Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-540-85152-3_5
Title: Hybridizing problem-specific operators with meta-heuristics for solving the multi-objective vehicle routing problem with stochastic demand
Authors: Cheong, C.Y.
Tan, K.C. 
Issue Date: 2009
Source: Cheong, C.Y.,Tan, K.C. (2009). Hybridizing problem-specific operators with meta-heuristics for solving the multi-objective vehicle routing problem with stochastic demand. Studies in Computational Intelligence 161 : 101-129. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-540-85152-3_5
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. © 2009 Springer-Verlag Berlin Heidelberg.
Source Title: Studies in Computational Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/56242
ISBN: 9783540851516
ISSN: 1860949X
DOI: 10.1007/978-3-540-85152-3_5
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