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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.Abstract: As a new kind of parts-to-pickers order picking systems, robotic mobile fulfillment systems (RMFS) have higher picking efficiency, better system scalability and flexibility compared with manual picking systems and AS/RS picking systems (hereinafter referred to as traditional picking systems). In order to fully understand the operation mode and optimization direction of RMFS, this paper first reviews the workflow and optimization theoretical framework of RMFS, then reviews and summarizes the problems of storage location assignment, order batching, task assignment, path planning and modeling methods of RMFS, as well as points out the similarities and differences between RMFS and traditional picking systems in the picking process and the shortcomings of current research. Finally, several important research directions of RMFS are discussed, which provides reference for the theoretical research and application practice of RMFS.
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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 11 基于强化学习的RMFS优化框架
Fig. 11 RMFS optimization framework based on Reinforcement Learning
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 等[4] (2014) 货架储位指派问题 多类封闭排队网络 Lamballais 等[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 等[15] (2017) 商品储位指派问题 爆炸存储策略、仿真方法 Krenzler 等[16] (2018) 货架储位再指派问题 确定性模型、组合优化算法 Yuan 等[17] (2019) 货架储位指派问题 流体模型、基于策略的存储方法 Weidinger 等[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 等[19] (2018) 货位指派问题 分区存储策略、仿真方法 Xiang 等[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] (2021) 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 等[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 等[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 等[11] (2020) 多机器人任务分配问题 平衡启发式机制与仿真 Dou 等[25] (2020) 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 等[27] (2017) 搬运机器人共享分配问题 共享协议策略、排队网络 Zou 等[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 等[12] (2018) RMFS作业调度决策问题 基于行走策略的研究方法 Merschformann 等[29] (2018) RMFS作业调度决策问题 基于策略的存储和仿真方法 Ghassemi 等[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 等[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 等[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 等[34] (2019) 机器人调度 实时全息调度方法 Zhang 等[35] (2020) 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 等[25] (2020) 任务调度和路径规划协同优化 遗传算法、强化学习 Kumar 等[36] (2018) RMFS路径规划问题 无冲突路径规划算法 Zhang 等[37] (2018) 多机器人无冲突路径规划 改进的Dijkstra算法、避碰规则 张丹露等[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 等[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、评估及其他问题研究 Gue 等[42] (2014) RMFS机器人系统控制与评估 面向对象建模与仿真 Yuan 等[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 等[44] (2019) 变形RMFS的拣货流程优化 混合整数规划模型、Gurobi Bozer 等[45] (2018) RMFS系统与miniload系统对比 仿真方法 Wang 等[46] (2020) 机器人搬运货架的运行周期问题 旅行时间模型 Zhang 等[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 等[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 等[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 
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