the leader in the hierarchy, aims to minimize both total direct and transportation time costs. The transportation agency, next in the hierarchy, assesses the target to minimize total transportation cost. To deal with the uncertainties, the expected value operator and chance constraint method are used to transform the uncertain model into a calculable one. Furthermore, a multiobjective bilevel particle swarm optimization algorithm with a fuzzy random simulation-based constraint checking procedure is applied to solve the model. Finally, the Shuibuya Hydropower Project is used as a practical example to demonstrate the practicality and efficiency of the proposed model. Results and a sensitivity analysis are presented to highlight the performance of the optimization method.