无人仓订单拣选效率影响因素分析

吴志樵, 兰永恒, 秦恒乐

系统工程理论与实践 ›› 2023, Vol. 43 ›› Issue (4) : 1192-1202.

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PDF(4849 KB)
系统工程理论与实践 ›› 2023, Vol. 43 ›› Issue (4) : 1192-1202. DOI: 10.12011/SETP2022-2080
论文

无人仓订单拣选效率影响因素分析

    吴志樵1,2, 兰永恒1, 秦恒乐3
作者信息 +

The impact of picker’s factors on the order picking efficiency in unmanned warehouse

    WU Zhiqiao1,2, LAN Yongheng1, QIN Hengle3
Author information +
文章历史 +

摘要

自动引导车 (automated guided vehicle, AGV) 的应用极大优化了订单拣选过程中的货物搬运流程, 同时也使员工行为以及员工与 AGV 的有效配合逐渐成为制约电商企业订单履约中心 (order fulfillment center, OFC) 拣选效率的关键因素. 本文基于京东 OFC 运作业务与实时数据, 首先从员工行为视角识别出了包含员工类型、工作时段、当日已连续工作时长等影响订单拣选效率的核心因素. 进一步建立生存分析回归模型, 分析了各因素对不同类型员工的拣选效率影响差异, 指出员工类型是影响拣选效率的主要因素. 最后基于仿真平台将员工行为因素引入拣选任务指派, 验证了生存分析模型及影响因素的有效性, 结果显示考虑员工行为因素下可提升约 23% 订单拣选效率.

Abstract

Picker has become the critical factor that affects the OFC's (order fulfillment center) picking efficiency due to the application of automated guided vehicles (AGVs). Based on the collected individual behavior data, this paper identified several factors that affect order-picking efficiency, including picker type, picking time slot (queue), continuous working time on the day, and so on. Then, the survival analysis regression model is further established, and the impact of various factors on the different types of pickers was quantitatively analyzed. It was pointed out that the type of pickers was the main factor affecting the picking efficiency. Finally, using the simulation platform (FlexSim), we demonstrate that the order picking efficiency can be improved by about 23\%, incorporating the picker's behavior factors into pick assignments.

关键词

数据驱动 / 拣选效率 / 行为因素 / 生存分析

Key words

data driven / picking efficiency / human behavior / survival analysis

引用本文

导出引用
吴志樵 , 兰永恒 , 秦恒乐. 无人仓订单拣选效率影响因素分析. 系统工程理论与实践, 2023, 43(4): 1192-1202 https://doi.org/10.12011/SETP2022-2080
WU Zhiqiao , LAN Yongheng , QIN Hengle. The impact of picker’s factors on the order picking efficiency in unmanned warehouse. Systems Engineering - Theory & Practice, 2023, 43(4): 1192-1202 https://doi.org/10.12011/SETP2022-2080
中图分类号: C93   

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基金

国家自然科学基金(72172027);大连市科技人才创新支持计划(2022RG17)
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