多目标下的自动化立体仓库拣选作业路径优化

自动化立体仓库系统是在计算机直接控制和管理下自动存取并实现综合自动化管理货物的多层仓库存储系统,其中,堆垛机作业时间长短直接影响仓库存储效率的高低.针对堆垛机拣选作业模式,提出了自动化立体仓库拣选作业的新型数学模型,并对遗传算法初始种群进行改进,用改进后的遗传算法对该模型进行了求解.     

自动化立体仓库堆垛机拣选作业调度研究

将堆垛机拣选作业调度归纳为旅行商问题，并从遗传算法角度，研究了求解堆垛机拣选作业调度最优路径的具体方法。经过仿真试验验证，这一方法完全可行。     

物流自动化仓库拣选作业调度粒子群优化研究

建立自动化仓库堆垛机拣选作业调度模型,采用粒子群算法对拣选作业调度进行优化。提出一种基于货位和粒子位置的二维粒子编码方法,并采用基于粒子位置互换操作的局部搜索方法。计算结果说明,混合粒子群算法能够有效地对自动化仓库拣选作业调度进行优化。     

基于遗传算法的仓库拣选作业优化与eM—Plant实现

在建立了仓库拣选作业优化模型的基础之上，提出了用遗传算法（GA）求解堆垛机拣选作业最优路径的具体方法，并在一个实例中用eM—Plant软件中的遗传算法模块实现了仓库拣选作业的仿真优化。     

基于正弦式自适应遗传算法的堆垛机路径优化研究

针对自动化立体仓储系统中堆垛机的路径优化问题,在分析AS/RS对货物的分配原则的基础上,论述了堆垛机的3种作业模式,得出了基于AGA的优化算法——正弦式自适应遗传算法。最后通过建立数学模型对算法进行分析,并结合MATLAB进行仿真验证。系统运行结果表明,该算法能有效地降低由AGA引起的交叉概率p_c和变异概率p_m所带来的影响,进而避免种群进化停滞和陷于局部最优等现象,提高堆垛机作业的效率。     

基于遗传算法的堆垛机路径优化

分析了自动化仓库堆垛机拣选作业的运行特点,并将堆垛机的路径优化问题归结为旅行商问题(TSP)来求解,通过计算货位点所在的坐标位置产生拣选点,运用基于顺序表示的遗传基因编码方法——Grefenstette编码,在MATLAB软件上编制并运行了堆垛机的访问路径优化程序,通过仿真试验和工程实际应用表明,遗传算法在优化路径问题中的收敛速度显著加快,且能有效提高系统的工作效率。     

自动化立体仓库双梭堆垛机作业时间及系统吞吐性能

分析了堆垛机作业时间的影响因素及系统吞吐性能;建立了多种情况下双梭堆垛机作业时间期望和方差的数学模型以及多种情况下双梭堆垛机自动存取系统的作业周期数学模型。     

基于遗传算法的仓库拣选作业优化与eM-Plant实现

在建立了仓库拣选作业优化模型的基础之上,提出了用遗传算法(GA)求解堆垛机拣选作业最优路径的具体方法,并在一个实例中用eM-Plant软件中的遗传算法模块实现了仓库拣选作业的仿真优化。     

基于粒子群算法的立体仓库系统堆垛机作业路线优化

运用粒子群算法优化立体仓库系统堆垛机作业路线。实际应用证明,该算法用于自动化立体仓库系统堆垛机作业路线优化是有效可行的。     

基于单亲遗传算法的堆垛机复合作业路径优化

基于堆垛机的作业方式和特点,针对复合作业路径优化问题进行数学建模,并采用单亲遗传算法求解,实验仿真和工程应用证明该模型和算法是可行和有效的。     

自动化仓库旋转货架拣选优化

对自动化仓库旋转货架拣选优化问题进行了描述,提出了求解该问题的分层遗传算法（HGA）。根据具体问题对低层和高层采用了不同的进化策略,详细介绍了HGA算法的原理。通过实际算例运算及分析,表明HGA对解决自动化仓库旋转货架拣选优化问题的有效性。     

Optimizing the order picking of a scholar and office supplies warehouse

The warehouse operation planning in the scholar and office supplies industry encompasses the activities of receiving, storing, order picking, and shipping products. The order picking operation is a laborious and capital intensive procedure. Despite the extensive literature to solve practical problems of warehouse routing as a step of the order picking process, the application of general approaches to deal with the routing of the warehouse pickers is not straightforward given the specific conditions and configurations and the singular design and operational policies commonly found in practice. In this paper, we present an optimization approach to the stacker routing problem found in the order picking of a Brazilian scholar and office supplies company. The approach is based on a routing algorithm that solves different integer linear problems to define the optimal routes of the stacker inside the facility. The solutions of several numerical experiments show the potential and effectiveness of the approach, for instance, that only one fifth of the routes actually performed by the company are optimal and that the algorithm application to a real set of batches reduced by more than one fourth the total distance monthly traveled by the stacker.     

改进自适应遗传算法求解“货到人”拣选系统订单分批问题

“货到人”拣选系统采用自动导引运输车(Automated guided vehicle,AGV)实现自动拣选作业,由AGV搬运货架到拣选站台,再由拣选人员从货架上拣取商品。订单分批作为拣选作业的准备工作,是影响AGV搬运次数和人工拣货次数的关键因素,优化订单分批策略对提高“货到人”拣选系统效率至关重要。在电商智能仓库背景下,综合考虑订单需求多种商品、商品多货架分布存储、订单与货架供需匹配关系未知等实际因素,以人工拣选成本和AGV搬运成本之和最小为目标构建数学模型,并设计改进自适应遗传算法求解。该算法采用启发式策略生成初始种群,引入具有自适应变换概率的交叉和变异算子,并加入局部搜索过程以增强寻优能力。最后通过试验测试验证模型和算法的有效性,证明种群初始化方法的优势,并采用灵敏度分析给出合理的周转箱数量配置建议。研究可为电商企业通过订单分批优化提高拣选效率、降低拣选成本提供实践指导,为“货到人”拣选系统的实际应用提供科学依据。     

自动化立体仓库拣选路径优化问题研究

利用蚁群算法结合遗传算法来求解固定货架拣选的TSP问题,通过MATLAB对随机产生的10个和30个待拣选货位点的拣选作业路径优化进行了仿真试验。仿真结果表明：对于待拣选货位点数目有较大范围变动的情况,该方法能够对拣选路径进行全局优化;利用蚁群算法结合遗传算法求解固定货架拣选TSP问题时,在拣选的货位点数量适中的情况下（10～30）,变异概率值在（0．008～0．020）之间是最优的。     

双拣货区自动分拣系统品项分配优化

提前拣选、压缩合流的分拣策略使双拣货区自动分拣系统的拣选效率明显高于单拣货区自动分拣系统,基于此提出该策略下品项分配优化问题。介绍压缩合流的工作原理,确定双拣货区的品项分配对订单的压缩程度及提前拣选时间的影响。以负的各订单提前拣选时间之和最小为目标函数建立数学模型,该模型可归结为0-1规划问题并应用最大最小蚂蚁系统算法求解。实例仿真与分析证明了该算法的有效性。     

COMPRESSIBLE VIRTUAL WINDOW ALGORITHM IN PICKING PROCESS CONTROL OF AUTOMATED SORTING SYSTEM

Compared to fixed virtual window algorithm （FVWA）, the dynamic virtual window algorithm （DVWA） determines the length of each virtual container according to the sizes of goods of each order, which saves space of virtual containers and improves the picking efficiency. However, the interval of consecutive goods caused by dispensers on conveyor can not be eliminated by DVWA, which limits a further improvement of picking efficiency. In order to solve this problem, a compressible virtual window algorithm （CVWA） is presented. It not only inherits the merit of DVWA but also compresses the length of virtual containers without congestion of order accumulation by advancing the beginning time of order picking and reasonably coordinating the pace of order accumulation. The simulation result proves that the picking efficiency of automated sorting system is greatly improved by CVWA.     

配送中心订单分批拣货模型及包络算法

对订单分批问题进行分析,构造了拣货作业订单分批问题的数学模型。以装箱算法为基础,考虑订单品项分布的具体位置,提出订单分批问题的包络算法,并用算例验证了算法的有效性和实用性。     

A multi-stage heuristic algorithm for matching problem in the modified miniload automated storage and retrieval system of e-commerce

E-commerce, as an emerging marketing mode, has attracted more and more attention and gradually changed the way of our life. However, the existing layout of distribution centers can't fulfill the storage and picking demands of e-commerce sufficiently. In this paper, a modified miniload automated storage/retrieval system is designed to fit these new characteristics of e-commerce in logistics. Meanwhile, a matching problem, concerning with the improvement of picking efficiency in new system, is studied in this paper. The problem is how to reduce the travelling distance of totes between aisles and picking stations. A multi-stage heuristic algorithm is proposed based on statement and model of this problem. The main idea of this algorithm is, with some heuristic strategies based on similarity coefficients, minimizing the transportations of items which can not arrive in the destination picking stations just through direct conveyors. The experimental results based on the cases generated by computers show that the average reduced rate of indirect transport times can reach 14.36% with the application of multi-stage heuristic algorithm. For the cases from a real e-commerce distribution center, the order processing time can be reduced from 11.20 h to 10.06 h with the help of the modified system and the proposed algorithm. In summary, this research proposed a modified system and a multi-stage heuristic algorithm that can reduce the travelling distance of totes effectively and improve the whole performance of e-commerce distribution center.     

Taboo search algorithm for item assignment in synchronized zone automated order picking system

The idle time which is part of the order fulfillment time is decided by the number of items in the zone; therefore the item assignment method affects the picking efficiency. Whereas previous studies only focus on the balance of number of kinds of items between different zones but not the number of items and the idle time in each zone. In this paper, an idle factor is proposed to measure the idle time exactly. The idle factor is proven to obey the same vary trend with the idle time, so the object of this problem can be simplified from minimizing idle time to minimizing idle factor. Based on this, the model of item assignment problem in synchronized zone automated order picking system is built. The model is a form of relaxation of parallel machine scheduling problem which had been proven to be NP-complete. To solve the model, a taboo search algorithm is proposed. The main idea of the algorithm is minimizing the greatest idle factor of zones with the 2-exchange algorithm. Finally, the simulation which applies the data collected from a tobacco distribution center is conducted to evaluate the performance of the algorithm. The result verifies the model and shows the algorithm can do a steady work to reduce idle time and the idle time can be reduced by 45.63% on average. This research proposed an approach to measure the idle time in synchronized zone automated order picking system. The approach can improve the picking efficiency significantly and can be seen as theoretical basis when optimizing the synchronized automated order picking systems.     

“货到人”拣选系统订单排序优化

"货到人"拣选系统采用"出库-拣选-回库"的拣选模式,货箱出入库频率高成为制约拣选效率的关键。为降低货箱出入库频率,进而提高系统拣选效率,提出订单排序优化模型。定义订单耦合因子表示两个订单之间可放置于拣选缓存中的共用货箱数量,并以此作为模型参数,将优化目标由提高拣选效率转化为降低货箱出入库频率。模型中,每个拣选台内的订单排序优化问题可归结为旅行商(Tranvelling salesman problem,TSP)问题。为求解模型,提出改进的K-Means聚类算法。计算每个订单与其他订单间的耦合因子,并对其进行排序;以单拣选台所有订单的耦合因子之和最大为目标,为每个拣选台分配订单并对订单进行排序。仿真结果显示,优化后系统拣选效率提高15.9%。