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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Chan WT, Fwa TF, Tan CY (1994) Road-maintenance planning using genetic algorithms – I. formulation. J Transportation Eng 120(5):693–709
Chen A, Subprasom K, Chootinan P (2001) Assessing financial feasibility of build-operate-transfer project under uncertain demand. Transportation Res Rec 1771:124–131
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
Balling RJ, Taber JT, Brown MR, Day K (1999) Multi-objectives urban planning using genetic algorithm. J Urban Plann Dev 125(2):86–99
Coelloc CA, Van Velhhuizen DA, Lamont GB (2002) Evolutionary algorithm for solving multi-objective problems. Kluwer Academic Publishers, MA
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
Deb K (2001) Multi-objective optimization using evolutionary algorithms. John Wiley and Sons, NY
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
Fwa TF, Tan CY, Chan WT (1994) Road-maintenance planning using genetic algorithms – II. analysis. J Transportation Eng 120(5):710–722
Glover F, Kochenberger GA (2003) Handbook of metaheuristics. Kluwer Academic Publishers, MA
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, MA
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
Markowitz H (1952) Portfolio selection. J Finance 7:77–91
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
Osyczka A, Kundu S (1995) A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm. Struct Optim 10:94–99
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
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
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
Yang J, Soh CK (1997) Structural optimization by genetic algorithms with tournament selection. J Comput Civil Eng 11(3):195–200
Yin Y (2000) Genetic-algorithms-based approach for bilevel programming models. J Transportation Eng 126(2):115–120
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/s11081-006-9970-y