Integrating biased-randomized GRASp with Monte Carlo simulation for solving the vehicle routing problem with stochastic demands

ew problems in Operations Research are regarded as highly as the Vehicle Routing Problem (VRP). Its relevance within management and industrial settings has led to the variants of this problem being widely studied by the scientific community. With the aim of solving the VRP with stochastic demands we analyze an extension of the classical GRASP metaheuristic. This work hybridizes a biased-randomized GRASP (BR-GRASP) with a two-stage Monte Carlo simulation which has the ability to attain robust and competitive solutions. In the first stage, a promising set of local optimum solutions is identified based on a short simulation evaluation. In the second stage, the promising solutions are tested for reliability using a larger number of simulation runs. The most reliable solution is the final solution. Experiment results are provided that demonstrate that the proposed integrated algorithm leads to higher quality solutions than the equivalent approach without such an integration