Storage Assignment Policy Research Articles

Scattered storage for retail e-commerce fulfillment warehouses with consideration for product turnover

Storage assignment policy of a warehouse controls how the stocks are assigned to various storage locations. Traditionally used storage policies, such as dedicated or random storage, do not address the specific needs of retail e-commerce fulfillment warehouses that store a wide assortment of products and service a large number of single-unit orders under strict timelines. This has led to the development of scattered storage assignment policy, which deliberately spreads the stocks of every product across the warehouse. However, the available approaches towards scattering have largely ignored product turnover, which is a significant determinant of order picking travel. To fill this research gap, we propose a new, turnover-incorporated scattered storage policy. More specifically, we develop a new measure of scattering of stocks and a mathematical model that optimizes the measure by scattering the stocks across the warehouse, while considering product turnover. Certain important properties of this measure are proved and eventually exploited in developing a heuristic approach to solve the proposed mathematical model for large problem instances. An upper bound for the objective function of the mathematical model is used as a benchmark to evaluate the performance and computation time of the proposed heuristic, as well as solutions obtained through genetic algorithms and surrogate optimization. We conduct a simulation study using the proposed heuristic to analyze the impact of the scattering of stocks on order picking performance in a single block, low level, picker-to-parts warehouse. We also present the impact of warehouse operating parameters on the performance of the proposed scattered storage policy.

Locating charging stations and routing drones for efficient automated stocktaking

Drones have received growing attention in logistics recently. One possible application is deploying drones for auditing inventory in warehouses. With the use of drones, warehouses are able to increase inventory record accuracy and decrease labor costs. In this research, we introduce the stocktaking drone routing problem (STDRP), which consists of routing a fleet of drones through a warehouse for stocktaking purposes as well as deciding on the location of charging stations on the warehouse floor, which is necessary due to the limited battery capacity of the drones. Subsequently, we develop an adaptive large neighborhood search-based heuristic (ALNS) with novel solution encoding and decoding approaches to solve the STDRP. In a numerical study, we show that ALNS can solve realistic instances in reasonable time. We also derive recommendations regarding the ideal size of the drone fleet, the charging infrastructure, and battery capacity. Finally, we investigate the interplay between the storage assignment policy (such as the popular ABC rule) and stocktaking efficiency using drones.

A new policy for scattered storage assignment to minimize picking travel distances

Various retail and e-commerce companies face the challenge of picking a large number of time-critical customer orders that include both a small number of items and multiple order lines. To reduce the unproductive work time of order pickers, several storage assignment policies have been proposed in the literature and in practice. In case of the scattered storage assignment (SSA) policy individual items are intentionally distributed to multiple positions in the picking area to increase the probability that items belonging to the same order can be picked at nearby positions. In this paper, we examine our recently proposed SSA policy that seeks to minimize the sum of pairwise distances (SPD) between all item positions that belong to the same order, including a drop-off point. We develop an efficient variable neighborhood search (VNS) metaheuristic to solve large instances in a reasonable computation time. We tested our SSA-SPD strategy by implementing a picking algorithm that considers multiple drop-off points and tracks inventory in the meantime. Our results show that our SSA-SPD policy helps reduce picking distances by up to 36% compared to a random scatter policy and 56% compared to a volume-based policy, depending on the number of order lines and drop-off points in the problem instance.

Robots’ picking efficiency and pickers’ energy expenditure: the item storage assignment policy in robotic mobile fulfillment system

The item storage assignment problem (ISAP) in a robotic mobile fulfillment system (RMFS) is addressed in the paper. Recently, most ISAP studies have concentrated on improving the robots’ picking efficiency while ignoring the fact that RMFS is a human-robot coordinated system. In ISAP, we also need to take human factors into account. This research investigates ISAP by considering both the robots’ picking efficiency and the pickers’ energy expenditure. The bi-objective mixed-integer programming model is developed to maximize the items’ similarities in the pods and minimize the pickers’ energy expenditure. The improved knee point-driven evolution algorithm (IKnEA) is proposed to solve the model. Experimental results demonstrate that IKnEA has superior distribution and convergence speed than NSGA-II and KnEA. IKnEA’s hypervolume (HV) indicator operates better than NSGA-II and KnEA in various warehouse scales. More layers of pods contribute to less travel distance for robots but more energy expenditure for pickers. Five Pareto solutions are presented to assist the decision-makers in selecting the optimal ISAP solutions given various preferences.

Considering pickers’ learning effects in selecting between batch picking and batch-synchronized zone picking for online-to-offline groceries

The performance of two common picking strategies, batch picking (BP) and batch-synchronized zone picking (BSZP), are compared by considering pickers’ learning effects in online-to-offline (O2O) groceries. As we know from previous research, one major advantage of BSZP is that pickers can be more familiar with the item locations because each picker works in a relatively smaller picking area. However, few papers have formulated a quantitative method to verify this advantage. To address this, we use pickers’ learning effects to quantify their familiarity with the item locations, and then compare the picking efficiency in BP and BSZP with learning effects. The optimization models of BP and BSZP are formulated to minimize the total service time, and heuristic methods are proposed to solve these models. The case study is implemented under three order types and three storage assignment policies, i.e. Random, ABC-1, and ABC-2. The results of the study indicate that with learning effects, although BSZP needs a longer travel time than BP, it requires a shorter search time. Which strategy, BP or BSZP, is more efficient mainly depends on the learning rates, storage assignment policies, and pickers’ working days. When the storage assignments are Random and ABC-1, a smaller learning rate can lead BSZP to perform better than BP. When the storage assignment is ABC-2, BSZP performs better than BP. When pickers’ working days are long enough, the learning effect is weakened, and BP outperforms. Awareness of the learning effects is required to choose a better picking strategy, evaluate more accurate picking efficiency and hire an appropriate number of pickers.

Evaluation of the Joint Impact of the Storage Assignment and Order Batching in Mobile-Pod Warehouse Systems

The rapid development of e-commerce has impacted the intelligence level of warehouses. The robotic mobile fulfillment system (RMFS) demonstrated that mobile-pod warehouse systems could process a significant number of order-picking operations within the same amount of time as other systems, saving money and improving operational efficiency. The order-picking mode in such a system is “parts-to-picker,” which is becoming both a prevailing operation mode in industrial warehouses and an encouraging research field during these years. One of the most significant fundamental factors in RMFS is the operational efficiency that is affected by storage assignment and order batching. This paper considers the joint impact of the storage assignment policies and order batching policies on order picking process. Our goal is to minimize the moving times of robots, which reflects the order-picking cost or efficiency. We propose using order similarity and item similarity to batch orders and assign item storage locations, respectively. Both the order batching and item grouping are tackled by a clustering model which is an integer linear program. We also develop a policy evaluation model to measure the order picking cost. We conduct numerical tests on six order batching and storage assignment policy combinations. A comparative analysis and an ANOVA analysis are then performed on the test results to compare the performances of these policy combinations. We find that the Weighted Support Count-based storage allocation combining with the correlation-based order batching achieves the best order-picking performance. Also, the more accurate information about the items and orders we can get from the historical data, the more order-picking workload we can save by exploiting the similarity features.

Travel distance estimation of relation-based storage assignment policies in picker-to-part warehouses

<p>Relation-Based Storage Assignment (RBSA), also called the “family grouping method,” has garnered significant interest among researchers and practitioners looking for an intuitive means by which to arrange related Stock Keeping Units (SKUs) in nearby regions for simultaneous picking. However, there is no analytic model for picking distance estimation for RBSA. Most of the related studies have targeted specific cases and applied simulation or mathematical programming to validate the family grouping method’s superiority over traditional storage assignment methods including the random assignment policy. To explore the effects of RBSA, this current study develops a preliminary analytical model of travel distance estimation for low- level, picker-to-part, and pick-by-order warehouses. We use Frequency-Based Storage Assignment (FBSA) as a baseline for comparison. We consider both single-aisle warehouse layouts and two-block, multi-aisle warehouse layouts, with return and traversal picking policies. The skewed ABC distribution curve of SKUs, the number of picks (N) per picking tour, the number of SKUs (n1) in a family, and the number of families (n2) are also taken into account. Our results show that, in comparison with the FBSA method, the RBSA method decreases the picking distance by as much as 32%.</p> <p> </p>

INCREASING WAREHOUSE THROUGHPUT THROUGH THE DEVELOPMENT OF A DYNAMIC CLASS-BASED STORAGE ASSIGNMENT ALGORITHM

This study investigates the problems faced by an agricultural wholesaler arising from an inefficient random storage assignment policy. Research was conducted on warehouse operations and storage policies. The ABC classification technique was used to create a class-based storage assignment policy. This policy was then input into an algorithm that packs products according to how frequently they are ordered. The aim of the algorithm is to store fast-moving products closest to the shipping area. If this is done, the order picking times would decrease by an average of 3 hours 20 mins per day. This would increase the warehouse’s throughput and profitability. Keywords Storage policy; SLAP; Inventory Management

Picking Efficiency and Pickers' Energy Expenditure: The Item Storage Assignment Policy in Robotic Mobile Fulfilment System

Picking Efficiency and Pickers' Energy Expenditure: The Item Storage Assignment Policy in Robotic Mobile Fulfilment System

New solution procedures for the order picker routing problem in U-shaped pick areas with a movable depot

This paper develops new solution procedures for the order picker routing problem in U-shaped order picking zones with a movable depot, which has so far only been solved using simple heuristics. The paper presents the first exact solution approach, based on combinatorial Benders decomposition, as well as a heuristic approach based on dynamic programming that extends the idea of the venerable sweep algorithm. In a computational study, we demonstrate that the exact approach can solve small instances well, while the heuristic dynamic programming approach is fast and exhibits an average optimality gap close to zero in all test instances. Moreover, we investigate the influence of various storage assignment policies from the literature and compare them to a newly derived policy that is shown to be advantageous under certain circumstances. Secondly, we investigate the effects of having a movable depot compared to a fixed one and the influence of the effort to move the depot.

Travel time models for tier-to-tier SBS/RS with different storage assignment policies and shuttle dispatching rules

This paper explores both random and class-based storage policies and three shuttle dispatching rules, random, distance-based, and demand-rate based, for a tier-to-tier shuttle-based storage and retrieval system (SBS/RS). Modeling the system as a discrete-time Markov Chain, this study derived the shuttle distribution under each policy combination, and further developed the expected travel time models for both single-command (SC) and dual-command (DC) operations. The developed models can help a warehouse designer or manager to comprehensively make four decisions for SBS/RS, whether SC or DC operations should be adopted, the most appropriate storage policy, the best shuttle dispatching rule, and the number of shuttles. The travel time models can evaluate each possible policy combination quickly through multiple criteria, such as the capacity, customer responsiveness, and costs of shuttles. Numerical experiments showed significant impacts of the policy combination on the expected travel time. Class-based storage, if implementable, is always better than random storage under each combination of shuttle dispatching rule and operational mode (i.e., SC or DC). Under random storage and SC operations, the distance-based shuttle dispatching rule is always better than the random rule, and their difference is concave in the number of shuttles. When classed-based storage is adopted and the number of shuttles is small, the demand-rate-based rule is the best under SC operations. Its benefit becomes more obvious when demand rates become more heterogeneous until some point but decreases as the system has more shuttles. Under DC Operations, depending on equipment speeds, the distance-based shuttle dispatching rule can be worse than the random rule under random storage. Under class-based storage and DC operations, the demand-rate-based rule is still the best when demand rates are heterogeneous. The differences between SC and DC operations are more significant when demand rates are more homogenous. When a system wants to be more responsive to customers, it should be run under SC operations. If the overall capacity is of interest, DC operations are much better.

Integrated production planning and warehouse storage assignment problem: An IoT assisted case

This study is motivated by a real-world problem in a food company, where production planning is restricted by the available warehouse space for the finished goods. A novel integrated strategy that combines production planning with a randomized storage assignment policy is presented. The strategy takes advantage of greater visibility and traceability of items provided by IoT-enabled tracking systems in order to increase space utilization. An integer linear programming model is developed to formulate the strategy to minimize the total cost of production and warehouse operations. Our model is the first dynamic model for a randomized storage assignment policy. The model's feasibility, complexity, and its lower bound are presented. A heuristic algorithm is developed to obtain the near-optimal solution for the large-scale real-world problem. Based on numerical experiments, comparisons between our solutions and the solutions to model with a dedicated storage policy are also presented. The results show that the integrated strategy with a randomized storage policy can significantly reduce the total cost (up to 16.84% with an average of 9.95%) and increase space utilization (up to 26.1% with an average of 14.8%), compared to the strategy with a dedicated policy. Such results provide evidence that may justify the cost of applying the new technologies, such as IoT-enabled tracking systems, in warehouse management.

The impact of integrated cluster-based storage allocation on parts-to-picker warehouse performance

Order picking is one of the most demanding activities in many warehouses in terms of capital and labor. In parts-to-picker systems, automated vehicles or cranes bring the parts to a human picker. The storage assignment policy, the assignment of products to the storage locations, influences order picking efficiency. Commonly used storage assignment policies, such as full turnover-based and class-based storage, only consider the frequency at which each product has been requested but ignore information on the frequency at which products are ordered jointly, known as product affinity. Warehouses can use product affinity to make informed decisions and assign multiple correlated products to the same inventory “pod” to reduce retrieval time. Existing affinity-based assignments sequentially cluster products with high affinity and assign the clusters to storage locations. We propose an integrated cluster allocation (ICA) policy to minimize the retrieval time of parts-to-picker systems based on both product turnover and affinity obtained from historical customer orders. We formulate a mathematical model that can solve small instances and develop a greedy construction heuristic for solving large instances. The ICA storage policy can reduce total retrieval time by up to 40% compared to full turnover-based storage and class-based policies. The model is validated using a real warehouse dataset and tested against uncertainties in customer demand and for different travel time models.

Storage assignment policy with awareness of energy consumption in the Kiva mobile fulfilment system

To facilitate more efficient and environmentally-friendly order picking operations, this study explores the optimal storage assignment policy in the Kiva mobile fulfilment system. First, temporal association analysis and a clustering approach are employed to identify highly correlated items to be stored in the same rack. Next, in order to avoid AGV blocking, a new turnover-rate-based decentralized storage policy (TRBDSP) is proposed. Subsequently, an order picking performance evaluation method is presented. Finally, simulation studies are performed to ascertain the effectiveness of TRBDSP. The results show that the new approach can significantly improve order picking efficiency and reduce AGV energy consumption.

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.

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.

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.

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.

Retrieval\u2013travel-time model for free-fall-flow-rack automated storage and retrieval system

Automated storage and retrieval systems (AS/RSs) are material handling systems that are frequently used in manufacturing and distribution centers. The modelling of the retrieval–travel time of an AS/RS (expected product delivery time) is practically important, because it allows us to evaluate and improve the system throughput. The free-fall-flow-rack AS/RS has emerged as a new technology for drug distribution. This system is a new variation of flow-rack AS/RS that uses an operator or a single machine for storage operations, and uses a combination between the free-fall movement and a transport conveyor for retrieval operations. The main contribution of this paper is to develop an analytical model of the expected retrieval–travel time for the free-fall flow-rack under a dedicated storage assignment policy. The proposed model, which is based on a continuous approach, is compared for accuracy, via simulation, with discrete model. The obtained results show that the maximum deviation between the continuous model and the simulation is less than 5%, which shows the accuracy of our model to estimate the retrieval time. The analytical model is useful to optimise the dimensions of the rack, assess the system throughput, and evaluate different storage policies.

Closed form models for dwell point locations with turnover-based storage assignment policy

This study investigates where an idle storage-retrieval device should dwell (locate) to minimise the expected response time of its next transaction for both an automated storage/retrieval system (AS/RS) and an automated storage carousel (ASC). A review of the relevant literature showed that previous attempts to obtain the dwell point location only considered a randomised storage assignment policy. This research proposes that under a myriad of operating conditions, the problem can be formulated as a p-median location problem on a tree network to incorporate the turnover-based storage assignment policy.