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

Anthony Chen, Xiangdong Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

53 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)4160-4170
Number of pages11
JournalExpert Systems with Applications
Volume39
Issue number4
DOIs
Publication statusPublished - 1 Mar 2012
Externally publishedYes

Keywords

  • Chance-constrained model
  • Dependent-chance model
  • Expected value model
  • Goal programming
  • Multi-objective
  • Network design

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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