Solving multiobjective vehicle routing problem with stochastic demand via evolutionary computation

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

142 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)813-839
Journal / PublicationEuropean Journal of Operational Research
Volume177
Issue number2
Online published20 Feb 2006
Publication statusPublished - 1 Mar 2007
Externally publishedYes

Abstract

This paper considers the routing of vehicles with limited capacity from a central depot to a set of geographically dispersed customers where actual demand is revealed only when the vehicle arrives at the customer. The solution to this vehicle routing problem with stochastic demand (VRPSD) involves the optimization of complete routing schedules with minimum travel distance, driver remuneration, and number of vehicles, subject to a number of constraints such as time windows and vehicle capacity. To solve such a multiobjective and multi-modal combinatorial optimization problem, this paper presents a multiobjective evolutionary algorithm that incorporates two VRPSD-specific heuristics for local exploitation and a route simulation method to evaluate the fitness of solutions. A new way of assessing the quality of solutions to the VRPSD on top of comparing their expected costs is also proposed. It is shown that the algorithm is capable of finding useful tradeoff solutions for the VRPSD and the solutions are robust to the stochastic nature of the problem. The developed algorithm is further validated on a few VRPSD instances adapted from Solomon's vehicle routing problem with time windows (VRPTW) benchmark problems.

Research Area(s)

  • Evolutionary algorithms, Multiobjective optimization, Stochastic, Vehicle routing problems