首页> 外文期刊>Proceedings of the Workshop on Principles of Advanced and Distributed Simulation >SIMULATION AND OPTIMIZATION FOR BATCH ORDER PICKING PROBLEM: THE APPLICATION OF ANT COLONY ALGORITHM
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SIMULATION AND OPTIMIZATION FOR BATCH ORDER PICKING PROBLEM: THE APPLICATION OF ANT COLONY ALGORITHM

机译:订单排序问题的仿真与优化:蚁群算法的应用

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This research deals with the batch order picking (BOP) problem in a man-on-board type of automated storage and retrieval system (AS/RS). When retrieval requests consist of multiple items and the items are kept in different locations, the storage/retrieval (S/R) machine must travel to various storage places to complete each batch of orders. In this article an ant colony optimization (ACO), in particular MAX- MIN ant system (MMAS) algorithm is employed for the resolution of BOP problems with multiple stock locations. Meanwhile a multi-agent simulation software called NetLogo is also used to demonstrate the importance of parameter selection in MMAS. The aim of this research is to minimize the total travel distance and travel time of S/R machine.
机译:这项研究解决了人为操作类型的自动存储和检索系统(AS / RS)中的批次订单拣选(BOP)问题。当检索请求包含多个项目并且这些项目保存在不同的位置时,存储/检索(S / R)机器必须移动到各个存储位置以完成每批订单。在本文中,采用蚁群优化(ACO),特别是MAX-MIN蚂蚁系统(MMAS)算法来解决具有多个种群位置的BOP问题。同时,还使用一种称为NetLogo的多代理程序仿真软件来演示MMAS中参数选择的重要性。这项研究的目的是最小化S / R机器的总行驶距离和行驶时间。

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