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

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Bio-inspired Algorithms for the Vehicle Routing Problem

Part of the book series: Studies in Computational Intelligence ((SCI,volume 161))

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Summary

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.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore

    Chun Yew Cheong & Kay Chen Tan

Authors
  1. Chun Yew Cheong
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  2. Kay Chen Tan
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Editor information

Francisco Babtista Pereira Jorge Tavares

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© 2009 Springer-Verlag Berlin Heidelberg

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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. In: Pereira, F.B., Tavares, J. (eds) Bio-inspired Algorithms for the Vehicle Routing Problem. Studies in Computational Intelligence, vol 161. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85152-3_5

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  • DOI: https://doi.org/10.1007/978-3-540-85152-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85151-6

  • Online ISBN: 978-3-540-85152-3

  • eBook Packages: EngineeringEngineering (R0)

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