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Dynamic Vehicle Routing Problem with Multiple Depots

机译:具有多个仓库的动态车辆路径问题

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Vehicle Routing Problems (VRPs) have been extensively studied and applied in many fields. Variants of VRPs have been proposed and appeared in researches for many decades. Dynamic Vehicle Routing Problem with Multiple Depots (D-MDVRP) extends the variation of VRPs to dynamism of customers by knowing the information of customers (both locations and due dates) at diverse times. An application of this problem can be found in food delivery services which have many service stores. The customer delivery orders are fulfilled by the scattered service stores where can be analogous to depots in D-MDVRP. In this example the information of all customer orders are not known at the same time depending on arrivals of customers. Thus the objective of this operation is to determine vehicle routing from service stores as well as dispatching time. This paper aims to develop the heuristic for D-MDVRP. The proposed heuristic comprises of two phases: route construction and vehicle dispatch. Routes are constructed by applying Nearest Neighbor Procedure (NNP) to cluster customers and select the proper depot, Sweeping and Reordering Procedures (SRP) to generate initial feasible routes, and Insertion Procedure (IP) to improve routing. Then the determination of dispatch is followed in the next phase. In order to deal with the dynamism, the dispatch time of each vehicle is determined by maximizing the waiting time to provide the opportunity to add more arriving customers in the future. An iterative process between two phases is adopted when a new customer enters the problem, and the vehicles are dispatched when the time becomes critical. From the computational study, the heuristic performs well on small size test problems in a shorter CPU time compared to the optimal solutions from CPLEX, and provides an overall average 8.36 % Gap. For large size test problems, the heuristic is compared with static problems, and provides an overall average 3.48 % Gap. (48 views)
机译:车辆路径问题(VRP)已被广泛研究并应用于许多领域。已经提出了VRP的变体,并在研究中出现了数十年。具有多个仓库的动态车辆路径问题(D-MDVRP)通过了解不同时间的客户信息(位置和到期日),将VRP的变化扩展到客户的动态性。这个问题的应用可以在有许多服务商店的送餐服务中找到。客户的交货订单由分散的服务商店完成,这些商店可以类似于D-MDVRP中的仓库。在此示例中,根据客户的到来,无法同时知道所有客户订单的信息。因此,该操作的目的是从服务商店确定车辆路线以及调度时间。本文旨在开发D-MDVRP的启发式方法。提议的启发式方法包括两个阶段:路线构建和车辆调度。通过应用最近邻居程序(NNP)来群集客户并选择适当的仓库,清扫和重新排序程序(SRP)以生成初始可行路由以及插入程序(IP)以改进路由来构造路由。然后在下一个阶段中遵循调度的确定。为了应对这种动态,通过最大化等待时间来确定每辆车的调度时间,以提供将来增加更多到达客户的机会。当新客户遇到问题时,将在两个阶段之间进行迭代过程,并在时间紧迫时调度车辆。通过计算研究,与CPLEX的最佳解决方案相比,启发式算法在更短的CPU时间内就小尺寸测试问题表现良好,并且平均总差距为8.36%。对于大型测试问题,将启发式方法与静态问题进行比较,并提供平均3.48%的总体差距。 (48观看次数)

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