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

Anthony Chen, Kitti Subprasom, Zhaowang Ji

Research output: Journal article publicationJournal articleAcademic researchpeer-review

74 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)225-247
Number of pages23
JournalOptimization and Engineering
Issue number3
Publication statusPublished - 1 Sept 2006
Externally publishedYes


  • Demand uncertainty
  • Genetic algorithm
  • Multiple objectives
  • Network design problem
  • Simulation

ASJC Scopus subject areas

  • Software
  • Civil and Structural Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Control and Optimization
  • Electrical and Electronic Engineering


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