Two memetic algorithms for heterogeneous fleet vehicle routing problems
Engineering Applications of Artificial Intelligence
An effective memetic algorithm for the cumulative capacitated vehicle routing problem
Computers and Operations Research
Formulations and Branch-and-Cut Algorithms for the Generalized Vehicle Routing Problem
Transportation Science
A genetic algorithm for solving the generalized vehicle routing problem
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
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The generalized vehicle routing problem (GVRP) involves finding a minimum-length set of vehicle routes passing through a set of clusters, where each cluster contains a number of vertices, such that the tour includes exactly one vertex from each cluster and satisfies capacity constraints. We consider a version of the GVRP where the number of vehicles is a decision variable. This paper introduces a new mathematical formulation based on a two-commodity flow model. We solve the problem using a branch-and-cut algorithm and a metaheuristic that is a hybrid of the greedy randomized adaptive search procedure (GRASP) and the evolutionary local search (ELS) proposed in [18]. We perform computational experiments on instances from the literature to demonstrate the performance of our algorithms.