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An iterated local search for the Traveling Salesman Problem with release dates and completion time minimization

机译:迭代本地搜索Traveling Salesman Problem,发布日期和完成时间最小化

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In the Traveling Salesman Problem (TSP) with release dates and completion time minimization an uncapacitated vehicle delivers to customers goods which arrive at the depot over time. A customer cannot be served before the demanded goods arrive at the depot. A release date is associated with each customer which represents the time at which the goods requested by the customer arrive at the depot. The vehicle may perform multiple routes, all starting and ending at the depot. The release dates of the customers served in each route must be not larger than the time at which the route starts. The objective of the problem is to minimize the total time needed to serve all customers, given by the sum of the traveling time and the waiting time at the depot. The waiting time is due to the fact that the vehicle has to wait at the depot until the latest release date of the customers it is going to serve in the next route. We introduce some properties, propose a mathematical programming formulation and present a heuristic approach based on an iterated local search where the perturbation is performed by means of a destroy-and-repair method. Two alternative repair operators, one simple and fast and the other based on a mathematical programming model, are proposed, which give rise to two variants of the heuristic. The mathematical formulation is used to find the optimal solution on instances with up to 20 customers, built from benchmark instances for the classical TSP. Comparison with optimal solutions shows that both algorithms provide high-quality solutions. Tests are also made on larger instances to compare the performance of the two variants of the heuristic. (C) 2018 Elsevier Ltd. All rights reserved.
机译:在发布日期和完成时间最小化的旅行商问题(TSP)中,一辆无能力的车辆向客户交付随时间推移到达仓库的货物。在需求的货物到达仓库之前无法为客户服务。每个客户都有一个下达日期,代表客户要求的货物到达仓库的时间。车辆可以执行多条路线,所有路线均始于仓库。在每个路线中服务的客户的下达日期必须不大于路线开始的时间。该问题的目的是使服务所有客户所需的总时间减到最少,这是由运输时间和在仓库的等待时间之和得出的。等待时间是由于车辆必须在仓库中等待,直到客户要在下一路线服务的最新发布日期为止。我们介绍了一些属性,提出了一种数学编程公式,并提出了一种基于迭代局部搜索的启发式方法,在该方法中,通过破坏和修复方法执行扰动。提出了两种替代的修复算子,一种简单而又快速,另一种基于数学编程模型,它们产生了启发式的两种变体。数学公式用于根据多达20个客户的实例(从传统TSP的基准实例构建)找到最佳解决方案。与最佳解决方案的比较表明,两种算法都可以提供高质量的解决方案。还对较大的实例进行测试,以比较启发式算法的两种变体的性能。 (C)2018 Elsevier Ltd.保留所有权利。

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