Warehouse Layouts Research Articles

The order batching problem: A state-of-the-art review

The Order Batching Problem (OBP) has received a lot of attention from researchers in recent years. Until now, this problem has been considered as a whole with different problems or it has been studied on its own, with different solution methods, in different warehouse layouts, with different data structures. The aim of this study is to provide the researchers with a general analysis for studies using the OBP. In accordance with this purpose; the OBP is analyzed by an extensive literature review that includes all relevant studies to date. This literature review is based on a specific method and the studies are grouped into specific classes. Accordingly, the most studied problem with the OBP is the Picker Routing Problem (PRP) with 17% and 51% of the studies were solved by metaheuristic algorithms. The most widely used metaheuristic algorithms are Neighborhood Search and Genetic Algorithms.

Optimal two-class-based storage policy in an AS/RS with two depots at opposite ends of the aisle

Although automated storage/retrieval systems (AS/RSs) with two depots at opposite ends of each aisle (TD-AS/RSs) have been adopted in I-shaped warehouses (one of the three typical warehouse layouts), class-based storage policy has surprisingly not been investigated in TD-AS/RSs. This paper studies TD-AS/RSs with two storage zones, aiming to determine the optimal first storage zone that minimises the S/R machine’s expected travel time. We first formulate an expected travel time model. The analysis is then performed for two situations: (1) the ratio between the number of storage and total requests (Ratio-ST) is constant and (2) the Ratio-ST changes with time but follows a probability distribution. For the first situation, utilising proposed properties, an efficient optimal search-based algorithm (i.e. Algorithm 1) is developed. The results show that the proposed storage policy can improve system performance by up to about 60% and 40%, respectively, compared with two policies used in practice. Contrary to the literature results, our results indicate that the single command cycle performs better than the dual command cycle in the TD-AS/RS. For the second situation, an algorithm based on Algorithm 1 is developed. A case study using two real-world datasets confirms that our policy outperforms practically used policies in this situation.

A computational software system to design order picking warehouses

Even though order picking is the most costly operation in a warehouse, current design practices have used the same principles (straight rows with parallel pick aisles and perpendicular cross aisles) to reduce travel distances between pick locations for more than sixty years. We present an open-source computational software system for facilitating the design of warehouse layouts to near-optimality considering average walking distance of the picker as the objective function. This software is particularly novel because a wide variety of traditional and innovative designs are automatically generated and evaluated. For the warehouse design parameters we consider the rectangular aspect ratio of the floor plan, the number and location of cross aisles, the number and location of pick aisles, and the location of a single input/output location. The main components of the design system are importing pick list profile data, creating the warehouse layout design as a network, product allocation (slotting) of SKUs through the warehouse, routing of pickers on a sample of orders using an exact routing algorithm, and design optimization using a meta-heuristic. We provide both mathematical and computational descriptions of the algorithms used by the software system, describe the types of problems that can be solved, and summarize our computational experience. This software is open source available on a GitHub website under an MIT license.

Warehouse operations logistics improvement in a cork stopper factory

In a market that is increasingly competitive, the success depends on the efficiency levels of the implemented processes, highlighting the importance of the operations related to the warehouse management in the logistics flow. This research focuses on the improvement of management and logistic flows within a Portuguese cork stopper production factory. The primary goal is to maximize the warehouse stocking capacity and to improve logistics operations associated to that. By using procedure mapping techniques, data analysis, and direct time monitoring, it was possible to identify non value added times, as well as the associated causes and impacts, inadequate planning, and sizing of the warehouse layouts that were causing significant negative impact in terms of efficiency management. Using lean thinking philosophy, several improvement suggestions were presented to mitigate the problems identified.

Optimal order picker routing in the chevron warehouse

Order picking has often been considered as one of the most labor- and time-intensive tasks in warehouse operations. Among the various planning problems that have to be solved in manual picker-to-parts systems, the routing of the order picker usually accounts for the highest share of the total warehouse operating cost. To minimize the cost of order picking, researchers have developed various routing procedures that guide the order picker through the warehouse to complete given customer orders. For some warehouse layouts such as the chevron warehouse, an optimal routing algorithm has not yet been proposed. This article, therefore, contributes to filling this research gap by developing an optimal order picker routing policy for the chevron warehouse. The optimal routing algorithm proposed in this article is based on the concept of graph theory and utilizes a dynamic programming procedure. In addition, we propose various simple routing heuristics for the chevron warehouse. In computational experiments, the average order picking tour lengths resulting from optimal routing and from the simple heuristics are compared. Moreover, we compare the performance of the chevron warehouse to the conventional two-block warehouse under various conditions using the tour lengths obtained by the optimal algorithms.

Layout Design for Intelligent Warehouse by Evolution With Fitness Approximation

With the rapid growth of the express industry, intelligent warehouses that employ autonomous robots for carrying parcels have been widely used to handle the vast express volume. For such warehouses, the warehouse layout design plays a key role in improving transportation efficiency. However, this work is still done by human experts, which is expensive and leads to suboptimal results. In this paper, we aim to automate the warehouse layout designing process. We propose a two-layer evolutionary algorithm to efficiently explore the warehouse layout space, where an auxiliary objective fitness approximation model is introduced to predict the outcome of the designed warehouse layout and a two-layer population structure is proposed to incorporate the approximation model into the ordinary evolution framework. Empirical experiments show that our method can efficiently design effective warehouse layouts that outperform both heuristic-designed and vanilla evolution-designed warehouse layouts.

An Integrated Metaheuristic Routing Method for Multiple‐Block Warehouses with Ultranarrow Aisles and Access Restriction

A problem‐specific routing algorithm integrating ant colony optimization (ACO) and integer‐coded genetic algorithm (GA) is developed to address the newly observed limitations imposed by ultranarrow aisles and access restriction, which exist in the largest e‐commerce enterprise with self‐run logistics in China. Those limitations prohibit pickers from walking through the whole aisle, and the access restriction even allows them to access the pick aisles only from specific entrances. The ant colony optimization is mainly responsible for generating the initial chromosomes for the genetic algorithm, which then searches the near‐optimal solutions of picker‐routing with our novel chromosome design by recording the detailed information of access modes and subaisles. To demonstrate the merits of the proposed algorithm, a comprehensive simulation for comparison is conducted with 12 warehouse layouts with real order information. The simulation results show that the proposed hybrid algorithm is superior to dedicated heuristics in terms of solution quality. The impacts of the parameters with respect to warehouse layout on the picking efficiency are analyzed as well. Setting more connect aisles and cross aisles is suggested to effectively optimize the picking‐service efficiency in the presence of access limitations.

Robotics in order picking: evaluating warehouse layouts for pick, place, and transport vehicle routing systems

Motivated by recent technological advances in mobile robotics, this paper explores a novel approach for warehouse order picking. In particular, this work considers two types of commercially available mobile robots – one that can grasp items from a shelf (a picker) and another (a transporter) that can quickly deliver all items from the pick list to the packing station. A new vehicle routing problem is defined which seeks to minimise the time to deliver all items from a pick list to the packing station, a problem termed the pick, place, and transport vehicle routing problem. A mixed integer linear programming formulation is developed to answer three related research questions. First, what combination of picker and transport robots is required to obtain performance exceeding traditional human-based picking operations? Second, how should the composition of the robot fleet be altered to affect the greatest performance improvements? Finally, what are the impacts of warehouse layout designs when coordinated mobile robots are deployed? An extensive numerical analysis reveals that, (1) increasing the number of cross aisles decreases system performance; (2) centrally located packing stations improve system performance; and (3) the average distance from each pick location to the packing station and the average distance between pick locations are effective metrics for identifying specific fleet modifications that are likely to yield system improvements.

On the evaluation of AGVS-based warehouse operation performance

An automated guided vehicle system (AGVS) is an advanced material handling system widely used in various automated systems, particularly in e-commerce warehouses. The warehouse operation performance, mainly focusing on transportation efficiency, is affected by many factors, such as traffic-control policies and warehouse layouts. Based on the digraph theory and real-time control, two effective traffic-control policies with polynomial-time complexity are proposed to avoid the collision and to solve deadlocks. To accommodate more complicated situations, different types of warehouse layouts are tested. Extensive simulations are carried out to study the effects of policies, warehouse layouts, task densities and timing of an AGV to apply for a resource on the evaluation of the AGVS-based warehouse operation performance, which provide guidelines for warehouse designers.

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 adapted ant colony optimization algorithm for the minimization of the travel distance of pickers in manual warehouses

This paper proposes a new metaheuristic routing algorithm for the minimization of the travel distance of pickers in manual warehouses. The algorithm is based on the ant colony optimization (ACO) metaheuristic, which is combined and integrated with the Floyd–Warshall (FW) algorithm, and is therefore referred to as FW–ACO. To assess the performance of the FW–ACO algorithm, two sets of analyses are carried out. Firstly, the capability of the algorithm to provide effective solutions for the picking problem is analyzed as a function of the settings of the main ACO parameters. Secondly, the performance of the FW–ACO algorithm is compared with that of six algorithms typically used to optimize the travel distance of pickers, including exact algorithms for the solution of the travelling salesman problem (where available), two heuristic routing strategies (i.e. S-shape and largest gap) and two metaheuristic algorithms (i.e. the MIN–MAX ant system and Combined+). The comparison is made considering different warehouse layouts and problem complexities. The outcomes obtained suggest that the FW–ACO is a promising algorithm generally able to provide better results than the heuristic and metaheuristic algorithms, and often able to find an exact solution. The FW–ACO algorithm also shows a very efficient computational time, which makes it suitable for defining the route of pickers in real time. The FW–ACO algorithm is finally implemented in a real case study, where constraints exist on the order in which items should be picked, to show its practical usefulness and quantify the resulting savings.

Analytical models for the evaluation of deep-lane autonomous vehicle storage and retrieval system performance

In the last decades, huge efforts have been made to develop innovative solutions supporting the automation of logistics activities. Among the available technologies, autonomous vehicle storage and retrieval systems (AVS/RS) are very promising: they use light vehicles able to independently travel on different axes, and thus perform different tasks at the same time. Despite the increasing diffusion of such systems, there is still a lack of performance evaluation techniques. Furthermore, there are no standards available to provide performance evaluation criteria. In this paper, novel analytical models to assess the performance of AVS/RS pertaining to deep-lane racks. These models extend the state of the art by (i) taking into account the real criteria related to the storage and retrieval of items, (ii) considering the capability of the shuttle and the satellite to simultaneously perform different tasks, and (iii) evaluating the standard deviation of the cycle time, beside its average value. The presented models are validated through simulations performed on different warehouse layouts, in different deployment scenarios. The ultimate aim of such models is to support warehouse equipment designers in evaluating the performance of their systems, considering a multiplicity of realistic scenarios.

Order Batching and Picker Routing in manual order picking systems: the benefits of integrated routing

Order Batching and Picker Routing Problems arise in warehouses when items specified by customer orders have to be retrieved from their storage locations. The Order Batching Problem includes the grouping of a given set of customer orders into feasible picking orders such that the total length of all picker tours is minimized. In order to calculate the length of a picker tour, the sequence has to be determined according to which the items contained in the picking order will be picked. This problem is known as the Picker Routing Problem. Although quite sophisticated heuristics and even efficient exact solution approaches exist to the Picker Routing Problem in warehouse with up to two blocks, the routing problem does not get much attention when dealing with the Order Batching Problem. Instead, the order pickers are assumed to follow a certain, simple routing strategy when making their ways through the warehouse. The advantage of this approach can be seen in the fact that—in particular for single-block warehouse layouts—the corresponding picker tours are very straightforward and can be memorized easily by the order pickers. This advantage diminishes, however, when more complex, multi-block layouts have to be dealt with. Furthermore, in such case, the approach may result in picker tours which are far from optimal. For multi-block layouts, we integrate different routing algorithms into an iterated local search approach for the batching in order to demonstrate what the benefits are from solving the Order Batching and the Picker Routing Problem in a more integrated way. By means of numerical experiments it is shown that paying more attention to the Picker Routing Problem results in a substantial improvement of the solution quality without increasing computing times.

Estimating performance in a Robotic Mobile Fulfillment System

This paper models Robotic Mobile Fulfillment Systems and analyzes their performance. A Robotic Mobile Fulfillment System is an automated, parts-to-picker storage system where robots bring pods with products to a workstation. It is especially suited for e-commerce distribution centers with large assortments of small products, and with strong demand fluctuations. Its most important feature is the ability to automatically sort inventory and to adapt the warehouse layout in a short period of time. Queueing network models are developed for both single-line and multi-line orders, to analytically estimate maximum order throughput, average order cycle time, and robot utilization. These models can be used to quickly evaluate different warehouse layouts, or robot zoning strategies. Two main contributions are that the models include accurate driving behavior of robots and multi-line orders. The results show that: (1) the analytical models accurately estimate robot utilization, workstation utilization, and order cycle time, (2) maximum order throughput is quite insensitive to the length-to-width ratio of the storage area and (3) maximum order throughput is affected by the location of the workstations around the storage area.

Detailed design of fishbone warehouse layouts with vertical travel

In this paper, we provide a method to generate a three-dimensional detailed design of fishbone layouts. This method takes the desired storage capacity and returns the location (x,y,z) of each opening of the warehouse in such a way that the total operational cost – area cost and material handling cost – of the warehouse is minimal. We model the arrangement of the openings using mathematical finite sequences and represent a fishbone layout in terms of four primary characteristics. Next, we develop an algorithm that generates a detailed design of a fishbone layout given values of its four primary characteristics. Then, we present an optimization model that finds the values for the four primary characteristics that minimize the total operational cost of the warehouse. Finally, we solve the optimization model using a genetic algorithm. Our results suggest that in 91.74% of the cases, our optimization procedure reaches a near optimum point – deviated only by 0.587% – in a reasonable computational time (maximum 4.5min). This paper aims to diminish dependence upon experts and human decision making in the process of implementing a fishbone layout on greenfield projects, and fulfills an identified need of warehouse practitioners by integrating the most recent advances on non-traditional layouts and detailed warehouse design.

Incorporating human factors in order picking planning models: framework and research opportunities

Order picking (OP) activities, essential to logistics operations, are laborious and time-intensive. Humans are central actors in the OP process and determine both OP effectiveness and efficiency. Many researchers have developed models for planning OP activities and increasing the efficiencies of such systems by suggesting different warehouse layouts, OP routes or storage assignments. These studies have, however, ignored workers’ characteristics, or human factors, suggesting that they cannot be substantiated, which led to only partially realistic results. This paper proposes a conceptual framework for integrating human factors into planning models of OP activities and hypothesises that doing so improves the performance of an OP system and workers’ welfare. The framework is based on a systematic literature review that synthesises findings documented in the OP and human factors literature. The results of the paper may assist researchers and practitioners in designing OP systems by developing planning models that help in enhancing performance and reducing long-term costs caused by work-related inefficiencies.

A U-shaped layout for manual order-picking systems

In manual order-picking systems, order pickers walk or ride through a warehouse in order to collect items required by customers. When designing such a warehouse, two conflicting goals have to be considered, namely the maximization of space utilization and the minimization of the average length of the tours on which the requested items are collected. The achievement of these goals is significantly dependent on the layout of the warehouse. Whereas in a traditional warehouse layout, the picking aisles run in parallel to each other, the picking aisles in the warehouse layout presented here are arranged around a U-shaped central aisle. This type of layout, so-called U-shaped layout, provides a kind of compromise solution with respect to the above-mentioned goals. It has recently been implemented for spare-parts warehouses in practice, but has not been studied in the scientific literature so far. In this paper, we present a new routing strategy for the order pickers who operate in such a warehouse, and we derive an analytical expression for the expected tour length per picking order. By comparing this estimation with those of routing schemes for traditional warehouse layouts, it is demonstrated under which conditions such U-shaped layouts allow for operating warehouses more efficiently.

The Effect of Warehouse Layout Design on Order Picking Efficiency

In this paper the order picking problem in warehouses is considered, a topic which has received considerable attention from the international academic body in recent years. The order picking problem deals with the retrieval of order items from prespecified locations in the warehouse, and its objective is usually the minimization of travel time or travel distance. Hence, a well-thought order picking policy in combination with an appropriate storage policy will enhance warehouse efficiency and reduce operational costs. This paper starts with a literature overview summarizing approaches to routing order pickers, assigning stock-keeping units to pick locations and designing warehouse layouts. Since the layout design might affect both storage and routing policies, the three factors are interdependent with respect to order picking performance. To test these interdependencies, a simulation experiment was set up, involving two types of warehouse layout, four types of storage policy, five well-known heuristics and five sizes of order picking list. Our results illustrate that from the point of view of order picking distance minimization it is recommended to equip the warehouse with a third cross aisle, although this comes at the cost of a certain space loss. Additionally, we propose a set of most appropriate matches between order picking heuristics and storage policies. Finally, we give some directions for further research and recommend an integrated approach involving all factors that affect warehouse efficiency.

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.

Optimization of cube-per-order index warehouse layouts with zoning constraints

Zoning constraints impose co-location of the storage addresses assigned to individual items within the same storage area of a warehouse. The cube-per-order index (COI) rule for assigning items to locations cannot guarantee orderpicking cost minimization when zoning constraints are active. A heuristic procedure is presented for using the COI to generate an initial item assignment followed by an improvement step. The improvement step includes a random search phase followed by a simulated annealing step designed to avoid trapping at local minima. The procedure is illustrated in the context of a sample problem with 100 items stored in a facility with 1600 storage addresses distributed among 25 zones. Computational experimentation aimed at developing approximate guidelines for controlling the improvement step are reported.