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Real-time split-delivery pickup and delivery time window problems with transfers

机译:实时分批提货和交货时间窗口问题

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摘要

In this research we present the design and implementation of heuristics for solving split-delivery pickup and delivery time window problems with transfer (SDPDTWP) of shipments between vehicles for both static and real-time data sets. In the SDPDTWP each shipment is constrained with the earliest possible pickup time from the origin and the latest acceptable delivery time to a destination. Split-deliveries occur when two or more vehicles service the same origin or destination. The proposed heuristics were applied to both static and real-time data sets. The heuristics computed a solution, in a few seconds, for a static problem from the literature, achieving an improvement of 60% in distance in comparison to the published solution. In the realtime SDPDTWP problems, requests for pickup and delivery of a package, breakdown of a truck or insertion of a truck can occur after the vehicle has left the origin and is enroute to service the customers. Thirty data sets, each consisting of one to seven real-time customer or truck events, were used to test the efficiency of the heuristics. The heuristics obtained solutions to real-time data sets in under five seconds of CPU time.
机译:在这项研究中,我们介绍了启发式方法的设计和实现,该方法用于解决在静态和实时数据集之间车辆之间的货物转移(SDPDTWP)的分开交付取货和交付时间窗问题。在SDPDTWP中,每次装运都受到从始发地到最近的可接受提货时间的限制。当两辆或多辆车辆服务于相同的起点或目的地时,就会发生拆分交付。拟议的启发式方法适用于静态和实时数据集。启发式算法在几秒钟内就从文献中计算出一个静态问题的解决方案,与已发布的解决方案相比,距离提高了60%。在实时SDPDTWP问题中,车辆离开原点并途中将为客户提供服务后,可能会出现包装取件和交付,卡车故障或卡车插入的请求。 30个数据集(每个数据集由1到7个实时客户或卡车事件组成)用于测试启发式方法的效率。启发式方法在不到5秒的CPU时间内就获得了实时数据集的解决方案。

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