A tabu search heuristic for the vehicle routing problem
Management Science
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Computers and Operations Research
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Genetic Algorithms in Search, Optimization and Machine Learning
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Simulation Modeling and Analysis
Simulation Modeling and Analysis
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Proceedings of the 33nd conference on Winter simulation
SavingsAnts for the Vehicle Routing Problem
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
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INFORMS Journal on Computing
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Computers and Operations Research
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Proceedings of the 34th conference on Winter simulation: exploring new frontiers
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A hybrid genetic algorithm for the capacitated vehicle routing problem
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Applied Soft Computing
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Engineering Applications of Artificial Intelligence
Winter Simulation Conference
Annals of Mathematics and Artificial Intelligence
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In this paper we present SR-1, a simulation-based heuristic algorithm for the Capacitated Vehicle Routing Problem (CVRP). Given a CVRP instance, SR-1 uses an initial "good solution", such as the one provided by the classical Clarke and Wright heuristic, in order to obtain observations for the variable "distance between two consecutive nodes in a route". These observations are then fitted by a statistical distribution, which characterizes the inter-node distances in good solutions. Then, the fitted distribution is employed to generate a large number of new random solutions with similar edge-size distribution. Thus, a random but oriented local search of the space of solutions is performed, and a list of "best solutions" is obtained. This list allows considering several properties per solution, not only aprioristic costs, which can be practically used when making multiple-criteria decisions. Several tests have been performed to discuss the effectiveness of this approach.