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.
Original language | English |
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Pages (from-to) | 1608-1619 |
Number of pages | 12 |
Journal | Expert Systems with Applications |
Volume | 37 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Mar 2010 |
Externally published | Yes |
Keywords
- Bi-level program
- Genetic algorithm
- Multi-objective
- Network design
- Stochastic program
- Traffic assignment
- User equilibrium
ASJC Scopus subject areas
- General Engineering
- Computer Science Applications
- Artificial Intelligence