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A local branching matheuristic for the multi-vehicle routing problem with stochastic demands

机译:随机需求的多辆路线问题的局部分支数学训练

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This paper proposes a local branching matheuristic for the vehicle routing problem with stochastic demands (VRPSD). The problem is cast in a two-stage stochastic programming model, in which routes are planned in the first stage and executed in the second stage. In this setting, a failure may occur if a vehicle does not have sufficient capacity to serve the realized demand of a customer, which is revealed only upon arrival at a customer's location. In the event of a failure, a recourse action is performed by having the vehicle return to the depot to replenish its capacity and resume its planned route at the point of failure. Thus, the objective of the VRPSD is to minimize the sum of the planned routes cost and of the expected recourse cost. We propose a local branching matheuristic to solve the multi-VRPSD. We introduce an intensification procedure applied at each node of the local branching tree. This procedure is embedded in a multi-descent scheme for which we propose a diversification strategy. Extensive computational results demonstrate the effectiveness of our matheuristic.
机译:本文提出了随机需求(VRPSD)的车辆路由问题的局部分支数学素描。问题在两阶段随机编程模型中展示,其中在第一阶段计划了路线并在第二阶段执行。在该设置中,如果车辆没有足够的能力来服务于客户的实现需求,则可能发生故障,这仅在抵达客户的位置揭示。如果发生故障,则通过将车辆返回到库以补充其容量并在故障点恢复其计划路线来执行追索行动。因此,VRPSD的目的是最大限度地减少计划的路线成本和预期追索费用的总和。我们提出了一个局部分支数学素描来解决多VRPSD。我们介绍应用于本地分支树的每个节点的强化过程。该程序嵌入到多个下降方案中,我们提出了多元化策略。广泛的计算结果证明了我们数学素描的有效性。

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