Storage Location Assignment Problem Research Articles

Velocity-based rack storage location assignment for the unidirectional robotic mobile fulfillment system

Nowadays, the robotic mobile fulfillment system (RMFS) has been increasingly used by online retailers. Compared with traditional picker-to-parts warehouses, the racks of RMFS do not have to return to the same location after picking, thus we can dynamically change their locations, which brings great potential to efficiently fulfill orders. Aiming at minimizing the sum of rack travel distances, the key question is how to reassign a rack to an unoccupied storage location after picking items from the rack. However, the issue involves two challenges for unidirectional RMFS, one is that huge differences may exist between the classical Manhattan distance estimation and the actual distance for the unidirectional aisles in RMFS, and the other one is that we need to account for the frequency of rack moving for multi-item orders. We thus first propose closed-form formulas to optimally estimate the cycle travel distance for each rack. Then, by overcoming the repeated counting issue for multi-item orders, we propose a novel SKU (Stock Keeping Units)-correlation-based algorithm to choose high-velocity racks, which can better fulfill multi-item orders. Finally, embedding the cycle travel distance and SKU-correlation-based velocity, we propose a Velocity-based Rack Storage Location Assignment method (VRSLA) to solve the rack storage location assignment problem by assigning high-velocity racks to the nearest storage locations. Collaborating with a large online retailer in China, we demonstrate the performance of VRSLA by using both small-scale and large-scale datasets. The computational results show that VRSLA not only can achieve near-best solutions compared with an integer programming model solved by Gurobi, but also outperforms four state-of-the-art assignment methods in literature (random, velocity-based class, shortest path, and sale-based) by reducing the rack travel distance up to 43.32%. We also found that the stronger the correlation between SKUs on the racks or the larger the size of the RMFS, the shorter the rack travel distance by the proposed VRSLA method.

A model-driven decision support system for the storage location assignment problem in bulk ports

Yard storage management involves tactical and operational decisions affecting bulk port productivity. The main challenge is to ensure that bulk materials are available and stored according to physical and managerial constraints while minimizing storage space utilization. This article studies a new variant of the storage location assignment problem by considering contamination constraints for bulk materials. The problem is inspired by a real case study of Africa's largest fertilizer hub port. To tackle the problem, a model-driven decision support system, optimizing the storage space while satisfying vessels' demands and avoiding contamination from overlapping bulk materials, is developed. A mixed-integer linear programming model is formulated, and an efficient rolling-horizon heuristic is investigated as a solution approach. Extensive numerical experiments are performed, and the results confirm the efficiency of this model-driven support system from both theoretical and practical points of view.

Storage Assignment Using Nested Metropolis Sampling and Approximations of Order Batching Travel Costs

The Storage Location Assignment Problem (SLAP) is of central importance in warehouse operations. An important research challenge lies in generalizing the SLAP such that it is not tied to certain order-picking methodologies, constraints, or warehouse layouts. We propose the OBP-based SLAP, where the quality of a location assignment is obtained by optimizing an Order Batching Problem (OBP). For the optimization of the OBP-based SLAP, we propose a nested Metropolis algorithm. The algorithm includes an OBP-optimizer to obtain the cost of an assignment, as well as a filter which approximates OBP costs using a model based on the Quadratic Assignment Problem (QAP). In experiments, we tune two key parameters in the QAP model, and test whether its predictive quality warrants its use within the SLAP optimizer. Results show that the QAP model’s per-sample accuracy is only marginally better than a random baseline, but that it delivers predictions much faster than the OBP optimizer, implying that it can be used as an effective filter. We then run the SLAP optimizer with and without using the QAP model on industrial data. We observe a cost improvement of around 23% over 1 h with the QAP model, and 17% without it. We share results for public instances on the TSPLIB format.

Machine Learning in Warehouse Management: A Survey

Warehouse design and planning involve complex decisions on receiving, storage, order picking and shipping products (i.e., stock-keeping units - SKUs) and can affect the performance of entire supply chains. With the advancement of Industry 4.0 and increased data availability, high-computing power, and ample storage capacity, Machine Learning (ML) has become an appealing technology to address warehouse planning challenges such as Storage Location Assignment Problems (SLAP) and Order Picking Problems (OPP) for intelligent warehousing management. This paper presents a state-of-the-art review of ML applied to Warehouse Management Systems (WMS) through the analysis of recent research application articles. A mapping to classify the scientific literature in this new research area, including ML methods, algorithms, data sources and use cases of ML-aided WMS, as well as further research perspectives and challenges, are introduced. Preliminary results suggest that the possible research areas in ML-WMS are still incipient and need to be further explored.

Adaptive warehouse storage location assignment with considerations to order-picking efficiency and worker safety

ABSTRACT Smart warehouses require software-based decision-making tools to manage the receiving, storing, and picking of products. A major challenge in achieving efficient operations is deciding where to store products associated with incoming orders. The storage location assignment problem (SLAP) is more complex in large-size warehouses due to several functional objectives and numerous possible shelving solutions. This paper introduces an artificial intelligence algorithm that seeks to find an acceptable solution to SLAP with presented linear and nonlinear objective functions. The near-optimal technique exploits basin-hopping and simulated-annealing algorithms to find a solution when considering four functional objectives including worker safety, which has not been optimized using similar approaches. The algorithm is experimentally evaluated, and results demonstrate that reasonablely achieved solutions are comparable to those obtained by well-known existing solvers. Furthermore, the problem could be solved with non-linear objectives which is beyond the commercial solvers’ like SCIP capability.

The storage location assignment problem of automated drug dispensing machines

Drug storage is an important issue in the pharmacy. This paper studies the drug storage location assignment problem of automated drug dispensing machines to improve the space utilization and improve the efficiency of machines. Based on the demand for drugs in the pharmacy, the storage space occupied by various drugs in the automated drug dispensing machines is solved. To prevent the drugs in one prescription from being picked across machines, the drugs are grouped based on the Jaccard similarity coefficient. Finally, the assignment strategy based on the demand frequency of drugs is proposed. Numerical experiments verify the effectiveness of the proposed method. The results of this paper provide a theoretical basis for the drug storage location assignment of automated drug dispensing machines in smart pharmacies.

A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations

The use of simulation and reinforcement learning can be viewed as a flexible approach to aid managerial decision-making, particularly in the face of growing complexity in manufacturing and logistic systems. Efficient supply chains heavily rely on steamlined warehouse operations, and therefore, having a well-informed storage location assignment policy is crucial for their improvement. The traditional methods found in the literature for tackling the storage location assignment problem have certain drawbacks, including the omission of stochastic process variability or the neglect of interaction between various warehouse workers. In this context, we explore the possibilities of combining simulation with reinforcement learning to develop effective mechanisms that allow for the quick acquisition of information about a complex environment, the processing of that information, and then the decision-making about the best storage location assignment. In order to test these concepts, we will make use of the FlexSim commercial simulator.

An efficient data-driven method for storage location assignment under item correlation considerations

This research investigates the storage location assignment problems of correlated-items under a realistic multiple-cross-aisle warehouse setting. To accommodate the shortest picking route decision and update customer orders in each replenishment cycle to capture the changing trend in customer preferences. An item-correlations considered fitness function is developed to evaluate the benefit of exchanging item locations and minimise the picking costs. A data-driven storage location assignment method called storage location assignment for correlated-item method is proposed to improve the order picking efficiency. The explicit considerations make this work distinct from existing studies: (1) correlation among items in customer orders, (2) penalty for crossing-aisles in warehouse traffic, and (3) real retail dataset adopted. Our method considers the effect of correlated items in customer orders, through a storage exchange benefit function to evaluate the fitness of storage location to minimise warehouse operation costs and enhance operation efficiency. With a real ecommerce dataset, the numerical study results show that our method can reduce the travelling distance by 5–10% compared with a conventional turnover-based storage policy. Our method not only outperforms in terms of travel distance. The picking time improvement is even more significant for large warehouse if a moderate penalty for crossing-aisles is considered.

Correlated storage assignment approach in warehouses: A systematic literature review

Purpose: Correlation-based storage assignment approach has been intensively explored during the last three decades to improve the order picking efficiency. The purpose of this study is to present a comprehensive assessment of the literature about the state-of-the-art techniques used to solve correlated storage location assignment problems (CSLAP).Design/methodology/approach: A systematic literature review has been carried out based on content analysis to identify, select, analyze, and critically summarize all the studies available on CSLAP. This study begins with the selection of relevant keywords, and narrowing down the selected papers based on various criteria.Findings: Most correlated storage assignment problems are expressed as NP-hard integer programming models. The studies have revealed that CSLAP is evaluated with many approaches. The solution methods can be mainly categorized into heuristic approach, meta-heuristic approach, and data mining approach. With the advancement of computing power, researchers have taken up the challenge of solving more complex storage assignment problems. Furthermore, applications of the models developed are being tested on actual industry data to comprehend the efficiency of the models.Practical implications: The content of this article can be used as a guide to help practitioners and researchers to become adequately knowledgeable on CSLAP for their future work.Originality/value: Since there has been no recent state-of-the-art evaluation of CSLAP, this paper fills that need by systematizing and unifying recent work and identifying future research scopes.

A Discrete-Event Simheuristic for Solving a Realistic Storage Location Assignment Problem

In the context of increasing complexity in manufacturing and logistic systems, the combination of optimization and simulation can be considered a versatile tool for supporting managerial decision-making. An informed storage location assignment policy is key for improving warehouse operations, which play a vital role in the efficiency of supply chains. Traditional approaches in the literature to solve the storage location assignment problem present some limitations, such as excluding the stochastic variability of processes or the interaction among different warehouse activities. This work addresses those limitations by proposing a discrete-event simheuristic framework that ensures robust solutions in the face of real-life warehouse conditions. The approach followed embraces the complexity of the problem by integrating the order sequence and picking route in the solution construction and uses commercial simulation software to reduce the impact of stochastic events on the quality of the solution. The implementation of this type of novel methodology within a warehouse management system can enhance warehouse efficiency without requiring an increase in automation level. The method developed is tested under a number of computational experiments that show its convenience and point toward future lines of research.

Dynamic Storage Location Assignment in Warehouses Using Deep Reinforcement Learning

The warehousing industry is faced with increasing customer demands and growing global competition. A major factor in the efficient operation of warehouses is the strategic storage location assignment of arriving goods, termed the dynamic storage location assignment problem (DSLAP). This paper presents a real-world use case of the DSLAP, in which deep reinforcement learning (DRL) is used to derive a suitable storage location assignment strategy to decrease transportation costs within the warehouse. The DRL agent is trained on historic data of storage and retrieval operations gathered over one year of operation. The evaluation of the agent on new data of two months shows a 6.3% decrease in incurring costs compared to the currently utilized storage location assignment strategy which is based on manual ABC-classifications. Hence, DRL proves to be a competitive solution alternative for the DSLAP and related problems in the warehousing industry.

Current studies on storage location assignment problem

Although operations seem more important than everything in a business operation, warehouse management has great importance to the success of a business operation. Storage location assignment is very important because of the uncertainty in the number of incoming products, the necessity of answering unknown customers’ demands and the speed rate of response to customer demands. Besides, several factors affect warehouse management. Assigning products to appropriate locations is one of the most important and complex. This study aims to present recent academic works on the storage location assignment problem published from 2018 to 2021. These problems are assigned accordingly. Methods of solution, goals and some related considerations are considered while investigating the works. Recommendations are provided in the conclusion. Based on the reviewed articles, warehouse capacity, market, products, logistics resources and operational configuration are taken into account as restrictions or considerations. Keywords: Location, logistics, storage, warehouse management;

The Storage Location Assignment Problem of Automated Drug Dispensing Machines

The Storage Location Assignment Problem of Automated Drug Dispensing Machines

Smart stacking for import containers using customer information at automated container terminals

Motivated by the practical import free-flow program that aims to expedite the container retrieval process, we conceptualize a new container stacking strategy, termed Smart Stacking (SS) strategy. The SS strategy aims to create relocation-free stacks (smart stacks) by utilizing customer information. The Storage Location Assignment Problem (SLAP) under the SS strategy is addressed. The problem is to determine the smart customers/containers, and the number and locations of smart stacks, when assigning a batch of import containers to a yard block at an automated container terminal to minimize the total retrieval time. Two variants of SLAP are investigated under the non-split policy and the split policy, depending on whether the containers from the same customer are allowed to be split between smart stacks and non-smart stacks. For the non-split variant, a mixed-integer programming (MIP) model is formulated first. By analyzing the properties of the optimal solution, an improved formulation with enhanced computational performance is then proposed. Based on the structure of the model, a divide-and-conquer heuristic is designed to solve the non-split variant more efficiently. For the split variant, a MIP model under the optimal partitions of the non-split model is developed. We theoretically prove that the split variant yields better results than the non-split variant. Extensive experiments are carried out to illustrate the effectiveness of smart stacking. It is found that customer information and yard utilization rate have a significant influence on the effectiveness of smart stacking.

Data-driven order correlation pattern and storage location assignment in robotic mobile fulfillment and process automation system

With the rapid development and implementation of ICT, academics and industrial practitioners are widely applying robotic process automation (RPA) to enhance their business processes and operational efficiencies. This paper intends to address the value creation of utilizing RPA under the cloud-based Cyber-Physical Systems (CPS) in Robotic Mobile Fulfillment System (RMFS). By providing a TO-BE analysis of RPA and cloud-based CPS framework, a data-driven approach is proposed for zone clustering and storage location assignment classification in RMFS. The purpose of the paper is to gain better operational efficiency in RMFS. A modified A* algorithm is adopted for calculating the total traveling cost of each moveable rack in the case company layout. Nine common clustering algorithms are applied for the RMFS’s zone clustering. The results from the zone clustering are considered as nine scenarios for data-driven order classification to solve the storage location assignment problem. Six common classification algorithms are applied for a detailed comparison which has been conducted with thousands of orders. The results reveal that K-means, Gaussian Mixture Models, and Bayesian Gaussian Mixture Model are worked well with six supervised classification algorithms which yield an average of 95% accuracy rate and a higher customers’ expectation can be achieved under the customer-driven e-commerce economy.

Ramping up a heuristic procedure for storage location assignment problem with precedence constraints

The retail industry is becoming increasingly competitive; as a result, companies are seeking to reduce inefficiencies in their supply chains. One way of increasing the efficiency of operations inside a warehouse is by better allocating products in the available spaces. In this paper, we propose a new heuristic approach to solving the storage location assignment problem (SLAP) considering precedence constraints, in multi-aisle, multi-product picking warehouses. A two-phase heuristic procedure is developed: the products are clustered and assigned to the available spaces. We tested the procedure in the non-perishables warehouse of a real-world Portuguese retail chain, which supplies 191 stores per day. The results show that the new assignment of products allows for an improvement of up to 15% on the distance travelled by the pickers, which implies savings of approximately 477 km per month. This problem is a special case of SLAP since we are dealing with large percentages of non-uniform products. This procedure incorporates four relevant criteria for the allocation decision: the products’ similarity, demand and weight, and the distance travelled by the picker. By using a two-phase heuristic method, this study offers companies and academics an alternative and more effective solution for SLAP than the usual methods based on the creation of density zones.

Warehouse scheduling in airport freight station with multi-objective artificial bee colony algorithm

To improve the efficiency and safety of automated warehouse, a new scheme is proposed to solve the problem of space allocation considering path planning, different weight as well as the inbound and outbound frequency of cargoes. First of all, the storage location assignment problem was induced as a constraint multi-objective optimisation model whose aims are minimising the gravity centre of shelf and the frequency of inbound and outbound. Then, multi-objective artificial bee colony algorithm and multi-objective particle swarm optimisation algorithm were proposed and the performance was tested on the benchmark functions as well as the problem of storage space allocation. The results show that the proposed strategies effectively solve the problem of optimisation of storage space allocation and realise the efficient operation of the warehouse.

Warehouse scheduling in airport freight station with multi-objective artificial bee colony algorithm

To improve the efficiency and safety of automated warehouse, a new scheme is proposed to solve the problem of space allocation considering path planning, different weight as well as the inbound and outbound frequency of cargoes. First of all, the storage location assignment problem was induced as a constraint multi-objective optimisation model whose aims are minimising the gravity centre of shelf and the frequency of inbound and outbound. Then, multi-objective artificial bee colony algorithm and multi-objective particle swarm optimisation algorithm were proposed and the performance was tested on the benchmark functions as well as the problem of storage space allocation. The results show that the proposed strategies effectively solve the problem of optimisation of storage space allocation and realise the efficient operation of the warehouse.

Defining a storage-assignment strategy for precedence-constrained order picking

This paper proposes a zero-one quadratic assignment model for dealing with the storage location assignment problem when there are weight constraints. Our analysis shows that operations can be improved using our model. When comparing the strategy currently used in a real-life company with the designed model, we found that the new placement of products allowed a reduction of up to 22% on the picking distance. This saving is higher than that achieved with the creation of density zones, a procedure commonly used to deal with weight constraints, according to the literature.

Data driven storage location assignment problem considering order picking frequencies: A heuristic approach

Data driven storage location assignment problem considering order picking frequencies: A heuristic approach