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A metaheuristic for the delivery man problem with time windows

机译:带时间窗口的送货员问题的成分态

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摘要

The Delivery Man Problem with Time Windows (DMPTW) is an extension of the Delivery Man Problem. The objective of DMPTW is to minimize the sum of customers' arrival time while the deliveries are made during a specific time window given by the customers. Another close variant of objective is a travel duration. In the case, the problem minimizes the sum of travel durations between a depot and customer locations. It has many practical applications to network problems, e.g., whenever servers have to accommodate a set of requests to minimize clients' total (or average) waiting time. To solve medium to large-sized instances, a two-phase metaheuristic algorithm is proposed. A construction stage generates a feasible solution using Neighborhood Descent with Random neighborhood ordering (RVND), and the optimization stage improves the feasible solution with an Iterated Local Search. Moreover, Tabu Search (TS) is incorporated in the proposed algorithm to prevent it from getting trapped into cycles. Therefore, our algorithm is prevented from becoming stuck at local optima. The results of experimental simulations are compared with well-known and successful metaheuristic algorithms. These results show that the proposed algorithm reaches better solutions in many cases.
机译:带时间窗的送货员问题(DMPTW)是送货员问题的扩展。DMPTW的目标是在客户给定的特定时间窗口内交货时,将客户的到达时间总和最小化。另一个与目标密切相关的变量是旅行持续时间。在这种情况下,该问题使车辆段和客户地点之间的旅行持续时间之和最小化。它对网络问题有许多实际应用,例如,每当服务器必须容纳一组请求以最小化客户端的总(或平均)等待时间时。为了求解大中型实例,提出了一种两阶段元启发式算法。构造阶段使用随机邻域排序的邻域下降生成可行解,优化阶段使用迭代局部搜索改进可行解。此外,该算法还引入了禁忌搜索(TS),以防止陷入循环。因此,我们的算法不会陷入局部最优。实验模拟的结果与著名和成功的元启发式算法进行了比较。这些结果表明,该算法在很多情况下都能获得更好的解。

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