A simulation-based multi-objective genetic algorithm (SMOGA) procedure for BOT network design problem

• Published in: Optimization and engineering Vol. 7; no. 3; pp. 225 - 247
• Main Authors: Chen, Anthony, Subprasom, Kitti, Ji, Zhaowang
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Nature B.V 01.09.2006
• Subjects: Genetic algorithms; Studies
• ISSN: 1389-4420; 1573-2924
• DOI: 10.1007/s11081-006-9970-y

Issue Title: Special Issue on Optimization Under Uncertainty Solving optimization problems with multiple objectives under uncertainty is generally a very difficult task. Evolutionary algorithms, particularly genetic algorithms, have shown to be effective in solving this type of complex problems. In this paper, we develop a simulation-based multi-objective genetic algorithm (SMOGA) procedure to solve the build-operate-transfer (BOT) network design problem with multiple objectives under demand uncertainty. The SMOGA procedure integrates stochastic simulation, a traffic assignment algorithm, a distance-based method, and a genetic algorithm (GA) to solve a multi-objective BOT network design problem formulated as a stochastic bi-level mathematical program. To demonstrate the feasibility of SMOGA procedure, we solve two mean-variance models for determining the optimal toll and capacity in a BOT roadway project subject to demand uncertainty. Using the inter-city expressway in the Pearl River Delta Region of South China as a case study, numerical results show that the SMOGA procedure is robust in generating 'good' non-dominated solutions with respect to a number of parameters used in the GA, and performs better than the weighted-sum method in terms of the quality of non-dominated solutions.[PUBLICATION ABSTRACT]