Location Assignment Problem Research Articles

Conceptual Framework for Adaptive Bacterial Memetic Algorithm Parameterization in Storage Location Assignment Problem

Recognized as an NP-hard combinatorial challenge, Storage Location Assignment Problem (SLAP) demands heuristic or algorithmic solutions for effective optimization. This paper specifically examines the enhancement of SLAP through the utilization of evolutionary algorithms, as they are particularly suitable for complex cases. Among others, the genetic algorithm (GA) is typically applied to solve this problem. This paper investigates the Bacterial Memetic Algorithm (BMA) as a possible solution for optimization. Though the comparative analysis of the BMA with the previously well-used GA algorithm under certain test parameters reveals that BMA is suitable for SLA optimization, BMA failed to achieve better results. We attribute the unsatisfactory results to the parameter settings, as illustrated by a few specific examples. However, the complexity of the problem and the parameterization does not allow for continuous manual parameter adjustment, which is why we have identified the need for a concept that automatically and adaptively adjusts the parameter settings based on the statistics and fitness values obtained during the execution. The novelty of this paper is to specify the concept of adaptive BMA parameterization and rules.

Data-driven simulation methodology for exploring optimal storage location assignment scheme in warehouses

Storage location assignment effects the efficiency of order picking, and thus it is significant for improving operation management in warehouses. Due to the dynamic nature of order picking, system simulation has been recently used for solving Storage Location Assignment Problem (SLAP). However, most previous studies aimed at using simulation to evaluate and verify the optimum storage schemes obtained from mathematical programming, which belongs to “optimize first and then simulation” in essence. In this context, we proposed a novel data-driven simulation methodology for solving SLAP in this study. A four-quadrant judgment criterion is proposed to guide storage locations assignment based on correlation and turnover frequency of items. Moreover, trajectory and blocked heatmaps are involved for exploring the contribution of different factors to order picking efficiency through scenario experiments. A prototype simulator was then developed and applied in a case study to demonstrate its utility. The results obtained reveals the fact that congestion blocking is another key factor impacting global order picking efficiency, and thus considering the correlation between items in storage schemes designing may not always improve the system performance due to the accompanying increases of blocking intensity during the dynamic order picking processes. The findings also demonstrate our inference that the priority of turnover frequency-based storage policy should be higher than correlation between items in designing storage schemes. This work provides new perspectives and valuable demonstration on the evaluating the contribution of different factors to global order picking efficiency as well as exploring optimal storage schemes through emerging data-driven simulation paradigm.

Integrating storage allocation with manual order picking and replenishment operations in a distribution centre

ABSTRACT This paper introduces a mathematical programming formulation and a simulation-based heuristic for the allocation of storage positions to products picked by human operators on man-aboard vehicles traveling through the warehouse of a wholesale company. In this problem, the tactical level of the assignment decisions affects the operational level of the picking process. We propose a simulation-optimisation framework that integrates the two. Our formulation of the storage location assignment problem also handles the constraint according to which a picking position should be paired with a (vertical) replenishment position for a given item. To solve realistic instances, we design an iterated local search (ILS) metaheuristic with an embedded discrete-event simulator (DES) that evaluates the most promising moves at each iteration. The DES allows reproducing the handling operations performed by multiple order pickers under uncertainty, mutual interferences and congestion-related phenomena. Overall, the flexible simulation-optimisation (SO) framework evaluates the operational times and daily productivity of the order picking organisation. Numerical results are presented for real data, under an S-shape picking policy with a skip-and-go rule to deal with lacking items. Under a proper tuning of the ILS parameters, the SO framework allows to achieve a nearly 17% improvement in warehouse productivity.

Solving the storage location assignment of large-scale automated warehouse based on dynamic vortex search algorithm

This paper establishes the mathematical model for Storage Location Assignment (SLA) problem in large-scale automated warehouses by combining three objectives: efficiency, shelf stability, and stacker load balancing. Along with a novel repair strategy to handle the complex constraints of large-scale problems. Additionally, a coding method and solution approach suitable for practical application scenarios are developed. In order to solve large-scale SLA problem, an improved vortex search algorithm is proposed based on attraction operation in flow field, dimension-by-dimension dynamic radius and leadership decision-making mechanism (FDVSA). In the experimental part, the algorithm effectiveness experiment of FDVSA was first conducted using the large-scale global optimization test sets IEEE congress on evolutionary computation 2010 and 2013 (CEC2010, CEC2013). The results show that: (1) Compared with other comparison algorithms, the comprehensive average optimization rate of FDVSA in CEC2010 and CEC2013 is 88 % and 78 %, respectively. (2) The experimental results of FDVSA showed that each improvement strategy has advantages in dealing with large-scale problems. (3) The post-hoc analysis showed that there are significant differences between FDVSA and other comparison algorithms, and FDVSA is significantly better. Finally, FDVSA and other comparison algorithms are solved on three different scale and complexity of SLA cases. The results show that: (1) FDVSA has significant advantages in solving large-scale SLA problem, and the comprehensive average optimization rate is 19 %. (2) The convergence curve and boxplot showed that FDVSA has good convergence speed and solving stability. (3) The effectiveness of the repair strategy was verified by experiments in the large-scale SLA problems.

A novel framework for production planning and class-based storage location assignment: Multi-criteria classification approach

Efficient warehouse management is essential for optimizing inventory, minimizing transportation costs, and enhancing overall performance. This research introduces a novel Mixed-Integer Nonlinear Programming (MINLP) model to address the Storage Location Assignment Problem (SLAP) in warehouse management. Integrating multi-criteria decision-making with strategic production planning, our model advances warehouse operations by allocating storage locations to products strategically, focusing on reducing transportation distances and maximizing storage efficiency. The distinctive innovation of this study is the nuanced application of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) results to strategic storage location assignments, enhancing the model's capability to consider a comprehensive evaluation of inventory attributes, including physical characteristics and perishability. This approach evolves TOPSIS's application in warehouse management, enabling it to consider both physical characteristics and perishability of products. The outcomes of TOPSIS, including product classifications and preferences, serve as vital inputs to the mathematical model, facilitating a comprehensive evaluation of storage locations that encompasses spatial, demand-related, and physical aspects of inventory. Additionally, our research introduces a versatile decision support system, adaptable to various operational requirements. This system enhances practical decision-making in warehouse management, accommodating scenarios based on single or multiple criteria, including the cube-per-order index (COI). The research results highlight the significant impact of this innovative approach in enhancing warehouse management. By addressing the complexities of storage location assignment and integrating multiple criteria, we achieve more efficient and cost-effective warehouse operations. The approach has been shown to be adaptable and practical, making it a valuable contribution to the field of logistics and warehouse management.

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.

Quantum Optimization for Location Assignment Problem in ASSR

In an Automated Storage and Retrieval System (AS/RS), a location assignment of products is important to improve the picking efficiency. In this paper, the optimization of shelf location assignment with a quantum annealing is investigated. Product pairs are considered in order of picking frequency and are assigned to empty shelves in order of distance from an outlet. Then swapping the position of product in the pair is considered as the decision variable. This reduces the number of required qubits and guarantees the feasibility of solution. The efficiency of quantum algorithm is evaluated by comparing with mixed integer programming (MIP).

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.

Solving Location Assignment and Order Picker-Routing Problems in Warehouse Management

One of the critical warehousing processes is the order-picking process. This activity consists of retrieving items from their storage locations to fulfill the demand specified in the pick lists. Therefore, the storage location assignment affects the picking time and, consequently, reduces the operating costs of the warehouse. This work presents two alternative mixed-integer linear models and an adaptive multi-start heuristic (AMH) for solving the integrated storage location and picker-routing problem. The problem considers a warehouse with a general layout and precedence constraints for picking according to the products weight. Experimental work confirms the efficiency of the proposed reformulations since we found out a total of 334 tested instances and optimal solutions for 51 new cases and 62 new feasible solutions. The proposed AMH improved more than 29% of the best-known solutions and required an average execution time of 117 s. Consequently, our proposed algorithm is an attractive decision-making tool to achieve efficiency when solving practical situations in a warehouse.

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.

Optimizing Parcel Locker Locations in a City Crowd Logistics Network

There is a growing market for crowd-shipping (CS), which hires people to transport parcels on their regular commutes. This paper considers a hybrid CS operation that hires crowd-shippers and regular drivers (RDs) in a physical internet environment. A city crowd logistics (CCL) network is designed to serve business-to-consumer logistics demand through solving a mixed-integer linear program of a parcel locker location assignment problem. The problem minimizes the total cost by optimizing the placement of parcel lockers and the movements of parcels. As an input to the program, the spatial distribution of potential crowd-shippers is analyzed by leveraging a behavior model and data that combines household travel survey data from the Greater Toronto Area (GTA), Canada, and Canadian census data. Parcel demand is simulated based on the proportions of retail employment and the population of the corresponding planning district in the GTA. The numerical results reveal that optimal parcel locker locations are more likely to be placed in Toronto while less likely to be installed in Durham and Halton regions. Concerning the service levels of the proposed network, it is found that a CCL network can provide more stable and cost-effective services when the parcel demand level is higher. The hiring of RDs is necessary to service some regions with low crowd-shipper supply. Insights from this study can help logistics companies to develop an effective business strategy for a hybrid CS operation system.

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.

Simheuristic and learnheuristic algorithms for the temporary-facility location and queuing problem during population treatment or testing events

ABSTRACT Epidemic outbreaks, such as the one generated by the coronavirus disease, have raised the need for more efficient healthcare logistics. One of the challenges that many governments have to face in such scenarios is the deployment of temporary medical facilities across a region with the purpose of providing medical services to their citizens. This work tackles this temporary-facility location and queuing problem with the goals of minimising costs, the expected completion time, population travel time, and waiting time. The completion time for a facility depends on the numbers assigned to those facilities as well as stochastic arrival times. This work proposes a learnheuristic algorithm to solve the facility location and population assignment problem. Firstly a machine learning algorithm is trained using data from a queuing model (simulation module). The learnheuristic then constructs solutions using the machine learning algorithm to rapidly evaluate decisions in terms of facility completion and population waiting times. The efficiency and quality of the algorithm is demonstrated by comparison with exact and simulation-only (simheuristic) methodologies. A series of experiments are performed which explore the trade-offs between solution cost, completion time, population travel time, and waiting time.

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.