Journal of Mathematical Modelling and Algorithms

, Volume 5, Issue 1, pp 91-110

First online:

Hybrid Metaheuristics for the Vehicle Routing Problem with Stochastic Demands

  • Leonora BianchiAffiliated withIDSIA Email author 
  • , Mauro BirattariAffiliated withIRIDIA, Université Libre de Bruxelles
  • , Marco ChiarandiniAffiliated withIntellectics Group
  • , Max ManfrinAffiliated withIRIDIA, Université Libre de Bruxelles
  • , Monaldo MastrolilliAffiliated withIDSIA
  • , Luis PaqueteAffiliated withIntellectics Group
  • , Olivia Rossi-DoriaAffiliated withSchool of Computing, Napier University
  • , Tommaso SchiavinottoAffiliated withIntellectics Group

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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)

68T20 90C27 90C59 90B06 90C15

Key words

objective function approximation local search a priori tour