Modeling and optimization of expected travel time for multi‐aisle AS/RSs with two‐class‐based storage policy

Hu Yu
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Abstract

Class‐based storage policy with optimized contour‐shaped class boundary can significantly improve storage system's performance. Surprisingly, this policy has not been explored in widely used multi‐aisle automated storage and retrieval systems (MA‐AS/RSs), which use one storage and retrieval machine to serve multiple aisles with the help of an aisle‐transfer technique. This paper investigates the two‐class‐based storage policy with contour‐shaped class boundary in MA‐AS/RSs that use a transfer car for aisle transfer. The aim is to optimize the system dimensions and class boundary by minimizing system's expected travel time. Based on the approximation of the MA‐AS/RS with a continuous cube and the proposed hierarchical procedure, analytical expected travel time expressions for systems with any dimensions and class boundary are calculated. In addition, based on several proved properties, closed‐form optimal system dimensions and class boundary are derived. Numerical results show the accuracy of our continuous cubic approximation is sufficient. By measuring the performance using the average of expected travel time over all tested systems with various dimensions, we find that (1) class‐based policy with our optimal class boundary can respectively improve the performance by at least 40%, 10%, and 50% compared to three previous policies in the case of 20/80 ABC curve; and (2) system with our optimal dimensions can improve the performance by about 20%–30%. Several managerial insights for warehouse practitioners are presented.
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基于两类存储策略的多通道自动旅客捷运系统预期旅行时间的建模与优化
基于类别的存储策略具有优化的轮廓形状类别边界,可以显著提高存储系统的性能。令人惊讶的是,这种策略尚未在广泛使用的多通道自动存储和检索系统(MA-AS/RS)中得到探索,该系统使用一台存储和检索机器,借助通道转移技术为多个通道提供服务。本文研究了在使用转运车进行通道转运的 MA-AS/RS 系统中基于两类的存储策略,该策略具有等高线形状的类边界。其目的是通过最小化系统的预期旅行时间来优化系统尺寸和等级边界。基于对连续立方体 MA-AS/RS 的近似和所提出的分层程序,计算出了具有任意尺寸和等级边界的系统的预期旅行时间分析表达式。此外,基于几个已证明的属性,还推导出了闭式最优系统尺寸和等级边界。数值结果表明,我们的连续三次方近似方法具有足够的准确性。通过使用不同维度的所有测试系统的平均预期旅行时间来衡量性能,我们发现:(1) 在 ABC 曲线为 20/80 的情况下,与之前的三种策略相比,采用我们的最优类边界的基于类的策略可分别提高至少 40%、10% 和 50%的性能;(2) 采用我们的最优维度的系统可提高约 20%-30% 的性能。本文为仓库从业人员提供了一些管理启示。
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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