基于商品关联度的智能仓库储位分配问题研究

储位分配方案是直接影响智能仓库工作效率和拣选成本的关键因素.根据历史订单信息定义了商品之间的关联度,以同一货架上的商品之间的关联度之和极大化为目标建立了智能仓库储位分配问题的数学模型,并设计了求解模型的算法.首先根据历史订单信息计算商品之间的关联度,然后结合商品的周转率、商品之间的关联度等信息,设计了启发式算法求解智能仓库储位分配问题,并且分析了启发式算法的时间复杂度.通过大量的模拟计算验证了本文建立的数学模型和设计的启发式算法的有效性,证明了以同一货架上商品之间关联度极大化为目标和以订单拣选过程中搬运货架总次数极小化为目标的一致性.通过对比分析本文算法得到的储位分配结果与随机储位分配结果可以看出,利用基于商品关联度的启发式算法得到的储位分配方案比随机储位分配方案对应的货架搬运次数平均减少了30.08%.     

基于移动机器人的订单拣选系统货位优化模型和算法研究

为了提高基于移动机器人的订单拣选系统的拣选效率,文章提出了基于货品相关性和货架中货品总出库频次的货位优化方法.将货位优化分为货品存放到货架及货架布局到仓库中现有位置两个阶段,为避免拥堵采用分散存储策略建立了最小化货架搬运次数以及最小化机器人总拣选路程的数学模型,并设计两阶段启发式算法求解.结合A网上药店的历史订单数据进行了仿真实验,实验结果表明,文章提出的货位优化方法有效提高了订单拣选效率.     

分类存储返回型与S型拣选路径随机模型的比较研究

物流配送中心中,减小订单拣选行走距离进而优化人工拣选作业系统可有效提高客户满意度,降低成本.货位指派和拣选方式是影响拣选行走距离的两个重要因素.作者在分类存储的货位指派策略下、分别对返回型和S型拣选方式,建立了拣选距离随机模型.仿真结果表明,模型结果能在误差允许条件下较好地与仿真逼近.通过在4种物品订购频率和货位分配情况下对返回型和S型拣选方式的比较,得出两种拣选方式各自适用的情况.     

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.     

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。     

基于AGV的智能仓库系统储位分配问题研究

研究了基于自动引导机器人(AGV)的"货到人"拣选模式下的智能仓库系统补货阶段的储位分配问题.根据待拣选订单信息计算出商品之间的关联度,考虑了货架上存放的物品信息、空余储位数量、待补货物品信息,以同一货架上的各种商品之间的关联度之和最大化为目标函数,建立了补货阶段储位分配问题的整数规划模型;设计了求解模型的贪婪算法,并分析了算法复杂度.利用一个具体实例进行模拟计算,分析了贪婪算法的求解效果.进一步利用不同规模算例进行模拟计算,分析了贪婪算法的计算时间和近似比,结果显示贪婪算法可以在很短的时间内得到近似最优解,近似比不超过1.15.设计的贪婪算法可以作为智能仓库管理信息系统的核心算法.     

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。     

基于聚类和动态规划的组合路径策略

为提高电商仓库的拣货作业效率,本文提出了基于聚类和动态规划的组合路径策略,实现了生成路径消耗时间和路径长度之间的平衡,并将这一策略成功地应用到多区型仓库。该策略分四步:首先,根据待拣储位分布特征,运用聚类分析法对其进行分类;然后,以各类的首末储位作为节点,运用动态规划法对已得分类进行排序,得到相应的类序;其次,得到各类内部路径;最后,依次拣取待拣商品,并返回出发点完成拣货作业。在提出新的路径策略后,通过仿真方法将新策略与三种传统路径策略(穿越策略、最大间隙策略和混合策略)和一种优质算法(蚁群算法)进行了对比分析,结果表明:该策略具备良好的适用性和实用性。     

Flying-V型仓储布局货位分配优化方法研究

Flying-V是一种典型的非传统布局方式,根据其布局方式的特性,针对仓储货位分配优化问题,以货物出入库效率最高和货物存放的重心最低为优化目标,建立了货位分配多目标优化模型,并采用自适应策略的遗传算法(GA),以及粒子群算法(PSO)进行求解。根据货位分配的优化特点,在GA算法的选择、交叉和变异环节均采用自适应策略, 同时采用惯性权重线性递减的方法设计了PSO算法,有效地解决了两种算法收敛速度慢和易“早熟”的问题,提高了算法的寻优性能。为了更好地表现两种优化求解算法的有效性和优越性,结合具体的货位分配实例利用MATLAB软件编程实现。通过对比分析优化结果表明,PSO算法在收敛速度和优化效果方面相比于自适应GA算法更具有优势,更加合适于解决Flying-V型仓储布局货位分配优化问题。     

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.     

Space allocation and aisle positioning for an industrial pick-to-belt system

Distribution centres (DCs) are a key component in supply chains. In order to respond quickly to customer demands, most DCs use a fast-pick area where order picking can be executed efficiently. In these areas, the amount of space allocated to each product is of great importance, as it determines the number of replenishments required to guarantee product availability, thus avoiding interruptions in the order picking. The obvious assumption is that the performance of DCs could be improved by using simple strategies such as assigning more space to products having the highest demand. However, as this paper shows, the practical constraints related to the need for aisles granting safe and easy access to storage locations make designing fast-pick areas difficult. A parameter-free, two-phase algorithm based on product-adding heuristics combined with a space-reduction heuristic is proposed to solve this design problem. When applied to real data provided by our industrial partner, this heuristic generated considerable time savings.     

Heuristics for the multi-depot petrol station replenishment problem with time windows

In the multi-depot petrol station replenishment problem with time windows (MPSRPTW), the delivery of petroleum products stored in a number of different petroleum depots to a set of petrol distribution stations has to be optimized. Each depot has its own fleet of heterogeneous and compartmented tank trucks. Stations specify their demand by indicating the minimum and maximum quantities to be delivered for each ordered product and require the delivery within a predetermined time window. Several inter-related decisions must be made simultaneously in order to solve the problem. For this problem, the set of feasible routes to deliver all the demands, the departure depot for each route, the quantities of each product to be delivered, the assignment of these routes to trucks, the time schedule for each trip, and the loading of the ordered products to different tanks of the trucks used need to be determined. In this paper, we propose a mathematical model that selects, among a set of feasible trips, the subset that allows the delivery of all the demands while maximizing the overall daily net revenue. If this model is provided with all possible feasible trips, it determines the optimal solution for the corresponding MPSRPTW. However, since the number of such trips is often huge, we developed a procedure to generate a restricted set of promising feasible trips. Using this restricted set, the model produces a good but not necessarily optimal solution. Thus the proposed solution process can be seen as a heuristic. We report the results of the extensive numerical tests carried out to assess the performance of the proposed heuristic. In addition, we show that, for the special case of only one depot, the proposed heuristic outperforms a previously published solution method.     

Solving the forward-reserve allocation problem in warehouse order picking systems

Many warehouses store at least some goods in two areas, a reserve areathat is efficient for storage and a forward area that is efficient fororder picking. The forward-reserve allocation problem determines the set ofStock-Keeping Units and their space allocations in the forward area to maximizethe forward area's benefit by trading off the relevant costs of orderpicking and internal replenishment. The mathematical model of this decisionresembles the classical knapsack problem with the additional complexity that ithas a discontinuous nonlinear cost function. A simple greedy heuristic has beenproposed in the literature to solve this problem. This paper proposes analternative branch-and-bound algorithm that can quickly solve the problem tooptimality. Heuristic and optimal solutions are numerically compared usingproblem instances based on real warehouse data. Results suggest that theheuristic solutions are very close to the optimal ones in terms of both theobjective value and the forward assignment.     

A Lagrangian relaxation-based heuristic for the vehicle routing with full container load

We address a problem of vehicle routing that arises in picking up and delivering full container load from/to an intermodal terminal. The substantial cost and time savings are expected by efficient linkage between pickup and delivery tasks, if the time of tasks and the suitability of containers for cargo allow. As this problem is NP-hard, we develop a subgradient heuristic based on a Lagrangian relaxation which enables us to identify a near optimal solution. The heuristic consists of two sub-problems: the classical assignment problem and the generalized assignment problem. As generalized assignment problem is also NP-hard, we employ an efficient solution procedure for a bin packing based problem, which replaces the generalized assignment problem. The heuristic procedure is tested on a wide variety of problem examples. The test results demonstrate that the procedure developed here can efficiently solve large instances of the problem.     

An Improved,Multiplier Adjustment Procedure for the Segregated Storage Problem

The segregated storage problem consists of determining an optimal distribution of products among existing storage compartments such that at most one product may be stored in a given compartment. The problem is solved using linear programming, Lagrangean relaxation and multiplier adjustment techniques. The procedures are easily adapted to obtain good heuristic solutions quickly. Computational results are given.     

The impact of product substitution on retail merchandising

We analyze the impact of product substitution on two key aspects of retail merchandising: order quantities and expected profits. To perform this analysis, we extend the basic news-vendor model to include the possibility that a product with surplus inventory can be used as a substitute for out of stock products. This extension requires a definition and an approximation for the resulting effective demand under substitution. A service rate heuristic is developed to solve the extended problem. The performance of this heuristic is evaluated using an upper bound generated by solving the associated Lagrangian dual problem. Our analysis suggests that this heuristic provides a tractable and accurate method to determine order quantities and expected profits under substitution. We apply this heuristic to examine how the level of demand uncertainty and correlation, and the degree of substitution between products affect order quantities and expected profits under substitutable demand. In addition, we use the heuristic to better understand the mechanism by which substitution improves expected profits.     

Reducing the elastic generalized assignment problem to the standard generalized assignment problem

The elastic generalized assignment problem (eGAP) is a natural extension of the generalized assignment problem (GAP) where the capacities are not fixed but can be adjusted; this adjustment can be expressed by continuous variables. These variables might be unbounded or restricted by a lower or upper bound, respectively. This paper concerns techniques aiming at reducing several variants of eGAP to GAP, which enables us to employ standard approaches for the GAP. This results in a heuristic, which can be customized in order to provide solutions having an objective value arbitrarily close to the optimal.     

On A Special Case of the Quadratic Assignment Problem with an Application to Storage-and-Retrieval Devices

In a storage-and-retrieval device, items are retrieved on demand from a storage bank by a picking mechanism. Many varieties of these robotic devices are in use in manufacturing, logistics and computer peripherals. In printed circuit board manufacturing, storage-and-retrieval is intertwined with component placement and product clustering. Under certain circumstances, the problem of assigning items by type to storage slots to minimize the expected retrieval time is a quadratic assignment problem. Although such models are very difficult to solve to optimality, an important special case considered here admits an easy solution, namely, the well known “organ pipe” arrangement of items.     

A model based evaluation of a commonly used rule of thumb for warehouse layout

A model is formulated to describe the cost effects of alternative warehouse layouts in a dedicated storage warehouse that utilizes dual address order picking cycles. The model is utilized to evaluate a common warehouse layout rule of thumb where stock levels are determined using economic order quantities, and stock locations are obtained using the cube per order index. A simple heuristic procedure is devised to utilize the cost model to obtain warehouse layouts with lower total inventory and order picking cost than those obtained using the rule of thumb. Four test problems are generated to assess the difficulty of obtaining improved warehouse layouts. It is shown that the heuristic procedure can achieve significantly improved warehouse layouts with minimal computational effort.