| 考虑商品数量和商品拣选成本的AGV智能仓库订单分批问题研究 |
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| 引用本文： | 张国维,吴凌云.考虑商品数量和商品拣选成本的AGV智能仓库订单分批问题研究[J].运筹与管理,2022,31(12):9-15. |
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| 作者姓名： | 张国维 吴凌云 |
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| 作者单位： | 1.中国科学院 数学与系统科学研究院 应用数学研究所,管理、决策与信息系统重点实验室,北京 100190;2.中国科学院大学 数学科学学院,北京 100049 |
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| 基金项目： | 北京市智能物流系统协同创新中心开放课题重点项目(BILSCIC-2019KF-18) |
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| 摘 要： | AGV(Automated Guided Vehicle,自动导引车)智能仓库是一种基于“货到人”拣选模式的自动化仓库。本文考虑了订单中商品的需求量和货架上商品的存储量,以极小化货架搬运成本和商品拣选成本为目标,建立了AGV智能仓库订单分批问题的整数规划模型。本文针对订单分批问题的特点,提出了一种基于订单和货架交替选择的贪婪求解算法。对比CPLEX求解器的精确最优解,本文提出的贪婪算法的误差百分比不超过10%,平均误差百分比为5.38%;对比基于相似性的分批算法的求解结果,本文提出的贪婪算法不仅运算时间更短,解的质量也更好。进一步地,对比不考虑商品拣选成本的订单分批模型,本文提出的模型在不明显增加货架搬运成本的前提下,可以大幅度降低商品拣选成本。因此,在订单分批模型中考虑商品拣选成本是非常必要的。
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| 关 键 词： | AGV智能仓库 订单拣选 订单分批 整数规划 贪婪算法 |
| 收稿时间： | 2020-11-18 |
Research on the Order Batching Problem in the AGV-based Intelligent Warehouse Considering the Product Quantity and the Product Picking Cost |
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| ZHANG Guo-wei,WU Ling-yun.Research on the Order Batching Problem in the AGV-based Intelligent Warehouse Considering the Product Quantity and the Product Picking Cost[J].Operations Research and Management Science,2022,31(12):9-15. |
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| Authors: | ZHANG Guo-wei WU Ling-yun |
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| Affiliation: | 1. Key Laboratory of Management, Decision and Information Systems, Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;2. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | Automated Guided Vehicle (AGV) based intelligent warehouse is a type of parts-to-picker automated warehouse. In this paper, we consider the product quantity required in the orders and stored in the pods and build an integer programming model for the order batching problem by minimizing the pod carrying cost and the product picking cost. Motivated by the characteristics of the order batching problem, we propose a greedy algorithm based on the alternative selection of orders and pods. Compared with the CPLEX solver, the error percentage of the proposed greedy algorithm is less than 10%, and the average error percentage is 5.38%. Compared with the similarity-based order batching algorithm, the proposed greedy algorithm possesses a shorter computation time and better solution quality. Furthermore, compared with the order batching model without considering the product picking cost, the proposed model could substantially reduce the product picking cost without significantly increasing the pod carrying cost. Therefore, it is necessary to consider the product picking cost in the order batching model. |
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| Keywords: | AGV-based intelligent warehouse order picking order batching integer programming greedy algorithm |
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