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考虑多车配合的AutoStore系统订单拣选排序研究
发表时间:2024-03-29 浏览量:268 下载量:58
全部作者: | 马云峰,崔宇昊,邹雅倩,胡依娜,卢阳 |
作者单位: | 武汉科技大学管理学院;武汉科技大学服务科学与工程研究中心;湖北普罗格科技股份有限公司 |
摘 要: | 为解决AutoStore系统中由于严格要求单辆小车参与整套拣货动作而导致的相同料箱多次翻拣及大量重复成本问题,本文提出堆垛检索概念并设计多车协同拣货作业流程,以减少对料箱的重复翻箱。基于该作业方式建立订单排序优化模型用于求解最优订单序列,并进一步设计了启发式算法SA用于大规模算例求解。数值实验显示,在小规模问题中,SA算法与数学模型最优解的误差仅为0.95%~3.5%;在大规模问题中,SA的性能高出现有方法15%~72%。结果证明了该种作业流程的有效性及SA算法求解订单排序问题的优越性。 |
关 键 词: | 运输系统工程;订单排序;启发式算法; AutoStore系统;多车协同 |
Title: | Research on order picking sorting in the AutoStore system with multi-vehicle collaboration |
Author: | MA Yunfeng,CUI Yuhao,ZOU Yaqian,HU Yina,LU Yang |
Organization: | School of Management, Wuhan University of Science and Technology;Research Center for Service Science and Engineering, Wuhan University of Science and Technology; Hubei Prolog Technology Co., Ltd. |
Abstract: | To solve the problem of repeated picking of the same bins and significant redundant costs in the AutoStore system, which is caused by the strict requirement for a single vehicle to conduct the entire picking process, this paper proposes a concept of stack retrieval and designs a multi-vehicle collaborative picking workflow to reduce the repetitive operation of bins. Based on this operational approach, we establish an order sequencing optimization model to determine the optimal order sequence. Additionally, we design a heuristic algorithm SA to address large-scale instances of the problem. Numerical experiments reveal that, in small-scale scenarios, the SA algorithm yields an error ranging from 0.95% to 3.5% compared to the mathematical optimal solution. In large-scale scenarios, SA outperforms existing methods from 15% to 72%. These results convincingly demonstrate the effectiveness of this operational approach and the superior performance of the SA algorithm in solving the order sequencing problem. |
Key words: | transportation systems engineering; order sorting; heuristic algorithm; AutoStore system; multi-vehicle coordination |
发表期数: | 2024年3月第1期 |
引用格式: | 马云峰,崔宇昊,邹雅倩,等. 考虑多车配合的AutoStore系统订单拣选排序研究[J]. 中国科技论文在线精品论文,2024,17(1):99-110. |

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