Goal programming approach to solving network design problem with multiple objectives and demand uncertainty

The transportation network design problem (NDP) with multiple objectives and demand uncertainty was originally formulated as a spectrum of stochastic multi-objective programming models in a bi-level programming framework. Solving these stochastic multi-objective NDP (SMONDP) models directly requires...

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Veröffentlicht in:Expert systems with applications Jg. 39; H. 4; S. 4160 - 4170
Hauptverfasser: Chen, Anthony, Xu, Xiangdong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.03.2012
Schlagworte:
Chance-constrained model
Demand
Dependent-chance model
Expected value model
Goal programming
Marketing
Mathematical models
Multi-objective
Multiple objective
Network design
Programming
Stochasticity
Uncertainty
Chance-constrained model
Dependent-chance model
Network design
Multi-objective
Expected value model
Goal programming
ISSN:0957-4174, 1873-6793
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Zusammenfassung:The transportation network design problem (NDP) with multiple objectives and demand uncertainty was originally formulated as a spectrum of stochastic multi-objective programming models in a bi-level programming framework. Solving these stochastic multi-objective NDP (SMONDP) models directly requires generating a family of optimal solutions known as the Pareto-optimal set. For practical implementation, only a good solution that meets the goals of different stakeholders is required. In view of this, we adopt a goal programming (GP) approach to solve the SMONDP models. The GP approach explicitly considers the user-defined goals and priority structure among the multiple objectives in the NDP decision process. Considering different modeling purposes, we provide three stochastic GP models with different philosophies to model planners’ NDP decision under demand uncertainty, i.e., the expected value GP model, chance-constrained GP model, and dependent-chance GP model. Meanwhile, a unified simulation-based genetic algorithm (SGA) solution procedure is developed to solve all three stochastic GP models. Numerical examples are also presented to illustrate the practicability of the GP approach in solving the SMONDP models as well as the robustness of the SGA solution procedure.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2011.09.118