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基于移动机器人的拣货系统研究进展

徐翔斌 马中强

徐翔斌, 马中强. 基于移动机器人的拣货系统研究进展. 自动化学报, 2020, 46(x): 1−20 doi: 10.16383/j.aas.c190728
引用本文: 徐翔斌, 马中强. 基于移动机器人的拣货系统研究进展. 自动化学报, 2020, 46(x): 1−20 doi: 10.16383/j.aas.c190728
Xu Xiang-Bin, Ma Zhong-Qiang. Robotic mobile fulfillment systems: state-of-the-art and prospects. Acta Automatica Sinica, 2020, 46(x): 1−20 doi: 10.16383/j.aas.c190728
Citation: Xu Xiang-Bin, Ma Zhong-Qiang. Robotic mobile fulfillment systems: state-of-the-art and prospects. Acta Automatica Sinica, 2020, 46(x): 1−20 doi: 10.16383/j.aas.c190728

基于移动机器人的拣货系统研究进展

doi: 10.16383/j.aas.c190728
基金项目: 国家自然科学基金(71761013), 江西省自然科学基金面上项目(20181BAB201010)资助
详细信息
    作者简介:

    徐翔斌:华东交通大学交通运输与物流学院教授.2015年获中南大学工学博士学位, 主要研究方向为物流与供应链管理, 本文通信作者. E-mail: champagnewq@aliyun.com

    马中强:中南大学交通运输工程学院博士研究生. 主要研究方向仓库拣货优化, 智能优化算法. E-mail: mzq11302@163.com

Robotic Mobile Fulfillment Systems: State-of-the-art and Prospects

Funds: Supported by National Natural Science Foundation of China (71761013), General Program of Natural Science Foundation of Jiangxi Province (20181BAB201010)
  • 摘要: 基于移动机器人的拣货系统(Robotic Mobile Fulfillment Systems, RMFS)作为一种新型物至人的拣货系统, 相比人工拣货系统和AS/RS拣货系统(下文统称传统拣货系统)具有更高的拣货效率、更好的系统可扩展性和柔性. 为全面了解RMFS的运行模式及其优化方向, 本文首先回顾了RMFS的工作流程及优化理论框架, 然后对RMFS的货位指派、订单分批、任务分配、路径规划以及建模方法等问题进行了文献回顾和总结, 并指出了RMFS与传统拣货系统在拣货过程方面的异同及当前研究的不足. 最后, 讨论了RMFS的几个重要研究方向, 为RMFS的理论研究和应用实践提供参考.
  • 图  1  RMFS拣货区域布局图

    Fig.  1  RMFS picking area layout

    图  2  RMFS订单拣选作业流程

    Fig.  2  RMFS order picking process

    图  3  RMFS拣货优化流程

    Fig.  3  RMFS picking optimization process

    图  4  RMFS优化理论框架

    Fig.  4  RMFS optimization theory framework

    图  5  RMFS货位指派示意图

    Fig.  5  RMFS location assignment diagram

    图  6  RMFS订单分批示意图

    Fig.  6  RMFS order batching diagram

    图  7  RMFS的货架与机器人分配示意图

    Fig.  7  RMFS shelf and robot distribution diagram

    图  8  RMFS的路径规划示意图

    Fig.  8  RMFS path planning diagram

    图  9  RMFS绩效评估的半开放排队网络模型

    Fig.  9  Semi-open queueing network for performance estimation of RMFS

    图  10  RMFS路径规划及其图表示

    Fig.  10  RMFS path planning and its graph representation

    图  11  基于强化学习的RMFS优化框架

    Fig.  11  RMFS optimization framework based on Reinforcement Learning

    图  12  基于Agent的RMFS多机器人运作结构

    Fig.  12  Agent-based multi-robot operation structure of RMFS

    表  1  RMFS研究文献汇总

    Table  1  Summary of literature on RMFS

    问题分类 作者 研究问题 解决方法
    货位指派 Nigam et al.[4](2014) 货架储位指派问题 多类封闭排队网络
    Lamballais et al.[3](2017) 仓库布局、商品储位指派、补货作业优化问题 半开放排队网络
    Onal et al.[15](2017) 商品储位指派问题 爆炸存储策略、仿真方法
    Krenzler et al.[16](2018) 货架储位再指派问题 确定性模型、组合优化算法
    Yuan et al.[17](2018) 货架储位指派问题 流体模型、基于策略的存储方法
    Weidinger et al.[18](2018) 货架储位动态指派 混合整数规划模型、自适应规划方法
    Yuan et al.[19](2018) 货位指派问题 分区存储策略、仿真方法
    Xiang et al.[20](2018) 商品储位指派问题与订单分批协同优化 混合整数规划模型、可变邻域搜索方法、自适应算法
    蔺一帅等[21](2020) 商品储位指派与路径规划协同优化 改进的协同优化遗传算法
    徐翔斌等[22](2020) 货架储位动态指派 改进的模拟退火算法
    订单分批 吴颖颖等[23](2016) 订单排序问题 订单排序优化模型、k-means聚类算法
    Boysen et al.[24](2017) 订单分批与订单排序以及货架在拣货站台排序的综合优化 混合整数规划模型、Cplex以及仿真方法
    Xiang et al.[20](2018) 商品储位指派问题与订单分批协同优化 混合整数规划模型、可变邻域搜索方法、自适应算法
    任务分配及调度 Zhou et al.[11](2014) 多机器人任务分配问题 平衡启发式机制与仿真
    Dou et al.[25](2015) 任务调度和路径规划协同优化 遗传算法、强化学习
    徐贤浩等[26](2016) 搬运机器人待命泊位策略问题 统计建模方法、基于策略的方法
    Yuan et al.[27](2017) 搬运机器人共享分配问题 共享协议策略、排队网络
    Zou et al.[28](2017) RMFS分配规则问题 半开放排队网络、基于规则的方法、邻域搜索方法
    Merschformann et al.[12] (2017) RMFS作业调度决策问题 基于行走策略的研究方法
    Merschformann et al.[29](2018) RMFS作业调度决策问题 基于策略的存储和仿真方法
    Ghassemi et al.[30](2018) 多机器人任务分配问题 基于二部图匹配和模糊聚类的分散多主体任务分配算法、仿真
    Zou et al.[31](2018) 评估机器人充电与更换电池策略的优劣 半开放排队网络、Arena仿真
    袁瑞萍等[32](2018) 拣货过程任务调度 共同进化遗传算法
    Roy et al.[33](2019) RMFS系统绩效评估、机器人分配策略 封闭排队网络、两阶段随机模型、Arena仿真
    Yoshitake et al.[34](2019) 机器人调度 实时全息调度方法
    Zhang et al.[35](2019) RMFS多机器人分配问题 改进的遗传算法
    路径规划 沈博闻等[8](2014) 多机器人路径规划问题 改进的A*算法
    Dou et al.[25](2015) 任务调度和路径规划协同优化 遗传算法、强化学习
    Kumar et al.[36](2018) RMFS路径规划问题 无冲突路径规划算法
    Zheng et al.[37](2018) 多机器人无冲突路径规划 改进的Dijkstra算法、避碰规则
    张丹霞等[38](2018) 多机器人协同路径规划 改进的A*算法、动态加权图
    夏清松等[39](2019) 路径规划与作业避障协同研究 蚁群算法、避障规则设计
    Lee et al.[40](2019) 多机器人无冲突路径规划 网络物理系统模型、改进的A*算法以及避碰规则
    于赫年等[41](2020) 多机器人路径规划问题 自调优A*算法、主动避障规则
    蔺一帅等[21](2020) 商品储位指派与路径规划协同优化 改进的协同优化遗传算法
    RMFS系统设计、
    评估及其它问题研究
    Gue et al.[42](2014) RMFS机器人系统控制与评估 面向对象建模与仿真
    Yuan et al.[43](2016) 评估RMFS的性能, 主要关注机器人数量、速度优化 开放排队网络模型
    Lee et al.[44](2018) 变形RMFS的拣货流程优化 混合整数规划模型、Gurobi
    Bozer et al.[45](2018) RMFS系统与miniload系统对比 仿真方法
    Wang et al.[46](2019) 机器人搬运货架的运行周期问题 旅行时间模型
    Zhang et al.[47](2019) RMFS快递分拣仓库布局自动化设计 机器学习与进化计算组合的方法
    Petković et al.[48](2019) RMFS工作人员的意图评估 隐马尔科夫模型和心理理论
    Wang et al.[49](2020) RMFS系统设计框架研究 基于瓶颈的模型和开放排队网络模型
    下载: 导出CSV
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