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首页> 外文期刊>Informatica: An International Journal of Computing and Informatics >Penalty Variable Neighborhood Search for the Bounded Single-Depot Multiple Traveling Repairmen Problem
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Penalty Variable Neighborhood Search for the Bounded Single-Depot Multiple Traveling Repairmen Problem

机译:惩罚变量邻域搜索有界单仓库的多仓库多行程修理员问题

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摘要

Multiple Traveling Repairmen Problem (mTRP) is a class of NP-hard combinatorial optimization problems with many practical applications. In this paper, a general variant of mTRP, also known as the Bounded Single-Depot Multiple Traveling Repairmen Problem (Bounded-mTRP), is introduced. In the BoundedmTRP problem, a fleet of identical vehicles is dispatched to serve a set of customers. Each vehicle that starts from the depot is only allowed to visit the number of customers within a predetermined interval, and each customer must be visited exactly once. Such restrictions appear in real-life applications where the purpose is to have a good balance of workloads for the repairmen. The goal is to find the order of customer visits that minimizes the sum of waiting times. In our work, the proposed algorithm is encouraged by the efficiency of the algorithms in [15, 19, 20] that are mainly based on the principles of the VNS [14]. The penalty VNS extends the well-known VNS [14] by including constraint penalization, to solve the BoundedmTRP effectively. Extensive numerical experiments on benchmark instances show that our algorithm reaches the optimal solutions for the problem with 76 vertices at a reasonable amount of time. Moreover, the new best-known solutions are found in comparison with the state-of-the-art metaheuristic algorithms.
机译:多次旅行维修门问题(MTRP)是一类NP硬组合优化问题,具有许多实际应用。本文介绍了MTRP的一般变型,也称为有界单仓多仓多行进修理问题(界限-MTRP)。在绑定的管理问题中,派遣了一支相同的车辆,为一系列客户提供服务。从仓库开始的每个车辆都仅被允许访问预定间隔内的客户数量,并且必须一次访问每个客户。这种限制出现在现实生活中的应用,其中目的是对维修门的工作负载进行良好的平衡。目标是找到客户访问的顺序,从而最大限度地减少等待时间的总和。在我们的工作中,通过主要基于VNS的原理的[15,19,20]中的算法效率来鼓励所提出的算法[14]。惩罚VN通过包括约束惩罚,扩展了众所周知的VNS [14],以有效解决RenededMtrp。基准实例的广泛数值实验表明,我们的算法在合理的时间内用76个顶点的问题达到了最佳解决方案。此外,与最先进的成群质算法相比,找到了新的最着名的解决方案。

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