摘要
移动货架仓库因其高效灵活的拣选优势而被广泛应用。然而,订单的持续增长促使大量机器人同时作业,导致仓库的巷道拥堵问题日益凸显,严重影响了其拣选效率和整体运营。为此,本文提出基于巷道均衡的货架选择优化方法,使所选择的货架既能满足订单拣选需求又能更加均衡地使用各个巷道,从而有效缓解巷道拥堵。按此方法,本文引入巷道均衡系数,以减少货架移动次数为目标,构建货架选择问题的混合整数规划模型;并通过建立拣货站任务分配模型和基于滚动时域的机器人调度策略,实现对仓库拣货作业关键指标的计算和对本文货架选择方法的科学评价。实验表明,基于巷道均衡的货架选择方法使巷道冲突节点减少了28.35%,机器人行驶时间缩短了22.54%,证明了本文方法的科学性和有效性。灵敏度分析发现巷道均衡系数设置在10%~20%范围内可以较好地平衡拣货效率和机器人效率。本文研究对提升复杂系统决策的科学性具有一定的参考价值。
Mobile-rack warehouses are widely adopted for their efficient and flexible picking capabilities.However,the continuous growth of orders has led to the simultaneous operation of a large number of robots,resulting in increasingly prominent aisle congestion issues that severely impact picking efficiency and overall operations.To address this challenge,this paper proposes an optimization method for rack selection based on aisle balancing.The method ensures that selected racks evenly distribute the use of aisles while meeting order picking requirements,significantly reducing congestion.To address this challenge,this paper proposes an optimization method for rack selection based on aisle balancing.The method ensures that selected racks evenly distribute the use of aisles while meeting order picking requirements,significantly reducing congestion.To support this method,an aisle equilibrium coefficient is introduced,and a mixed-integer programming model is developed to optimize rack selection,with the goal of minimizing rack movements.Additionally,a picking station assignment model and a rolling horizon-based robot scheduling strategy are constructed to compute key indicators of warehouse picking operations,enabling a thorough evaluation of the proposed rack selection approach.Experimental validation utilizes real-world data from a large e-commerce warehouse,involving 24 test instances with varying order sizes(50~1200 orders).Experimental results show that the method reduces aisle conflict points by 28.35%and shortens robot travel time by 22.54%,validating the scientific rigor and effectiveness of the approach.The paper makes two key contributions:(1)It introduces aisle balancing into the rack selection phase,optimizing both rack selection and aisle resource allocation to reduce congestion in mobile-rack warehouses.(2)It proposes an integrated optimization-evaluation framework that links theoretical optimization with practical validation,creating a closed-loop for complex warehouse system optimization.This framework also offers a generalized solution for resource allocation and task scheduling in systems with multi-objective constraints and real-time requirements.Managerial insights include:(1)Proactively incorporating aisle balancing into rack selection enables synergy between short-term operational efficiency(e.g.,immediate productivity)and long-term strategic goals(e.g.,sustainability);(2)The correlation between rack movements and core efficiency metrics enables enterprises to align subsystem objectives with global efficiency in complex challenges.(3)Aisle balancing is identified as a key constraint for congestion mitigation,with enterprises advised to set a 10%~20%aisle balancing coefficient to balance picking efficiency and robot utilization.
作者
翟梦月
王征
何家宁
张睿珊
秦崟华
ZHAI Mengyue;WANG Zheng;HE Jianing;ZHANG Ruishan;QIN Yinhua(School of Maritime Economics and Management,Dalian Maritime University,Dalian 116025,China)
出处
《工程管理科技前沿》
北大核心
2025年第5期35-43,共9页
Frontiers of Science and Technology of Engineering Management
基金
国家自然科学基金资助项目(71971036)。
关键词
移动货架仓库
巷道拥堵
货架选择优化
混合整数规划
滚动时域
机器人效率
mobile-rack warehouses
aisle congestion
optimizing rack selection
mixed-integer programming
rolling horizon
robot utilization
