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.     

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。     

A note on the linearity of Ratliff and Rosenthal's algorithm for optimal picker routing

Ratliff and Rosenthal state that their dynamic programming algorithm for optimal picker routing has linear complexity in the number of aisles. Indeed, solving the dynamic program is linear, but computing the cost coefficients of the dynamic program certainly requires the consideration of all picking positions, whose number is independent of the number of aisles. For a given unsorted sequence of picking positions, our algorithm is linear in the sum of the number of aisles and number of picking positions.     

A travel-time model for a person-onboard order picking system

The design of an order picking system in a distribution center depends on several decisions, where a key decision is determining the optimal storage system configuration (the number, length, and height of the storage aisles). To make this decision, a throughput model that considers vertical, as well as horizontal, travel is needed. In this paper we extend prior research that considers horizontal travel for a given number and length of the storage aisles so that we are also able to consider the height of the aisles as well. Such a model will provide a more accurate estimate of the throughput of an order picker and it will also permit an examination of the tradeoff between the length and height of the aisles. The analytical model we develop to estimate throughput is based on probability models and order statistics results assuming random storage. It is intended for person-onboard order picking systems and we consider both Tchebychev and rectilinear travel. We illustrate the use of our travel-time model by incorporating it into a simple, cost-based optimization model to recommend the height of a one-pallet-deep storage system.     

A dynamic programming algorithm for the space allocation and aisle positioning problem

The space allocation and aisle positioning problem (SAAPP) in a material handling system with gravity flow racks is the problem of minimizing the total number of replenishments over a period subject to practical constraints related to the need for aisles granting safe and easy access to storage locations. In this paper, we develop an exact dynamic programming algorithm for the SAAPP. The computational study shows that our exact algorithm can be used to find optimal solutions for numerous SAAPP instances of moderate size.     

On-line order batching and sequencing problem with multiple pickers: A hybrid rule-based algorithm

Existing on-line order batching rules, namely fixed time window batching (FTWB) and variable time window batching (VTWB), try to choose the fixed time window in the case of FTWB or the fixed number of orders in the case of VTWB. However, these solutions are not appropriate for the fluctuating order environment. The reasonable assignment of batches to order pickers is also an important issue in order picking systems. Motivated by these issues, we study the problem of integrating on-line order batching and the assignment of the batches, which is called the on-line order batching and sequencing problem with multiple pickers (OOBSPMP). The objective is to minimize the turnover time. To solve the problem, a hybrid rule-based algorithm, referred to FTWB, is proposed in order to form batches and assign them to appropriate pickers under a fluctuating order environment. Three batching situations (off-peak, normal and peak arrival time) and two assigning situations (assigning to one busy picker and assigning to one idle picker) are distinguished. Through a series of experiments, we discover several enlightening findings: (i) the rule-based algorithm demonstrates high effectiveness and efficiency in turnover time with multiple pickers; (ii) the rule-based algorithm leads to an impressive improvement in both saving time and wage costs under different arrival rates, picking devices and time intervals compared with VTWB; (iii) to obtain both good warehouse performance and a reasonable workload distribution, the factors, such as the fixed time window, the average workload per picker and the average idle time per picker are also important issues in analysing the efficiency of order picking systems.     

A constructive aisle design model for unit-load warehouses with multiple pickup and deposit points

We develop a network-based warehouse model of individual pallet locations and their interactions with appropriate cross aisles in order to evaluate the expected travel distance of a given design. The model is constructive in that it uses Particle Swarm Optimization to determine the best angles of cross aisles and picking aisles for multiple, pre-determined pickup and deposit (P&D) points in a unit-load warehouse. Our results suggest that alternative designs offer reduced expected travel distance, but at the expense of increased storage space. The opportunity for benefit also seems to decline as P&D points increase in number and dispersion.     

Facility placement with sub-aisle design in an existing layout

In this paper, we consider the integration of facility placement in an existing layout and the configuration of one or two connecting sub-aisles. This is relevant, for example, when placing a new machine/department on a shop floor with existing machines/departments and an existing aisle structure. Our work is motivated by the work of Savas et al. [Savas, S., Batta, R., Nagi, R., 2002. Finite-size facility placement in the presence of barriers to rectilinear travel. Operations Research 50 (6), 1018–1031], that considered the optimal planar placement of a finite-size facility in the presence of existing facilities. Our work differs from theirs in that we consider material handling to be restricted to the aisle structure. We do not allow the newly placed facility to overlap with existing facilities or with the aisle structure. Facilities are rectangular and travel is limited to new or existing aisles. We show that there are a finite number of candidate placements for the new facility. Algorithms are developed to find the optimal placement and the corresponding configurations for the sub-aisles. Complexity of the solution method is analyzed. Also, a numerical example is provided to explore the impact of the number of sub-aisles added.     

Using a TSP heuristic for routing order pickers in warehouses

In this paper, we deal with the sequencing and routing problem of order pickers in conventional multi-parallel-aisle warehouse systems. For this NP-hard Steiner travelling salesman problem (TSP), exact algorithms only exist for warehouses with at most three cross aisles, while for other warehouse types literature provides a selection of dedicated construction heuristics. We evaluate to what extent reformulating and solving the problem as a classical TSP leads to performance improvements compared to existing dedicated heuristics. We report average savings in route distance of up to 47% when using the LKH (Lin–Kernighan–Helsgaun) TSP heuristic. Additionally, we examine if combining problem-specific solution concepts from dedicated heuristics with high-quality local search features could be useful. Lastly, we verify whether the sophistication of ‘state-of-the-art’ local search heuristics is necessary for routing order pickers in warehouses, or whether a subset of features suffices to generate high-quality solutions.     

Improving product location and order picking activities in a distribution centre

This article examines the effect of different product location strategies on the distance that order pickers must cover to do their job. This distance is an important cost component in warehousing activities. Our empirical study is based on a real industrial setting, in which the products are located on both sides of a conveyor belt. We show that choosing the right product location strategy allows the current picking distance to be reduced more than 10%. We also propose a post-optimization procedure that can further reduce picking distances—up to 20% of the current distances. Through a study of the routing strategy used to dispatch pickers, we demonstrate that solving a simple travelling salesman problem can further reduce distances up to 13%, compared with the distances incurred using a predetermined route. We show that reductions of up to 27% compared to current picking distances are possible if our product location and routing methods are combined.     

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.     

A clustering algorithm for item assignment in a synchronized zone order picking system

In a synchronized zone order picking system, all the zones process the same order simultaneously. There may be some idle time when the zone pickers wait until all the pickers complete the current order. This paper develops a heuristic algorithm to balance the workload among all pickers so that the utilization of the order picking system is improved and to reduce the time needed for fulfilling each requested order. A similarity measurement, using customer orders, of any two items is first presented for measuring the co-appearance of both items in the same order. With this similarity measurement, a natural cluster model, which is a relaxation of the well-studied NP-hard homogeneous cluster model, is constructed. The heuristic algorithm is then proposed to solve the model for locating all the items into distinct zones. Finally, empirical data and simulation experiments verify that the objectives of the item cluster model are achieved.     

Tabu search heuristics for the order batching problem in manual order picking systems

In a manual order picking system, order pickers walk or ride through a distribution warehouse in order to collect items requested by (internal or external) customers. In order to perform these operations efficiently, it is usually required that customer orders be combined into (more substantial) picking orders that are limited in size. The order batching problem considered in this paper deals with the question of how a given set of customer orders should be combined into picking orders such that the total length of all picker tours necessary for all of the requested items to be collected is minimized. For the solution of this problem the authors suggest two approaches based on the tabu search principle. The first is a (classic) tabu search (TS), and the second is the attribute-based hill climber (ABHC). In a series of extensive numerical experiments, these approaches are benchmarked against other solution methods put forward in the current literature. It is demonstrated that the proposed methods are superior to the existing methods and provide solutions which may allow distribution warehouses to operate more efficiently.     

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.     

The deterministic product location problem under a pick-by-order policy

This paper treats the product location problem in warehouses, i.e., stock keeping units (SKUs) are to be assigned to storage positions in order to minimize the resulting picking effort when retrieving SKUs in a pick-by-order environment. We restrict our view on warehouses having a single cross aisle and show that already very simple layouts consisting of only a single rack lead to NP-hard optimization problems. In addition to a complexity analysis for different layouts, elementary solution procedures are introduced and tested. Finally, we investigate the robustness of our deterministic problem when facing erroneous input data.     

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.     

Forward-reserve allocation in a warehouse with unit-load replenishments

Many companies configure their warehouse with a forward area and a reserve area. The former is used for efficient order-picking, the latter for replenishing the forward area. We consider a situation in which orders are picked during a certain time period, referred to as the picking period. Prior to the picking period there is sufficient time to replenish the forward area. Our objective is to determine which replenishments minimize the expected amount of labor during the picking period. Further, we present a second model with a constraint on the replenishment activity. We model the problem as a binary programming problem and present efficient heuristics that provide tight performance guarantees. We compare the heuristics with procedures that are popular in practice and show that significant labor-savings are possible.     

Prioritizing replenishments of the piece picking area

Having sufficient inventories in the forward or piece picking area of a warehouse is an essential condition for warehouse operations. As pickers consume the inventory in the piece racks, there is a risk of stockout. This can be reduced by the timely replenishment of products from the bulk reserve area to the forward area. We develop and compare three policies for prioritizing replenishments for the case where order picking and replenishments occur concurrently because of time restrictions. The first policy, based on the ratio of available inventory to wave demand, reduces the number of stockouts considerably. The other two more sophisticated policies reduce the number of stockouts even more but require much more computation time, and are more costly in terms of implementation, maintenance and software updates. We present the results of implementing one of these policies in the warehouse of a large cosmetics firm.     

考虑商品数量和商品拣选成本的AGV智能仓库订单分批问题研究

AGV(Automated Guided Vehicle,自动导引车)智能仓库是一种基于“货到人”拣选模式的自动化仓库。本文考虑了订单中商品的需求量和货架上商品的存储量,以极小化货架搬运成本和商品拣选成本为目标,建立了AGV智能仓库订单分批问题的整数规划模型。本文针对订单分批问题的特点,提出了一种基于订单和货架交替选择的贪婪求解算法。对比CPLEX求解器的精确最优解,本文提出的贪婪算法的误差百分比不超过10%,平均误差百分比为5.38%;对比基于相似性的分批算法的求解结果,本文提出的贪婪算法不仅运算时间更短,解的质量也更好。进一步地,对比不考虑商品拣选成本的订单分批模型,本文提出的模型在不明显增加货架搬运成本的前提下,可以大幅度降低商品拣选成本。因此,在订单分批模型中考虑商品拣选成本是非常必要的。