Optimization and Engineering

, Volume 7, Issue 3, pp 225–247 | Cite as

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

  • Anthony Chen
  • Kitti Subprasom
  • Zhaowang Ji


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.


Network design problem Multiple objectives Demand uncertainty Simulation Genetic algorithm 


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Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringUtah State UniversityLoganUSA
  2. 2.Planning Division, Department of HighwaysBangkokThailand

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