Storage Assignment Research Articles

Integration of single and dual command operations in non-traditional warehouse design

Non-traditional warehouses rise as effective solutions to shorten the travelled distances to store and retrieve unit loads, adding aisles crossing the parallel racks. Multiple warehouse configurations are proposed by the literature discussing the enhancements toward standard layouts. In previous contributions, the authors introduced the diagonal cross-aisle model, concluding about its positive impact on the handling performances under single command operations. This paper extends the previous works, integrating dual command operations, through an original analytic model supporting the design of non-traditional warehouses with a couple of symmetric straight diagonal cross-aisles and random storage assignment strategy. The closed-form expressions to compute the expected cycle travel distances are provided, optimising the aisle position. An industrial case study applies the model, getting distance savings ranging from 11 to 17%, compared to standard layout and further considering the loss of storage space due to the presence of the additional aisles.

Using a heuristic multi-objective genetic algorithm to solve the storage assignment problem for CPS-based pick-and-pass system

ABSTRACT With the advancement of AI and the widespread application of IoT and cloud computing, the pick-and-pass system (PKPS) can potentially be transformed into a cyber-physical system (CPS) based intelligent warehouse picking system. This paper proposes a CPS-based PKPS with a heuristic multi-objective genetic algorithm to solve the NP-hard storage assignment problem (SAP) for order picking operations in an e-commerce-based warehouse. The proposed algorithm considers both the workload balance between picking lines and emergency replenishment during picking operation. Finally, the study shows that the proposed algorithm is effective in improving the efficiency of picking operations based on software simulation.

Genetic algorithm based approaches to solve the order batching problem and a case study in a distribution center

The order batching problem is a combinatorial optimization problem that arises in the warehouse order picking process. In the order batching problem, the aim is to find groups of orders and picking routes of these groups to minimize distance travelled by the order picker. This problem is encountered especially in manual order picking systems where the capacity of picking vehicle is limited. Solving the order batching problem becomes more important when the size of the problem (e.g. number of storage locations, number of aisles, number of customer orders, etc.) is large. The content of the batch and picking route affect the retrieval-time of the orders. Therefore, an effective batching and routing approach is essential in reducing the time needed to collect ordered items. The main objective of this study is to develop fast and effective metaheuristic approaches to solve the order batching problem. For this purpose, two genetic algorithm based metaheuristic approaches are proposed. The numerical test of the proposed algorithms is performed with generated data sets. The proposed methods are thought to be useful to solve real-life problems in different warehouse configurations. Accordingly, a real case study is conducted in the distribution center of a well-known retailer in Turkey. The case study includes the storage assignment process of incoming products. The results demonstrate that developed algorithms are practical and useful in real-life problems.

Improving picking performance at a large retailer warehouse by combining probabilistic simulation, optimization, and discrete‐event simulation

AbstractDistribution warehouses are a critical part of supply chains, representing a nonnegligible share of the operating costs. This is especially true for unautomated, labor‐intensive warehouses, partially due to time‐consuming activities such as picking up items or traveling. Inventory categorization techniques, as well as zone storage assignment policies, may help in improving operations, but may also be short‐sighted. This work presents a three‐step methodology that uses probabilistic simulation, optimization, and event‐based simulation (SOS) to analyze and experiment with layout and storage assignment policies to improve the picking performance. In the first stage, picking performance is estimated under different storage assignment policies and zone configurations using a probabilistic model. In the second stage, a mixed integer optimization model defines the overall warehouse layout by selecting the configuration and storage assignment policy for each zone. Finally, the optimized layout solution is tested under demand uncertainty in the third, final simulation phase, through a discrete‐event simulation model. The SOS methodology was validated with three months of operational data from a large retailer's warehouse, successfully illustrating how it may be successfully used for improving the performance of a distribution warehouse.

Assignment of duplicate storage locations in distribution centres to minimise walking distance in order picking

With the rapid development of e-commerce, the orders processed in B2C warehouses are characterised by heterogeneous and small volume. The traditional storage assignment strategies used in the picker-to-parts warehouses do not have advantage any more. In this case, the scattered storage strategy is a good alternative. In this paper, we study a new scattered storage strategy that allows the same product to be placed in multiple storage locations. The correlation between products which reflects how frequently any two products will be ordered together in the same order is considered. The problem is formulated as a 0-1 integer programming model to minimise the weighted sum of distances between the products, with weight being the elements of the correlation matrix. To solve large-scale problems, a GA and a basic PSO algorithm are developed. To improve solution quality, a new PSO algorithm based on the problem characteristic is designed and a hybrid algorithm combing it with GA is proposed. Experiments show that the solutions of these algorithms are close to the optimal solutions for the small-sale problems. For larger problems, the specially designed new PSO greatly improves solution quality as compared to the basic algorithms and the hybrid algorithm makes further improvement.

Warehousing process performance improvement: a tailored framework for 3PL

PurposeThird-party logistic providers (3PLs) continuously strive for controlling and improving their performances to gain a competitive advantage. The challenging environment where they operate is affected by high variety in type and number of clients, the inventory mix and the demand profiles they have to meet. Consequently, better understanding the dynamics of warehousing operations and the characteristics of the inventory mix is critical to handle such a complexity.Design/Methodology/approachThis paper proposes a decision-support framework, suited for 3PL warehouse practitioners, that aids to design and implement effective and affordable activities for measuring and improving the warehousing performances. Such goal is pursued by the framework by leading the managers through an initial mapping and diagnosis of the system, then by developing a tailored measurement system to track the performance, paving the way to the identification of the criticalities and the potential improvement scenarios.FindingsThis paper presents a case study on the implementation of the proposed framework at a warehouse of an Italian 3PL provider to introduce a new storage assignment policy and reduce the travelling time for order picking. Furthermore, the paper exemplifies how the framework contributes to enhance the awareness of managers on warehousing operations and the involvement of the personnel throughout the improvement process.Practical implicationThe proposed framework can be implemented by operations managers of 3PL warehouses who want to pursue general performance improvement projects. With respect to the case study, this framework contributes to identify the storage assignment policy that reduces the travelling for order picking in the observed warehouse of 8 percent in a month but is intended to address to even other areas of improvement in 3PL warehousing environments.Originality/valueInstead of focusing on the proper methods and models that optimize a specific task or performance indicator, it provides a general framework that leads the managers through the decisional process, from the preliminary diagnosis of the system, to its benchmarking, towards the implementation of corrective and improving solutions.

A Text-Granulation Clustering Approach With Semantics for E-Commerce Intelligent Storage Allocation

With the hot rise and increasing popularity of e-commerce and online shopping, exponentially growing orders require to be picked up timely. In the order-based warehouse picking process, more intelligent optimization methods are urgently demanded to meet the maturity of robot technology. Due to the wide variety of SKUs (stock-keeping units) in warehouses, direct analysis based on orders can hardly get strong correlations among SKUs. Since orders and SKUs usually are presented in text form files, this paper proposes a clustering optimization algorithm based on text information granules which makes full use of context information and semantics. Storage assignment is determined by clustering SKUs first and then performing correlation analysis on the clustering results according to orders. The algorithm can give the semantic description of each cluster so that the clustering process is interpretable and transparent. Extensive experiments on a distribution center of a clothing production enterprise indicate 40.5 to 57.9% improvement in the average order picking distance compared with the ABC classification.

Improving order-picking operation through efficient storage location assignment: A new approach

Fast development of online retail industry requires customer orders to be fulfilled within tight windows, where order-picking, the most time-consuming and labor-intensive activity in warehouses, plays an important role. One of the basic ways to improve order-picking operation is assigning storage locations to appropriate items. The storage location assignment problem is in general NP-hard and is mainly solved by heuristics which usually suffer from limited solution quality or high computational effort, especially for large scale problems. In literature, most studies make the storage assignment decisions according to item properties, such as turnover or correlation, which are statistically extracted from item orders. These storage methods follow a data→concept→assign decision mechanism and may ignore useful data characteristics that are not conceptualized. This paper presents a new approach to improve the order-picking operation, which directly uses item orders to make the decisions without any statistical treatments, i.e., following a data→assign mechanism. The concept of good move pair is introduced to quickly find a better assignment through directly exploiting data characteristics of item orders, and an iterative algorithm is developed to minimize the total travel distance. We evaluate the algorithm on real data and numerical instances, and compare its performance with extant methods in the literature. The results show that the proposed method significantly outperforms other methods in most cases. We also extend the algorithm to the case of high-level warehouses and examine its effectiveness.

Fusion of dedicated and shared storage to maximize the use of space of static warehouses

ABSTRACTThe shared storage assignment policy, contrary to the dedicated one, allows a high use of space. However, by using a dedicated storage the order pickers become familiar with product locations. Thus, we propose in this paper the fusion of dedicated and shared storage for assigning to each product the optimum location relative to the product and to the location assignment system. To make the fusion of dedicated and shared storage, we have considered several critical sections as shared sections whose access is managed by a product-driven system. The new storage policy was applied to manage the locations assignment in a steel coil warehouse. As a result, we have validated the optimization of the use of space.

Optimising the storage assignment and order-picking for the compact drive-in storage system

One of the most common systems in non-automated warehouses, is drive-in pallet racking with a shared storage policy (which is usually based on the duration-of-stay). Such scheme targets towards an efficient use of storage space, since its operation costs are directly related to the size and layout of the warehouse. In this paper, two mathematical programming models and two greedy-randomised based heuristics for finding (nearly) optimal storage and retrieval operation sequences for this type of storage system are proposed. The computational effectiveness of the proposed approaches is measured by considering two sets of synthetic instances. The obtained results show that the proposed heuristics are not only able to compute high-quality solutions (as observed when being compared with the optimal solutions attained by the mathematical programming models), but it is also capable of providing solutions in very short running times even for large instances for which the mathematical programming model failed to find feasible solutions. At the light of these results, the best heuristic is also tested using a rolling-horizon planning strategy in a real-world case study, obtained from a Chilean company. It turns out that the attained results are more effective than the company's current storage policy.

Trolley line picking: storage assignment and order sequencing to increase picking performance

Trolley line picking is a special warehousing system particularly suited to fulfill high-volume demands for heavy stock keeping units (SKUs). In such a system, unit loads of SKUs are positioned along a given path passed by automated trolleys, i.e., carriers hanging from a monorail or automated guided vehicles. Once a trolley reaches a requested SKU, it automatically stops and announces the requested items on a display. This is the signal for an accompanying human picker to put the demanded items onto the trolley. In this way, picking continues until, at the end of the path, the current picking order is complete and the trolley moves onward to the shipping area. Meanwhile, the picker rushes back to meet the subsequent trolley associated with the next picking order. The picking performance of the trolley line system is mainly influenced by the picker’s unproductive walking from SKU to SKU during order processing and back to the next trolley when switching to the next order. In this paper, we investigate how the storage assignment of SKUs along the path and the order sequence influence picking performance. Specifically, we explore the positive effect of duplicating SKUs and storing them at multiple positions along the path. We formulate the interdependent storage assignment and order sequencing problems and introduce a decomposition heuristic. Our computational study investigates the solution performance of this procedure and shows that SKU duplication can considerably improve picking performance.

Two-stage storage assignment to minimize travel time and congestion for warehouse order picking operations

This research presents a systematic and integrated approach that extends the correlated storage assignment strategy to improve the efficiency of warehouse order picking operations. The correlated storage assignment can reduce a significant amount of travel costs, but could lead to traffic congestion due to the imbalanced traffic flow. Hence, this research proposes the correlated and traffic balanced storage assignment (C&TBSA) to minimize the travel time and picking delays, which is modeled in two stages: clustering and assignment. In the clustering stage, a bi-objective optimization model is formulated to group items with the consideration of both travel efficiency and traffic flow balance, which is solved using multi-objective evolutionary algorithms (MOEAs). In the assignment stage, items in each cluster are distributed to the available storage locations. C&TBSA is evaluated with an actual warehouse case study and the results show that C&TBSA outperforms random, class-based, and correlated storage assignment methods by 48.74%, 23.82%, and 7.58% respectively, regarding the total time consisting of travel time and picking delays.

Ergonomic and economic optimization of layout and item assignment of a U-shaped order picking zone

Order picking is considered one of the most labor- and cost-intensive warehouse operating processes. Regularly, order pickers are exposed to severe physical demands, which increase the likelihood for developing muscular-skeletal disorders, such as lower back pain. Muscular-skeletal disorders cause significant compensation, recovery and deficiency costs and are expected to gain further importance in the future due to an aging workforce. Both companies and workers may therefore benefit from decision support models that explicitly take ergonomics aspects into account. The work at hand investigates a warehouses where the storage area is divided into zones with shelves in each zone arranged in the shape of a U. For this warehouse, we determine an optimal configuration of the U-zone’s layout as well as an optimal assignment of products to storage locations. We depart from prior research by considering both the minimization of the total travel distance as well as the minimization of the total ergonomic strain workers are exposed to. Both optimization problems are formalized as mixed-integer programs. An exact polynomial-runtime solution procedure, suitable for both objectives, is developed. Using this solution procedure, we illustrate how the relevant ergonomic strains can be quantified to apply them to our optimization model. Computational studies illustrate the efficacy of our proposed solution procedure. Optimal layouts and storage assignments significantly reduce the walking distance and ergonomic strain during order picking. Additionally, both objectives are only marginally conflicting, such that, mutually, an optimal solution for one objective is also a close-to-optimal solution for the other. We finally derive insights on the optimal layout and storage assignment for future research and practical application.

Digital twin-driven joint optimisation of packing and storage assignment in large-scale automated high-rise warehouse product-service system

ABSTRACT Current mass individualisation and service-oriented paradigm calls for high flexibility and agility in the warehouse system to adapt changes in products. This paper proposes a novel digital twin-driven joint optimisation approach for warehousing in large-scale automated high-rise warehouse product-service system. A Digital Twin System is developed to aggregate real-time data from physical warehouse product-service system and then to map it to the cyber model. A joint optimisation model on how to timely optimise stacked packing and storage assignment of warehouse product-service system is integrated to the Digital Twin System. Through perceiving online data from the physical warehouse product-service system, periodical optimal decisions can be obtained via the joint optimisation model and then fed back to the semi-physical simulation engine in the Digital Twin System for verifying the implementation result. A demonstrative prototype is developed and verified with a case study of a tobacco warehouse product-service system. The proposed approach can maximise the utilisation and efficiency of the large-scale automated high-rise warehouse product-service system.

Throughput models for a dual-bay VLM order picking system under different configurations

Purpose Vertical lift module (VLM) is a parts-to-picker system for order picking of small products, which are stored into two columns of trays served by a lifting crane. A dual-bay VLM order picking (dual-bay VLM-OP) system is a particular solution where the operator works in parallel with the crane, allowing higher throughput performance. The purpose of this paper is to define models for different operating configurations able to improve the total throughput of the dual-bay VLM-OP system. Design/methodology/approach Analytical models are developed to estimate the throughput of a dual-bay VLM-OP. A deep evaluation has been carried out, considering different storage assignment policies and the sequencing retrieval of trays. Findings A more accurate estimation of the throughput is demonstrated, compared to the application of previous models. Some use guidelines for practitioners and academics are derived from the analysis based on real data. Originality/value Differing from previous contributions, these models include the acceleration/deceleration of the crane and the probability of storage and retrieve of each single tray. This permits to apply these models to different storage assignment policies and to suggest when these policies can be profitably applied. They can also model the sequencing retrieval of trays.

Velocity‐Based Storage Assignment in Semi‐Automated Storage Systems

Our research focuses on the storage decision in a semi‐automated storage system, where the inventory is stored on mobile storage pods. In a typical system, each storage pod carries a mixture of items, and the inventory of each item is spread over multiple storage pods. These pods are transported by robotic drives to stationary stations on the boundary of the storage zone where associates conduct pick or stow operations. The storage decision is to decide to which storage location within the storage zone to return a pod upon the completion of a pick or stow operation. The storage decision has a direct impact on the total travel time and hence the workload of the robotic drives. We develop a fluid model to analyze the performance of velocity‐based storage policies. We characterize the maximum possible improvement from applying a velocity‐based storage policy in comparison to the random storage policy. We show that class‐based storage with two or three classes can achieve most of the potential benefits and that these benefits increase with greater variation in the pod velocities. To validate the findings, we build a discrete‐time simulator with real industry data. We observe an 8% to 10% reduction in the travel distance with a 2‐class or 3‐class storage policy, depending on the parameter settings. From a sensitivity analysis, we establish the robustness of the class‐based storage policies as they continue to perform well under a broad range of warehouse settings, including different zoning strategies, resource utilization, and space utilization levels.

Enhancing the order picking process through a new storage assignment strategy in forward-reserve area

This paper reexamines the order picking process in a warehouse facing the challenges that e-commerce brings about and which are characterised by a very large number of small sized orders and returns. Implementing effective storage assignment strategies combined with efficient batching, in this context, is very fundamental to keep the warehouse’s responsiveness and order completion times up to the standards. This paper investigates a new storage assignment strategy, initially implemented by a large shoes and footwear wholesaler, to enhance the performance of the order picking process in its warehouses. The impact of this new storage assignment strategy on the performance of the order picking process is analysed via simulation. The performance of the system, measured in terms of total picking time as well as order lead time while taking congestion into account, is compared to the cases where conventional storage assignment strategies are implemented. A full factorial design is set up and the simulation output is statistically analysed. The results of this analysis are reported and thoroughly discussed. Attending to the results, the proposed strategy presents a remarkable potential to shorten total picking travel distances and order completion time and consequently customer satisfaction.

New model of the storage location assignment problem considering demand correlation pattern

Order-picking is the most time- and labor-consuming operation in a warehouse and significantly influences supply chain efficiency. One of the basic methods for improving order-picking efficiency involves assigning storage locations to appropriate items, i.e., the storage location assignment problem (SLAP). In existing studies, most storage assignment methods only consider the properties of individual item rather than the item groups that are usually collectively required. This paper introduces the concept of the demand correlation pattern (DCP) to describe the correlation among items, based on which a new model is constructed to address the SLAP. The model is subsequently reduced using the S-shape routing strategy, and a method for determining DCPs from historical data is proposed. To solve the model, a heuristic and a simulated annealing method are developed. The proposed methods are examined and compared extant methods using both real data collected from an online retailer and numerical instances that are randomly generated. The computational results are discussed.

Adaptive Storage Reassignment in Order Picking Systems to Picker Learning and Change of Demand

In today’s logistical environment, companies must quickly deliver many kinds of products to customers, owing to intensifying competition, shortening product lifecycle. Mid-supply-chain logistical warehouses play an important role of adjusting the difference between manufacturer supply and customer demand. At the warehouses, most of the work comprises picking. Thus, it is imperative to make the picking process more efficient. The working time for picking is determined by a combination of elements, such as warehouse layout, storage assignment, order batching, and routing. And, adaptive storage assignments to change of demand have been effective. In practice, if the storage assignment changes, the picker will have a learning curve negatively affecting picking time. A model handling such effects has already been proposed. However, it is assumed all of the picker learning is reset. This is impractical. Our study proposes a new model in which the picker only relearns the shelf for which a storage assignment has been changed. Furthermore, pickers’ learning grows with the number of storage-shelf accesses, the degree of picker’s learning affect the probability of picking errors and search time. Our study specifies the effectiveness of the storage-shelf learning method for handling adaptive storage assignments.

Unit-load Storage Assignment Problem for Warehouse Stacking Pallets

Unit-load Storage Assignment Problem for Warehouse Stacking Pallets