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.
Original language | English |
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Pages (from-to) | 225-247 |
Number of pages | 23 |
Journal | Optimization and Engineering |
Volume | 7 |
Issue number | 3 |
DOIs | |
Publication status | Published - 1 Sept 2006 |
Externally published | Yes |
Keywords
- Demand uncertainty
- Genetic algorithm
- Multiple objectives
- Network design problem
- Simulation
ASJC Scopus subject areas
- Software
- Civil and Structural Engineering
- Aerospace Engineering
- Mechanical Engineering
- Control and Optimization
- Electrical and Electronic Engineering