配送中心单品拣货模式及其效能分析

紧扣拣货作业的关键目标,介绍了8种低成本的单品拣货模式,并从生产力指标和人员配置两个方面对各种模式的效能进行了计算分析,能为配送中心拣货系统的设计和选择合适的单品拣货模式提供依据.     

基于Flexsim的某医药物流中心拣货流程优化研究

目的将Flexsim仿真软件应用于医药物流行业,以提高医药物流中心零货库拣货流程的运作效率。方法以某大型医药物流中心为研究对象,根据该零货库区的布局,使用Flexsim仿真软件进行建模仿真,并根据订单拣选作业流程运用Flexsim脚本语言和C语言进行了编程。分析模型运行结果,找出了瓶颈,并对拣货流程进行了优化。结果优化后的流程,提高了拣货作业的效率,缩短了拣货时间。结论采用建模仿真的方法,找出拣货流程的瓶颈问题,并进行合理优化,可以提高医药物流中心零货库的拣货效率。     

基于鱼骨型仓库布局的多车拣选路径问题优化

为了推动鱼骨型仓库在实际场景下的应用,针对鱼骨型仓库布局下的拣货路径优化问题,构建待拣货点距离计算模型和以有载重、容积限制的多车拣货距离最短为总目标的拣选路径优化模型。考虑遗传算法(GA)全局搜索能力强、粒子群算法(GAPSO)收敛速度快以及蚁群算法(ACO)较强的局部寻优能力,提出一种解决拣选路径优化模型的混合算法(GA-PSO-ACO)。通过不同订单规模的仿真实验,得出该混合算法在适应度值、迭代次数、收敛速度等方面均优于GA算法和GAPSO算法,且在订单规模较大时,平均适应度值约降低8%,有效缩短了总拣选距离,验证了混合算法在解决鱼骨型仓库布局下的拣货路径问题的先进性和有效性,为解决此类仓库内部的拣货路径问题提供新的解决方法和思路。     

物流配送中心拣货系统分析

探讨了物流中心拣货系统的重要性,分析了拣货系统的EIQ法及拣货系统的信息系统,并就拣货系统对商品包装的要求进行了分析.     

A字架物流拣货设备控制方案设计

随着我国科学技术不断地发展,在物流拣货工作中,发明了A字架物流拣货设备,其是一种制动化的拣货设备,有效解决了以往品种多,货物量少的商品拣货低效率。该设备不仅节约了拣货的时间,而且有效降低了物流企业的操作成本,提高了企业的经营利益,为其以后的发展开辟了新道路。     

基于蚁群算法的双分区仓库拣货路径的优化

为提高现代仓库作业中拣货这一核心环节的效率,研究了仓库拣货路径的优化,提出了根据双分区仓库中拣货路径的特点,采用蚁群算法优化求解的拣货路径算法,并通过仿真将该算法的性能与传统穿越策略、S形启发式算法进行了比较。比较结果表明,以蚁群算法优化路径问题可以明显减少路径的距离,具有良好的实用性。     

双区型仓库动态拣货策略的设计及路径优化研究

目的 针对双区型仓库,以拣货时间最短为目标函数构建数学模型,进一步提高拣货效率。方法 提出并设计动态货位调整与人工拣货协同作业的动态拣货策略,分别采用GA算法和GASA算法进行最优化求解。结果 GASA算法优于GA算法,拣货单为1张情况下的拣货时间可减少4%;与静态拣货策略相比,拣货单为10张情况下,采用GASA算法时,文中设计动态拣货策略下的拣货时间可减少6%,且随着拣货单数量的增加,拣货时间节约占比越大。结论 GASA算法较GA算法其求解动态拣货路径优化问题更高效、优化结果更好。文中所提动态拣货策略更方便实施,在静态拣货路径优化基础上,可进一步提高拣货效率,且拣货单越多,效果就越显著。     

可移动货架下的储位选择与拣货路径联合优化

随着电商直播等在线模式的兴起，订单异质化加重，商品需求变化比以往更快。面对频繁变化的订单，许多电商仓库选择使用更加柔性的存储模式，如可移动货架，对仓库的存储布局进行快速调整，进而更好地进行拣货作业。针对分散存储策略和可移动货架下的订单拣货路径进行研究，提出一个包含储位选择与拣货路径的联合优化模型和货架移动优化模型的双层优化模型，设计一种双层元启发式算法框架，最后利用不同规模的数值实验对模型和算法的有效性进行验证。     

仓储企业分区拣货效率探析

通过对分区拣货作业策略的探讨,旨在结合国内中小型零售型配送行业的特点,提出加强拣货作业管理的模式和方法,从而促进我国物流业的快速发展.介绍了各种分区拣货作业的优缺点,对拣货作业成本分析,为拣货作业模式的选择提供了依据.     

拣货方式、存储策略与路径策略协同研究

对拣货方式、路径策略与存储策略进行协同研究,设计了具有代表性的策略组合。推出了不同路径策略下,实际拣货路径长度的计算公式。通过对各种策略组合仿真结果的比较分析,确定了3种策略的相对重要性:1）分批策略对减少拣货作业总时间影响最大;2）分类存储策略比随机存储策略所需的行走路径缩短很多;3）路径策略对拣货行走的时间的减少明显小于分批拣货方式和分类存储策略带来的拣货作业时间的减少。具体决策时,应优先考虑分类存储策略和分批拣货方式,在确定其已经有效的情况下再考虑路径策略,以使拣货效率达到整体最优。     

基于NSGA-Ⅱ算法的分区拣选优化

电商背景下的客户订单呈现出多品种、小批量、高频次等特点,给仓库拣选工作带来很大的挑战。为提高拣选效率,在订单完全拆分的分批策略和组合优化的行走策略下,设计了以总服务时间最小、分区工作量平衡度最优和二次分拣效率最高的多目标分区拣选模型。由于3个目标函数之间存在矛盾,设计了NSGA-II算法对多目标优化模型进行求解。通过数值实验,与传统的不拆分订单的分区拣选系统对比,发现在订单批量环境为[1,4]时,分别使总服务时间减少了43.88%,平衡度改善了84.61%,并分析了区域个数、订单总数和订单批量环境对系统效率的影响。     

基于混合算法的单堆垛机多巷道拣选作业调度研究

针对单堆垛机在多巷道自动化仓库中的拣选路径规划问题,建立了求解含周转箱约束的堆垛机拣选作业最短路径数学模型,提出用遗传模拟退火混合算法进行求解。通过Matlab分别对不同算法进行实例仿真,结果表明：该混合算法克服了遗传算法早熟,以及模拟退火算法收敛性慢的缺点,求出的解更优,收敛速度更快,稳定性更好。该混合算法具有可行性和高效性。     

Determining the number of zones in a pick-and-sort order picking system

In this study we consider a pick-and-sort order picking system, in which batches of orders are picked simultaneously from different (work) zones by a group of order pickers. After picking, the orders are transported by a conveyor to a next station for consolidation and packing. Packing can only occur when an order has been picked completely. For a given number of workers, each assigned to a single zone, a larger number of zones reduces pick time (since travel time reduces), but increases waiting time for completion at the packing stations, because more partial batches needing assembly arrive at the packing stations. Our aim is to determine the optimal number of zones such that the total (picking and packing) time to complete a batch is minimised. We solve this problem by optimally assigning items to pick routes in each zone. We illustrate the method with data taken from a distribution centre of one of the largest online retailers in The Netherlands.     

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。     

Maverick picking: the impact of modifications in work schedules on manual order picking processes

Order picking, the process of retrieving items from their storage locations to fulfil customer orders, ranks among the most labour- and time-intensive processes in warehousing. Prior research in this area had a strong focus on the development of operating policies that increase the efficiency of manual order picking, for example by calculating optimal routes for the order pickers or by assigning products to storage locations. One aspect that poses a major challenge to many warehouse managers in practice has, curiously enough, remained largely unexplored by academic research: modifications in workflows (i.e. workplace deviance in a positive or negative sense) in order picking, which we define as ‘maverick picking’. The purpose of this paper is to characterise maverick picking and to study its causes, its forms of appearance and its potential impact on order picking performance. To gain insights into maverick picking, we first survey the literature to illustrate the state-of-knowledge of maverick picking. Subsequently, we report the results of a multi-case study on maverick picking and deduct a related content framework. The results of our case study support the proposition that maverick picking is highly relevant in practice and that it is a major determinant of order picking performance.     

Order picking in a parallel-aisle warehouse with turn penalties

In many real-life routing problems, incorporating the negative effects of turns is an important, but often overlooked aspect. This is especially true for order picking in warehouses, where making the turns not only decreases the picking efficiency by reducing the speed of the vehicle, but it also results in other unquantifiable effects such as vehicle tipovers, increased congestion and increased risk of collision with pedestrians or other vehicles. In this paper, we consider the order picking problem in a parallel-aisle warehouse by taking into account the number and effect of the turns. In particular, we show that the problem of minimising the number of turns, minimising travel time under turn penalties, the biobjective problem that involves turn and travel time minimisation as separate objectives, and the triobjective problem with U-turn minimisation as a third objective can all be solved in polynomial time. Our computational results show that the algorithms we develop can generate the corresponding Pareto front very quickly, and significantly outperform heuristic approaches used in practice.