基于“货到人”拣选系统的动态货位分配研究

目的 为了提高“货到人”拣选系统的订单拣选效率，研究了电商仓库货位初始存储状态为非空情况下的商品货位分配问题。方法 考虑货架上现存商品信息、仓库空余货位数、待补货商品和新收入商品信息，提出基于商品关联度的分散存储策略，以极大化货架上所有存储商品的关联度之和为目标构建商品上架与下架的动态货位分配数学模型，并设计贪婪算法，再采用改进粒子群算法对贪婪算法得到的结果进行优化。结果 基于商品关联度的分散存储策略可以减少货架平均搬运次数29.32%左右。结论 与随机分配策略相比，文中提出的货位分配策略能够有效提升整个电商仓储中心的拣选速度。     

一种考虑货位共享效应的Fishbone仓储布局优化方法

以Fishbone仓储布局为基础,针对3种经典存储策略,考虑货位共享效应,以单程平均货物拣选距离最短为目标,建立Fishbone布局仓库设计优化模型,以探讨将货位共享效应考虑在内时,不同存储策略对仓储布局的影响;采用分次逼近策略和动态规划算法确定货物的优化分类及类别边界;设计基于自适应的遗传算法获得最优存储分类下的仓储布局参数,并结合案例实验数据进行仿真分析。结果表明,基于分类存储策略的仓储布局表现优异,所需货位数量少且货物拣选距离短;另外,仓储存储物品的需求差异越大,考虑货位共享效应的优势就越显著,最高可减少37.1%的货物拣选距离。     

Auto Store仓储系统的运作与效率优化研究

目的 为缓解零售电商商品仓库占地面积广,拣选效率受限等问题。方法 文中就存储策略、指派策略以及路径策略方面对Auto Store仓储系统进行详尽的介绍,在此基础上流程化的分析Auto Store系统完成单次订单拣货作业的业务流程,并运用Anylogic软件对所提出的模型进行仿真和验证。结果 假定订单到达服从Erlang分布,在拣选车和工作站数量和拣选货物数量相同的情况下,对比了Auto Store仓储系统混存布局和传统布局的拣选效率,验证了混存布局的可行性。结论 同时对比基于2种任务指派策略,得出了以基于拣选时间最小化的指派策略下系统运作效率更优这一结论,对理论分析与仿真研究之间的结果进行分析比较,验证了模型的有效性。     

基于遗传算法的分区拣选系统储位优化实证研究

目的研究零售连锁企业区域配送中心分区拣选系统下的储位优化方法。方法以某药品连锁零售企业区域配送中心为研究对象,将不同分区的拣选时间趋于均衡作为储位优化目标,结合样本订单与历史订单的特点,建立储位优化数学模型,并采用遗传算法进行求解,以获取约束条件内该模型的最优解。结果在样本订单占80%权重的前提下,将所求得的分区与储位的解所对应的拣选时间,与研究对象的拣选现状进行对比,发现采用均衡各区拣选时间模型的求解结果提高了37.34%的效率,优化效果显著。结论实证研究证明了该模型和算法的有效性,该优化方法也广泛适用于其他分区拣选系统的储位分配作业。     

现代仓储中心存储布局的优化研究

仓储中心的货架摆放布局是影响拣选作业效率的重要因素之一,为研究货架摆放布局对有效存储面积的影响,以鱼骨布局为研究对象,构建其有效存储面积利用率模型,并对其进行仿真验证。结果表明,在鱼骨布局设计的仓库中,主拣选通道角度一定时,拣选通道宽度与货架宽度比例越大,有效存储面积利用率越小;拣选通道宽度与货架宽度比例一定时,主拣选通道角度越趋近于0度或者90度时,有效存储面积利用率越大。由此,鱼骨布局等改进型仓储布局为决策拣选路径提供了新的思路,也从理论上证明了改进型仓储布局实践应用的可行性。     

考虑并行分区的智能接力拣选系统储位分配问题研究

本文研究了一类考虑并行分区的智能接力拣选系统的储位分配问题,建立了多目标混合整数规划模型,旨在最大化订单拣选量的同时使各分区的工作负荷均衡,从而降低拣选时间。针对问题的特点,设计了求解该类问题的两阶段优化算法。经过对某第三方仓储企业进行实例研究,数值结果表明设计的算法可以有效求解本文的储位分配问题。     

基于二进制粒群的拣选作业优化

目的提高电子标签拣选系统中拣选作业的效率与货位占有率。方法以某电子拣选库为研究对象,提出以订单完成度、货位占有率以及货位聚集度为目标的拣选优化模型。设计基于二进制粒子群算法(BPSO)和遗传算法(GA)的模型求解仿生算法。结果试验及优化结果表明,基于BPSO的电子拣选库订单的完成度、货位占有率以及货位聚集度较遗传算法更高。结论基于二进制粒子群算法求解的优化模型较符合实际的电子拣选库人工拣选作业,同时仓储作业货位的利用率及拣选效率得到了提高。     

考虑排产约束的汽车零部件出库订单排序优化北大核心CSSCI

针对TPL仓库面向汽车零部件企业的产前物料拣选和配送作业环节,研究了JIT生产模式下配送送达时间限定和生产线缓冲库容量有限条件下的出库作业拣选订单排序的优化模型及算法。在分析现有拣选订单排序模式的基础上,提出了融合现有模式优点的新作业模式,建立了该模式下拣选订单排序优化模型,并针对这一整数组合优化模型,设计了一套基于贪心理论的算法对其进行求解。通过实际案例分析表明,该模型及算法可以达到减少拣选作业空余时间、提高作业效率、降低作业成本的目的。     

考虑排产约束的汽车零部件出库订单排序优化

针对TPL仓库面向汽车零部件企业的产前物料拣选和配送作业环节,研究了JIT生产模式下配送送达时间限定和生产线缓冲库容量有限条件下的出库作业拣选订单排序的优化模型及算法。在分析现有拣选订单排序模式的基础上,提出了融合现有模式优点的新作业模式,建立了该模式下拣选订单排序优化模型,并针对这一整数组合优化模型,设计了一套基于贪心理论的算法对其进行求解。通过实际案例分析表明,该模型及算法可以达到减少拣选作业空余时间、提高作业效率、降低作业成本的目的。     

新型无人仓AutoStore的货物合箱方法研究

目的 为减少AutoStore系统在共同存储策略下的订单处理时间,解决经营品类繁多,但仓储面积有限的企业货物存储问题。方法 提出货物合箱的存储方法,设计关联规则挖掘算法与混沌种子优化算法的混合优化算法进行求解。从机器人翻箱操作、料箱的分配规则、系统布局三方面进行分析,结合AutoStore运作规律,建立以机器人拣货行走距离最短为目标函数的数学模型,通过Apriori算法输出货物热度,输入混沌种子算法,得到储位分配结果,实现联动决策。在基本种子算法的迭代中加入混沌序列,帮助种子避开局部极小值。结果 实例分析表明,货物合箱方法能够显著减少共同存储下AutoStore系统的订单处理时间,缩小与指定存储策略订单处理时间的差距,与粒子群算法和基本种子优化算法相比,混沌种子优化算法的优化效率更高、稳定性更好。结论 文中的研究为经营品类繁多,但仓储面积有限的企业提供了一种更适合的货物存储策略,具有较强的操作性和实际意义。     

Optimal layout in low-level picker-to-part systems

The paper presents an analytical approach to layout design of the picking area in low-level, picker-to-part systems using COI (cube per order index)-based and random storage policies. The layout of the picking area is one of the major issues in increasing picking system productivity, i.e. in reducing the time required to complete a given set of orders, and must take account of the inter-relationship between the main operating policies, i.e. storage, routing and batching. The main system parameters affecting the layout design are the total length of the picking aisles, the number of pick stops per tour and the shape of the COI-based ABC curve. A formula that relates the optimal number of aisles to the above parameters will be presented, together with the increase in the expected tour distance stemming from the adoption of a non-optimal number of aisles. The study thus provides a comprehensive framework for layout design.     

Enhancing performance in order picking processes by dynamic storage systems

Increasing productivity and reducing labour cost in order picking processes are two major concerns for most warehouse managers. Particularly picker-to-parts order picking methods lead to low productivity as order pickers spend much of their time travelling along the aisles. To enhance order picking process performance, an increasing number of warehouses adopt the concept of dynamic storage where only those products needed for the current order batch are dynamically stored in the pick area, thereby reducing travel time. Other products are stored in a reserve area. We analyse the stability condition for a dynamic storage system with online order arrivals and develop a mathematical model to derive the maximum throughput a DSS can achieve and the minimum number of worker hours needed to obtain this throughput, for order picking systems with a single pick station. We discuss two applications of dynamic storage in order picking systems with multiple pick stations in series. In combination with simulation modelling, we are able to demonstrate that dynamic storage can increase throughput and reduce labour cost significantly.     

A model for warehouse layout

This paper describes an approach to determine a layout for the order picking area in warehouses, so that the average travel distance for the order pickers is minimized. We give analytical formulas that can be used to calculate the average length of an order picking route under two different routing policies. The optimal layout can be determined by using these formulas as the objective function in a nonlinear programming model. The optimal number of aisles in an order picking area appears to depend strongly on the required storage space and the pick list size.     

Routing methods for warehouses with multiple cross aisles

This paper considers routing and layout issues for parallel aisle warehouses. In such warehouses order pickers walk or drive along the aisles to pick products from storage. They can change aisles at a number of cross aisles. These cross aisles are usually located at the front and the back of the warehouse, but there can also be one or more cross aisles at positions in between. We describe a number of heuristics to determine order picking routes in a warehouse with two or more cross aisles. To analyse the performance of the heuristics, a branch-and-bound algorithm that generates shortest order picking routes is used. Performance comparisons between heuristics and the branch-and-bound algorithm are given for various warehouse layouts and order sizes. For the majority of the instances with more than two cross aisles, a newly developed heuristic appears to perform better than the existing heuristics. Furthermore, some consequences for layout are discussed. From the results it appears that the addition of cross aisles to the warehouse layout can decrease the handling time of the orders by lowering average travel times. However, adding a large number of cross aisles may increase average travel times because the space occupied by the cross aisles has to be traversed as well.     

Heuristic routing methods in multiple-block warehouses with ultra-narrow aisles and access restriction

This paper focuses on multiple-block warehouses with ultra-narrow aisles and access restriction. These new features observed from one of the largest online retailers in China allow order pickers enter pick aisles from specific entrances but prohibit them from traversing the aisles. This impedes the application of traditional heuristic order picking methods. To address the order picking problem in such warehouses, we propose six heuristic routing methods by extending the basic Return, Largest Gap and Mid-point methods for the single-block warehouse. These six heuristic methods are named RNA, LNA, MNA, RNAP, LNAP and MNAP, respectively. The major improvements are achieved through setting rules with respect to determining the access mode of aisles as well as changing working aisles. Using real order information, a comprehensive simulation for comparison is conducted to evaluate the effectiveness of our improved routing methods under 12 warehouse layouts. The simulation results demonstrate that LNAP achieves the shortest average picking routes in most scenarios. The impacts of warehouse layout on performance measurements are analysed as well. It is ascertained that setting more cross aisles and connect aisles helps mitigate the negative impacts.     

An evaluation of routing policies for order-picking operations in low-level picker-to-part system

This paper evaluates the performance of three routing policies in the order-picking process, i.e. return, traversal, and midpoint policy. It is assumed that items are assigned to storage locations on the basis of the cube-per-order index (COI) rule in a low-level picker-to-part warehousing system. First, for the three policies, analytical models are developed for the total expected travel distance of the order picker considering the number of the stocking aisles is even or odd. Then the developed models are compared with simulation results to show the validity. Finally, the performance of the three policies is examined by varying the parameter value of the COI-based ABC curve, number of picks in the pick list, and ratio of the length to the width of the warehouse.     

An integrated storage assignment method for manual order picking warehouses considering cost,workload and posture

This paper presents an integrated storage assignment method for low-level picker-to-parts order picking warehouses taking into account economic and ergonomic objectives. Three different pallet rack layouts are studied in this paper, namely (a) picking from full pallets on the floor, (b) picking from half-pallets on the floor, and (c) picking from half-pallets on the upper rank of the shelf. First, cost functions are developed to assess the total order picking performance impact of these different pallet rack layouts. Second, with regard to workload, the metabolic cost and energy expenditure rates for picking from the different rack layouts under study are derived. Third, for assessing the working posture during order picking, the Ovako Working Posture Analysing System index is used where the required data is collected using a motion capturing system. The developed models are combined to propose a heuristic storage assignment procedure that supports the decision of which item to store on which pallet. The developed storage assignment method is then applied to an industrial case study. The results of the paper support warehouse managers in assessing the order picking storage assignment from an ergonomics viewpoint and in estimating its impact on financial order picking performance.     

The use of time series forecasting in zone order picking systems to predict order pickers’ workload

In order to differentiate from competitors in terms of customer service, warehouses accept late orders while providing delivery in a quick and timely way. This trend leads to a reduced time to pick an order. This paper introduces workload forecasting in a warehouse context, in particular a zone picking warehouse. Improved workforce planning can contribute to an effective and efficient order picking process. Most order picking publications treat demand as known in advance. As warehouses accept late orders, the assumption of a constant given demand is questioned in this paper. The objective of this study is to present time series forecasting models that perform well in a zone picking warehouse. A real-life case study demonstrates the value of applying time series forecasting models to forecast the daily number of order lines. The forecast of order lines, along with order pickers’ productivity, can be used by warehouse supervisors to determine the daily required number of order pickers, as well as the allocation of order pickers across warehouse zones. Time series are applied on an aggregated level, as well as on a disaggregated zone level. Both bottom-up and top-down approaches are evaluated in order to find the best-performing forecasting method.     

Modelling and performance evaluation of explosive storage policies in internet fulfilment warehouses

Internet Fulfilment Warehouses (IFWs) are designed to exclusively process online retail orders. An observational study reveals that IFW operating and design attributes are significantly different from traditional warehouses in their storage and fulfilment policies. Specifically, we identify six IFW differentiators: explosive storage, very large number of beehive storage locations, bins with commingled SKUs, immediate fulfilment, short picking routes with single unit picks and high transactions with total digital control. Explosive storage of incoming bulk allows for faster fulfilment of customer orders often within a few hours. A new IFW control model is developed. This describes the associated receiving and fulfilment flows. Two decision algorithms for generating (i) a stocking list and (ii) an order picking list are presented. A simulation model was built to evaluate the fulfilment performance of the explosive policy. Experimental runs on a problem with 400 SKUs, 3240 bins and 22,000 customer orders over nine days are reported. Results show that increasing levels of explosion reduce the linear fulfilment time by as much as 16%, confirming the IFW storage policy is advantageous. The results also show that fulfilment time behaviour is convex as a function of the maximum number of stops allowed by the picking algorithm parameter.