一种人工蜂群解决订单拣选问题的实现

Luis Enrique Cisneros Saucedo, J. P. Sánchez-Solís, F. López-Ramos, Jorge Rodas-Osollo
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引用次数: 1

摘要

人工蜂群算法是一种基于群体智能的优化方法,已被证明能够在多种优化问题上获得满意的结果。然而,这种优化方法的实现在拣货问题上还没有得到太多的探索,尽管拣货占典型仓库总运营成本的55%。对于像食品银行这样的非营利组织来说,订单选择问题更为重要,因为它们的预算有限。在本章中,作者实现了一种ABC算法来解决食品银行内的订单挑选问题。目标是确定在优化过程中哪个参数值贡献最大。实验采用九组ABC参数;结果表明,该方法适用于该研究实例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of an Artificial Bee Colony to Solve an Order Picking Problem
The artificial bee colony (ABC) algorithm is an optimization method based on swarm intelligence which has demonstrated to be capable of obtaining satisfactory results on a diversity of optimization problems. However, the implementation of this optimization method hasn't been much explored on order picking problems, even though order picking represents up to 55% of the total operational cost of a typical warehouse. The order picking problem has even more importance on nonprofit organizations like food banks since they operate with a limited budget. In this chapter, the authors implemented an ABC algorithm to solve the order picking problem within a food bank. The goal was to determine which parameter values contribute the most during the optimization process. Experiments were conducted using nine sets of parameters for the ABC; results show that the approach is suitable for the study case.
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