B2C电子商务仓库拣货路径优化策略应用研究

当前国内B2C电子商务仓库多为人至物的拣货模式,拣货作业成为其核心作业之一,占据仓库大量时间成本和资金成本,拣货路径优化成为企业亟需解决的问题。本文基于TSP对拣货路径进行建模,利用蚁群算法、模拟退火算法和禁忌搜索对该NP-hard问题进行求解,并同当前企业普遍采用的S型启发式策略进行对比,拣货时间节约13.35%。进一步得出当拣货品数量较少时应采用模拟退火算法求解,而当拣货品数量较大时采用蚁群算法仅进行一次迭代,则可以实现短时间得到相对较优的解。所得结果已应用于某大型电子商务企业,效果明显。     

考虑车载容量的多区型仓库拣货路径优化研究

拣货作业是仓库核心作业之一,占据仓库运营大量的时间成本和资金成本.针对多区型仓库拣货路径优化问题,对多区型仓库布局、货位坐标、路径等问题进行了定义,构建了多区型仓库拣货路径优化建模,接着通过大量实验确定了人工鱼群算法在求解拣货路径问题时的最优算法参数组合,通过演示性实验验证了模型与算法的有效性,最后从波次订单对实验结果的影响、车载容量对实验结果的影响和算法对比分析3个方面验证了人工鱼群算法的实用性和优越性.结果表明,所建立的多区型仓库拣货路径优化的模型及其求解方法,能够有效提高仓储拣货作业效率.     

基于移动机器人的拣选系统货架动态储位分配研究

为了提高基于移动机器人的拣选系统拣货效率,更好地满足客户动态需求和订单时效要求,提出了考虑货架后续需求频次、需求紧迫程度以及拥堵因素的货架动态储位分配策略,构建了最小化货架搬运距离的动态储位分配模型,并设计了启发式算法进行模型求解.首先,基于货架需求紧迫程度,构造贪婪算法生成动态货架储位分配的初始解;然后,基于货架在后续批次订单的需求频次及通道间负载均衡,采用邻域搜索算法进行动态货架储位优化.最后,通过与其他静态和动态储位分配方法对比,验证文章提出的模型和算法的有效性.     

优化配送中心订单拣取路径的一种动态规划方法

订单拣取过程是配送中心最耗时耗力的作业环节，因此提高拣货作业效率成了大多数配送中心努力的方向。而优化拣货路径则是提高拣货作业效率的有效措施之一，所以本在传统拣货规则的基础上，提出了一种采用动态规划方法优化订单拣取路径的方法。该方法是优化配送中心订单掠取路径的一种新的思路和方法，且其确定的拣货路径是在既定规则下的最优拣货路径。针对不同的拣货单采用不同的拣货路径，能有效缩短拣货行走的距离，对提高配送中心的拣货效率具有现实意义。     

蔬菜B2C直销中基于批配送模式的拣货方案序贯求解方法

针对蔬果类商品B2C直销模式下拣货与配送环节拣货量大、订单个性化强、时间性强及批配送等特点,基于相似订单成组拣货这一现实需求,引入成组作业思想,建立最小化拣货成本和配送成本之和的成批成组拣货序列优化模型;针对该模型多阶段决策、多决策变量及NP-难等特点,以降低求解维度和减少求解时间为目标,基于逆序决策思想,提出序贯求解方法,并给出了客户成批聚类、批次内相似订单成组及成批成组拣货序列优化求解算法;通过应用实例验证本文模型和算法的可行性和有效性。研究结果表明,本文方法得到的方案比成组拣货与配送独立决策,以及批配送但非成组方法大大缩减了拣货时间,为蔬果类商品网上直销企业生成拣货作业计划提供理论指导。     

基于改进粒子群算法V型非传统布局仓库通道优化设计

V型仓储布局是一种典型的非传统布局方式,针对V型布局主通道设计的问题,将主通道抽象为若干个点连接而成的折线通道,每条拣货通道按物动量大小对仓库进行分区,采用更加符合实际的存取货物作业的概率不相等的非完全随机存储策略,建立最小化平均拣货距离的仓库主通道设计数学优化模型。其次,设计了基于极值扰动算子的改进粒子群优化算法(EDO-PSO)进行算法求解,利用极值扰动算子解决易陷入局部最优问题,采用并行深度搜索策略,提高算法性能,并用Benchmark函数与其他改进PSO算法对比验证算法性能。最后,结合具体实验数据仿真分析,计算结果表明,该方法在相同货位分配策略下,能有效缩短总拣货距离,验证了方法的有效性。     

基于多候选储位的存取路径优化问题研究

针对单储位储存方式可能导致仓库存取通道拥挤和作业效率低的情形,提出了一种基于多候选储位的存取路径优化方法。首先分配了货物的存取储位,然后建立了多候选储位的车辆路径问题(MLVRP)模型,并基于储位优先解码原则设计了遗传算法,最后通过算例证明该方法的有效性和算法的高效性。多候选储位的方法可以为取货任务至少节约18.4%(两个候选储位)和21.8%(三个候选储位)的路程,算法迭代10000次只需要434s。     

仓储配送中ABC管理的优化问题及其实证

ABC管理是80／20原则在仓储管理中的一种应用，能有效提高企业效益。目前的研究很少分析ABC管理的改善程度、各种应用策略间的影响和整体作用，而且国内仓储运作和国外存在较大的差异。本文基于一种结合国内仓储实践、具有普遍性和实用性的ABC管理模式，建立ABC管理对作业效率改善程度的测度模型。通过对模型假设的实证和模型分析表明：仓储配送中ABC管理的实质是对劳动时间这个可变资源进行重点管理，利用储位分配策略缩短部分订单的拣货路径以减少订单拣货时间，利用库存控制和订货补充策略提高优化作业订单所占的比重，共同作用提高作业效率。最后讨论了ABC管理在国内大规模推广的原因。     

基于偏离度的仓储中心拣货路径优化方法研究

针对现代仓储作业中广泛使用的双区仓库,为提高拣选作业的质量和效率,首先应用RFID技术对仓储作业中货物的入库、定位、拣选、出库等进行自动化识别,实现管理数据库的实时更新,减少订单中货物搜索时间.在此基础上,提出了一种基于偏离度的路径优化方法,通过与传统穿越策略、S型启发式算法进行仿真对比.结果表明,在双区仓库的路径拣选中,基于偏离度方法对仓库作业优化效果显著.     

基于“货到人”拣选模式的储位分配问题研究

研究了“货到人”拣选模式下的储位分配问题,以订单拣选过程中搬运货架总时间最短为目标建立了整数非线性规划模型,并证明其为NP-hard问题,分别设计了求解模型的贪婪算法和单亲进化遗传算法。首先根据订单和物品的关联关系对物品进行聚类,基于聚类结果设计了求解模型的贪婪算法。然后设计了直接求解模型的单亲进化遗传算法,遗传算法中采用了0-1矩阵编码、多点基因倒位算子、单点基因突变算子和精英保留等策略,通过合理选取参数,能够很快求解出问题的近似最优解。最后利用模拟算例和一个具体实例进行计算,并对贪婪算法和遗传算法的求解时间和求解效果进行了比较分析。结果显示,对于小规模问题,两种算法均能在较短的时间内以很高的概率得到问题的全局最优解,对于中等规模的实际问题,利用两种算法得到的储位分配方案均优于企业目前采取的基于出库频率的储位分配方案,遗传算法得到的储位分配方案对应的货架搬运次数、货架搬运总时间等均优于贪婪算法。本文设计的遗传算法可以作为智能仓库管理信息系统的核心算法。     

Design and control of warehouse order picking: A literature review

Order picking has long been identified as the most labour-intensive and costly activity for almost every warehouse; the cost of order picking is estimated to be as much as 55% of the total warehouse operating expense. Any underperformance in order picking can lead to unsatisfactory service and high operational cost for the warehouse, and consequently for the whole supply chain. In order to operate efficiently, the order-picking process needs to be robustly designed and optimally controlled. This paper gives a literature overview on typical decision problems in design and control of manual order-picking processes. We focus on optimal (internal) layout design, storage assignment methods, routing methods, order batching and zoning. The research in this area has grown rapidly recently. Still, combinations of the above areas have hardly been explored. Order-picking system developments in practice lead to promising new research directions.     

A fast simulated annealing method for batching precedence-constrained customer orders in a warehouse

Batching customer orders in a warehouse can result in considerable savings in order pickers’ travel distances. Many picker-to-parts warehouses have precedence constraints in picking a customer order. In this paper a joint order-batching and picker routing method is introduced to solve this combined precedence-constrained routing and order-batching problem. It consists of two sub-algorithms: an optimal A^∗-algorithm for the routing; and a simulated annealing algorithm for the batching which estimates the savings gained from batching more than two customer orders to avoid unnecessary routing. For batches of three customer orders, the introduced algorithm produces results with an error of less than 1.2% compared to the optimal solution. It also compares well to other heuristics from literature. A data set from a large Finnish order picking warehouse is rerouted and rebatched resulting in savings of over 5000 kilometres or 16% in travel distance in 3 months compared to the current method.     

Routing order pickers in a warehouse with a middle aisle

This paper considers a parallel aisle warehouse, where order pickers can change aisles at the ends of every aisle and also at a cross aisle halfway along the aisles. An algorithm is presented that can find shortest order picking tours in this type of warehouses. The algorithm is applicable in warehouse situations with up to three aisle changing possibilities. Average tour length is compared for warehouses with and without a middle aisle. It appears that in many cases the average order picking time can be decreased significantly by adding a middle aisle to the layout.     

Exact results for the order picking time distribution under return routing

This paper derives exact expressions for the Laplace-Stieltjes transform of the order picking time in single- and 2-block warehouses. We consider manual warehouses that deploy return routing and assume that order sizes follow a Poisson distribution. The results in this paper apply to a wide range of storage policies, including but not restricted to class-based and random storage. Furthermore, we compare the performance of the storage policies and warehouse lay-outs by using numerical inversion of the Laplace-Stieltjes transforms.     

A travel time estimation model for a high-level picker-to-part system with class-based storage policies

Most previous related studies on warehouse configurations and operations only investigated single-level storage rack systems where the height of storage racks and the vertical movement of the picking operations are both not considered. However, in order to utilize the space efficiently, high-level storage systems are often used in warehouses in practice. This paper presents a travel time estimation model for a high-level picker-to-part system with the considerations of class-based storage policy and various routing policies. The results indicate that the proposed model appears to be sufficiently accurate for practical purposes. Furthermore, the effects of storage and routing policies on the travel time and the optimal warehouse layout are discussed in the paper.     

考虑完全拆分的拣选分批与拣选路径集成优化模型

在电商海量订单背景下,在线订单拣选作业难度加大,因此设计了基于订单完全拆分的拣选分批与拣选路径综合优化模型解决此问题.模型共分两阶段.第一阶段,基于种子算法,设计考虑订单完成度、等待时间与拣选路径的拣选分批模型;第二阶段以拣选单流为单队列,设计多拣选员并行服务的拣选系统.行走策略为基于返回型和遍历型的综合策略,拣选路径优化模型采用模拟退火算法求解.算例分析表明,与传统的不拆分拣选分批模型相比,构建的综合优化模型能够显著提高拣选系统效率.拣选员为4人时,模型能够使总服务时间减少58.79%,订单完成率提高10.09%.     

A branch and bound algorithm for class based storage location assignment

Class-based storage implementation decisions have significant impact on the required storage space and the material handling cost in a warehouse. In this paper, a nonlinear integer programming model is proposed to capture the above. Effects of storage area reduction on order picking and storage space cost are incorporated. A branch and bound algorithm is developed to solve the model. Computational experience with randomly generated data sets and an industrial case shows that branch and bound algorithm is computationally more efficient than a baseline dynamic programming algorithm. It is further observed that the class based policy results in lower total cost of order picking and storage space than the dedicated policy.     

Travel time estimation and order batching in a 2-block warehouse

Order batching problem (OBP) is the problem of determining the number of orders to be picked together in one picking tour. Although various objectives may arise in practice, minimizing the average throughput time of a random order is a common concern. In this paper, we consider the OBP for a 2-block rectangular warehouse with the assumptions that orders arrive according to a Poisson process and the method used for routing the order-pickers is the well-known S-shape heuristic. We first elaborate on the first and second moment of the order-picker’s travel time. Then we use these moments to estimate the average throughput time of a random order. This enables us to estimate the optimal picking batch size. Results from simulation show that the method provides a high accuracy level. Furthermore, the method is rather simple and can be easily applied in practice.