Applying Multiobjective Bilevel Optimization under Fuzzy Random Environment to Traffic Assignment Problem: Case Study of a Large-Scale Construction Project
Publication: Journal of Infrastructure Systems
Volume 20, Issue 3
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.
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Acknowledgments
This research was supported by the Key Program of NSFC (Grant No. 70831005), “985” Program of Sichuan University “Innovative Research Base for Economic Development and Management” and the Research Foundation of Ministry of Education for the Doctoral Program of Higher Education of China. The authors greatly appreciate the editors and anonymous referees for their helpful and constructive comments and suggestions, which have helped to improve this paper.
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Published In
Journal of Infrastructure Systems
Volume 20 • Issue 3 • September 2014
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© 2014 American Society of Civil Engineers.
History
Received: May 14, 2012
Accepted: Jan 24, 2013
Published online: Jan 26, 2013
Discussion open until: Jul 6, 2014
Published in print: Sep 1, 2014
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