A hybrid metaheuristic algorithm for the vehicle routing problem with stochastic demands

Abstract This article deals with the Vehicle Routing Problem with Stochastic Demands. To solve this problem, a hybrid metaheuristic combining a Memetic Algorithm and Greedy Randomized Adaptive Search Procedure is designed. The developed approach is tested on a 40 instances benchmark. The results are validated by comparing them to state of the art metaheuristics, they show that our method outperforms these metaheuristics in terms of quality and efficiency. A new testbed of 39 instances with up to 385 customers is also proposed and tested. This paper is the first one to deal with closer to real life size problems.

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