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

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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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References

  • Chan WT, Fwa TF, Tan CY (1994) Road-maintenance planning using genetic algorithms – I. formulation. J Transportation Eng 120(5):693–709

    Article  Google Scholar 

  • Chen A, Subprasom K, Chootinan P (2001) Assessing financial feasibility of build-operate-transfer project under uncertain demand. Transportation Res Rec 1771:124–131

    Google Scholar 

  • Chen A, Subprasom K, Ji Z (2003) Mean-variance model for the build-operate-transfer scheme under demand uncertainty. Transportation Res Rec 1857:93–101

    Google Scholar 

  • Balling RJ, Taber JT, Brown MR, Day K (1999) Multi-objectives urban planning using genetic algorithm. J Urban Plann Dev 125(2):86–99

    Article  Google Scholar 

  • Coelloc CA, Van Velhhuizen DA, Lamont GB (2002) Evolutionary algorithm for solving multi-objective problems. Kluwer Academic Publishers, MA

    Google Scholar 

  • Cree ND, Maher MJ, Paechter B (1998) The continuous equilibrium optimal network design problem: A genetic algorithm approach. In: Transportation networks: recent methodological advances. Elsevier, Oxford, pp 163–174

  • Daskin MS (1995) Network and discrete location: models, algorithms, and applications. John Wiley and Sons, NY

    MATH  Google Scholar 

  • Deb K (2001) Multi-objective optimization using evolutionary algorithms. John Wiley and Sons, NY

    MATH  Google Scholar 

  • Fisk CS (1984) Optimal signal controls on congested networks. In: Proceedings of the Ninth International Symposium on Transportation and Traffic Theory, Delft, Netherlands, pp 197–226

  • Friesz TL, Cho H-J, Mehta NJ, Tobin RL, Anandalingam G (1992) A simulated annealing approach to the network design problem with variational inequality constraints. Transportation Sci 26(1):18–26

    Article  MATH  Google Scholar 

  • Fwa TF, Tan CY, Chan WT (1994) Road-maintenance planning using genetic algorithms – II. analysis. J Transportation Eng 120(5):710–722

    Article  Google Scholar 

  • Glover F, Kochenberger GA (2003) Handbook of metaheuristics. Kluwer Academic Publishers, MA

    MATH  Google Scholar 

  • Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, MA

    MATH  Google Scholar 

  • Haidar A, Naoum S (1997) Application of genetic algorithms in the excavation process in road construction. In: Proceedings of the 4th Congress on Computing in Civil engineering, Reston, Virginia. pp 1319–1324

  • Holland JH (1975) Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control and artificial intelligence, University of Michigan Press, Ann Harbor, Michigan

  • Lippai I, Heaney JP, Laguna M (1999) Robust water system design with commercial intelligent search optimizers. J Comput Civil Eng 13(3):135–143

    Article  Google Scholar 

  • Markowitz H (1952) Portfolio selection. J Finance 7:77–91

    Article  Google Scholar 

  • McKay MD (1988) Sensitivity and uncertainty analysis using a statistical sample of input values. In: Yigal Ronan (ed) Chapter 4 in Uncertainty Analysis, CRC Press: FL

  • Meng Q, Yang H (2002) Benefit distribution and equity in road network design. Transportation Res Part B 36(1):19–35

    Article  MathSciNet  Google Scholar 

  • Osyczka A, Kundu S (1995) A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm. Struct Optim 10:94–99

    Article  Google Scholar 

  • Schott JR (1995) Fault tolerant design using single and multicriteria genetic algorithm optimization, Master’s thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA

  • Sheffi Y (1985) Urban transportation networks: equilibrium analysis with mathematical programming methods. Prentice-Hall, NJ

  • Sidney ML (1996) Build, operate, transfer: Paving the way for tomorrow’s infrastructure. John Wiley and Sons, NY

  • Yang H, Bell MGH (1998) Traffic restraint, road pricing and network equilibrium. Transportation Res B. 31:303–314

    Article  Google Scholar 

  • Yang H, Meng Q (2000) Highway pricing and capacity choice in a road network under a build-operate-transfer scheme. Transportation Res A. 34:207–222

    Google Scholar 

  • Yang H, Meng Q (2002) A note on highway pricing and capacity choice in a road network under a build-operate-transfer scheme. Transportation Res A 36:659–663

    Google Scholar 

  • Yang J, Soh CK (1997) Structural optimization by genetic algorithms with tournament selection. J Comput Civil Eng 11(3):195–200

    Article  Google Scholar 

  • Yin Y (2000) Genetic-algorithms-based approach for bilevel programming models. J Transportation Eng 126(2):115–120

    Article  Google Scholar 

Download references

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Correspondence to Anthony Chen.

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Chen, A., Subprasom, K. & Ji, Z. A simulation-based multi-objective genetic algorithm (SMOGA) procedure for BOT network design problem. Optim Eng 7, 225–247 (2006). https://doi.org/10.1007/s11081-006-9970-y

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  • DOI: https://doi.org/10.1007/s11081-006-9970-y

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