A simulation-based multi-objective genetic algorithm (SMOGA) procedure for BOT network design problem |
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Authors: | Anthony Chen Kitti Subprasom Zhaowang Ji |
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Affiliation: | (1) Department of Civil and Environmental Engineering, Utah State University, Logan, Utah 84322-4110, USA;(2) Planning Division, Department of Highways, Bangkok, 10400, Thailand |
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Abstract: | 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. |
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Keywords: | Network design problem Multiple objectives Demand uncertainty Simulation Genetic algorithm |
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