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Hybridizing Problem-Specific Operators with Meta-heuristics for Solving the Multi-objective Vehicle Routing Problem with Stochastic Demand

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 12 - Chapter in an edited book (Author)peer-review

Abstract

This book chapter extends a recently published work on solving the multi-objective vehicle routing problem with stochastic demand (VRPSD). In that work, a few problem-specific operators, including two search operators for local exploitation and the route simulation method (RSM) for evaluating solution quality, were proposed and incorporated with a multi-objective evolutionary algorithm (MOEA). In this chapter, the operators are hybridized with several meta-heuristics, including tabu search and simulated annealing, and tested on a few VRPSD test problems adapted from the popular Solomon's vehicle routing problem with time window (VRPTW) benchmark problems. The experimental results reveal several interesting problem and algorithmic characteristics which may have some bearing on future VRPSD research.
Original languageEnglish
Title of host publicationBio-inspired Algorithms for the Vehicle Routing Problem
EditorsFrancisco Babtista Pereira, Jorge Tavares
PublisherSpringer Berlin Heidelberg
Pages101-129
ISBN (Print)9783540851516
DOIs
Publication statusPublished - 2009
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
Volume161
ISSN (Print)1860-949X

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