A simulation-based multi-objective genetic algorithm (SMOGA) procedure for BOT network design problem

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.

Mixed network design using hybrid scatter search

“…Like scatter search, GA is a population-based metaheuristic which was first introduced by Holland (1975). GA has been successfully applied in the network design problems (e.g., Yin, 2000;Drezner and Salhi, 2002;Drezner and Wesolowsky, 2003;Cantarella et al, 2006;Chen et al, 2006).…”

Markov chain analysis of genetic algorithms applied to fitness functions perturbed concurrently by additive and multiplicative noise

“…Their fitness values, f (1), f (2), f (3), and f (4), will be set to 1, 2, 3, and 4, respectively; thus chromosome 4 is the globally optimal solution. We will execute a GA with proportional selection and without crossover.…”

“…mization tool for dealing with noisy objective functions (e.g., Beyer [2], Di Pietro, While, and Barone [5], Chen, Subprasom, and Ji [3]). In theoretical studies that examine evolutionary computation schemes applied to perturbed fitness functions, fitness functions are typically assumed to be disturbed by a single source of additive noise (e.g., Goldberg and Rudnick [6], Miller and Goldberg [13], Nissen and Propach [16], Beyer [2], Rudolph [21], Arnold [1], Di Pietro, While, and Barone [5]).…”

Joint pricing and design of urban highways with spatial and user group heterogeneity

“…The studies are mostly restricted to simple networks, having either parallel or serial links. The given problem has been also examined for the case of general inter-urban road networks in [12][13][14] under alternative project objectives and market condition. These models have considered a standard, deterministic user equilibrium (DUE) traffic assignment procedure with elastic demand to represent the responses of users to optimal flat (uniform) tolls and capacity investments.…”

“…Another strong assumption typically employed in the existing literature (e.g., see [11][12][13][14]) treats road capacity as being adjustable in continuous increments. Provided that, in reality, the number of lanes or links is discrete, the expression of capacity as a discrete variable provides a solution that is infrastructure related, which may involve greater physical intuition and bearing in the road network design for investment planning purposes, in comparison to the solution of the corresponding continuous version of the problem.…”