系统仿真学报 ›› 2025, Vol. 37 ›› Issue (10): 2613-2629.doi: 10.16182/j.issn1004731x.joss.24-0511

• 论文 • 上一篇    

考虑作业平衡及学习效应的接力拣选系统动态订单调度

刘微宏1, 赵思翔1,2, 张大力1,2, 蒋振辉3   

  1. 1.上海交通大学 安泰经管学院中美物流研究院,上海 200030
    2.上海交通大学 数字化管理决策部哲学社会科学实验室,上海 200030
    3.上海发网供应链管理有限公司,上海 200443
  • 收稿日期:2024-05-11 修回日期:2024-06-18 出版日期:2025-10-20 发布日期:2025-10-21
  • 通讯作者: 赵思翔
  • 第一作者简介:刘微宏(1999-),女,硕士生,研究方向为物流与供应链优化。
  • 基金资助:
    国家自然科学基金重大研究计划(72192822);国家自然科学基金(72071128);国家自然科学基金(72001141);上海交通大学行业研究院资助项目(YY2131)

Dynamic Order Scheduling for Pick-and-pass System Considering Workload Balance and Learning Effects

Liu Weihong1, Zhao Sixiang1,2, Zhang Dali1,2, Jiang Zhenhui3   

  1. 1.Sino-US Global Logistics Institute, Antai College of Economics & Management, Shanghai Jiao Tong University, Shanghai 200030, China
    2.Data-Driven Management Decision Making Lab, Shanghai Jiao Tong University, Shanghai 200030, China
    3.Shanghai FineEx Logistics Co. , Ltd. , Shanghai 200443, China
  • Received:2024-05-11 Revised:2024-06-18 Online:2025-10-20 Published:2025-10-21
  • Contact: Zhao Sixiang

摘要:

在电商物流领域,混联结构接力拣选系统兼具复杂性与灵活性,能够适应多样化的订单拣选场景,因而得到广泛应用。然而,该系统也使得订单调度问题更为复杂,尤其在需要同时考虑作业平衡与拣选人员学习效应的情况下,如何实现高效订单调度并缩短拣选时间,成为一项重要挑战。针对订单已知的静态调度问题,本研究构建相应的数学模型,基于该模型进一步建立混联拣选分区的仿真模型,并提出一种结合不同系统特征变量的调度规则采用启发式算法对规则中的核心参数进行优化,以实现动态订单调度。基于实际仓储数据的仿真结果表明,所提方法在调度效果和求解速度上均优于传统调度规则及经典启发式方法,可为自动化仓储系统实时调度决策提供有效支持。

关键词: 混联接力拣选系统, 动态订单调度, 订单排序, 作业平衡, 学习效应

Abstract:

In e-commerce logistics, the hybrid pick-and-pass systems offer both complexity and flexibility, enabling adaptation to a wider range of order picking scenarios. Therefore, they have been widely used. However, this also complicates the order scheduling problem, particularly when both workload balance and pickers' learning effects need to be considered. Efficiently scheduling orders to reduce picking time under these conditions poses a significant challenge. This study began by constructing a mathematical model for the static scheduling problem with known orders. Based on this model, a simulation model of hybrid pick-and-pass zones was developed, and a scheduling rule incorporating multiple system feature variables was proposed. The heuristic algorithm was employed to optimize the core parameters in the rules and achieve dynamic order scheduling. Simulation results based on real warehouse data demonstrate that the proposed method outperforms conventional scheduling rules and classical heuristic methods in both scheduling performance and computational efficiency, providing effective support for real-time decision-making in automated warehouse systems.

Key words: hybrid pick-and-pass system, dynamic order scheduling, order sequencing, workload balance, learning effect

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