A Special Case of the Multiple Traveling Salesmen Problem in End-of-Aisle Picking Systems

Lennart Baardman, K. J. Roodbergen, H. J. Carlo, Albert H. Schrotenboer
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引用次数: 13

Abstract

This study focuses on the problem of sequencing requests for an end-of-aisle automated storage and retrieval system in which each retrieved load must be returned to its earlier storage location after a worker has picked some products from the load. At the picking station, a buffer is maintained to absorb any fluctuations in speed between the worker and the storage/retrieval machine. We show that, under conditions, the problem of optimally sequencing the requests in this system with a buffer size of m loads forms a special case of the multiple traveling salesmen problem in which each salesman visits the same number of cities. Several interesting structural properties for the problem are mathematically shown. In addition, a branch-and-cut method and heuristics are proposed. Experimental results show that the proposed simulated annealing-based heuristic performs well in all circumstances and significantly outperforms benchmark heuristics. For instances with negligible picking times for the worker, we show that this heuristic provides solutions that are, on average, within 1.8% from the optimal value.
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通道末端拣选系统中多旅行推销员问题的一个特例
本研究的重点是通道末端自动存储和检索系统的排序请求问题,在该系统中,每个检索的负载必须在工人从负载中取出一些产品后返回到其先前的存储位置。在拾取站,保持一个缓冲器,以吸收工人和存储/检索机器之间的任何速度波动。我们证明,在一定条件下,该系统中缓存大小为m个负载的请求最优排序问题形成了每个推销员访问相同数量城市的多旅行推销员问题的特殊情况。数学上给出了这个问题的几个有趣的结构性质。此外,还提出了一种分切法和启发式算法。实验结果表明,所提出的模拟退火启发式算法在各种情况下都表现良好,显著优于基准启发式算法。对于工人采摘时间可以忽略不计的例子,我们表明这种启发式方法提供的解决方案平均在最优值的1.8%以内。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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