Stochastic multi-objective models for network design problem

Authors:
Anthony Chen;Juyoung Kim;Seungjae Lee;Youngchan Kim
Affiliations:
Department of Civil and Environmental Engineering, Utah State University, Logan, UT 84322-4110, USA;Center for National Transport Database, The Korea Transport Institute, 2311 DaehwaDong, Ilsan-Gu, Goyang City, Republic of Korea;Department of Transportation Engineering, University of Seoul, Dongdaemoon-Ku, Seoul, Republic of Korea;Department of Transportation Engineering, University of Seoul, Dongdaemoon-Ku, Seoul, Republic of Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

Transportation network design problem (NDP) is inherently multi-objective in nature, because it involves a number of stakeholders with different needs. In addition, the decision-making process sometimes has to be made under uncertainty where certain inputs are not known exactly. In this paper, we develop three stochastic multi-objective models for designing transportation network under demand uncertainty. These three stochastic multi-objective NDP models are formulated as the expected value multi-objective programming (EVMOP) model, chance constrained multi-objective programming (CCMOP) model, and dependent chance multi-objective programming (DCMOP) model in a bi-level programming framework using different criteria to hedge against demand uncertainty. To solve these stochastic multi-objective NDP models, we develop a solution approach that explicitly optimizes all objectives under demand uncertainty by simultaneously generating a family of optimal solutions known as the Pareto optimal solution set. Numerical examples are also presented to illustrate the concept of the three stochastic multi-objective NDP models as well as the effectiveness of the solution approach.