Journal of Mathematical Modelling and Algorithms

, Volume 5, Issue 1, pp 91–110

Hybrid Metaheuristics for the Vehicle Routing Problem with Stochastic Demands

Authors

  • Mauro Birattari
    • IRIDIAUniversité Libre de Bruxelles
  • Marco Chiarandini
    • Intellectics Group
  • Max Manfrin
    • IRIDIAUniversité Libre de Bruxelles
  • Monaldo Mastrolilli
    • IDSIA
  • Luis Paquete
    • Intellectics Group
  • Olivia Rossi-Doria
    • School of ComputingNapier University
  • Tommaso Schiavinotto
    • Intellectics Group
Article

DOI: 10.1007/s10852-005-9033-y

Cite this article as:
Bianchi, L., Birattari, M., Chiarandini, M. et al. J Math Model Algor (2006) 5: 91. doi:10.1007/s10852-005-9033-y
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Abstract

This article analyzes the performance of metaheuristics on the vehicle routing problem with stochastic demands (VRPSD). The problem is known to have a computationally demanding objective function, which could turn to be infeasible when large instances are considered. Fast approximations of the objective function are therefore appealing because they would allow for an extended exploration of the search space. We explore the hybridization of the metaheuristic by means of two objective functions which are surrogate measures of the exact solution quality. Particularly helpful for some metaheuristics is the objective function derived from the traveling salesman problem (TSP), a closely related problem. In the light of this observation, we analyze possible extensions of the metaheuristics which take the hybridized solution approach VRPSD-TSP even further and report about experimental results on different types of instances. We show that, for the instances tested, two hybridized versions of iterated local search and evolutionary algorithm attain better solutions than state-of-the-art algorithms.

Mathematics Subject Classifications (2000)

68T2090C2790C5990B0690C15

Key words

objective function approximationlocal searcha priori tour
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Copyright information

© Springer Science+Business Media, Inc. 2005