Storage Assignment Policy Research Articles

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

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

Necessity and complexity of order picking routing optimisation based on pallet loading features

Abstract Order picking is the most labour-intensive and costly activity of warehouses. The main challenges of its improvement are the synchronisation of warehouse layout, storage assignment policy, routing, zoning, and batching. Furthermore, the competitiveness of the warehouse depends on how it adapts to the unique customer demands and product parameters and the changes. The operators usually have to manage the picking sequence based on best practices taking into consideration the product stacking factors and minimising the lead time. It is usually necessary to support the operators by making e ective decisions. Researchers of the pallet loading problem, bin packing problem, and order picking optimisation provide a wide horizon of solutions but their results are rarely synchronised.

Picking efficiency and stock safety: A bi-objective storage assignment policy for temperature-sensitive products

Abstract The increasing attention of consumers to product quality and safety raises new challenges for logistics. Enhancing the operation efficiency and mutually ensuring the safety and quality of the handled products are key levers for logistics providers and other operators in temperature-sensitive supply chain. Handling the temperature conditions experienced by the inventory is valuable in warehouses and any other points along the supply chain where products pause for long periods. This paper proposes an original adaptive storage assignment policy for temperature-sensitive products, which enables to mutually manage both efficiency and stock safety goals. This policy is based on a bi-objective integer programming model and an original solving algorithm. We intend our policy for warehouses that handle temperature-sensitive products in presence of high demand and weather seasonality and strong inventory mix turn-over. To the best of authors’ knowledge, this is the first attempt to integrate into a storage assignment policy the issue of stock quality conservation, the optimization of the picking activities, and the management of weather and demand seasonality at the warehouse. A multi-scenario what-if analysis was applied to a 3PL warehouse of biomedical products to validate the policy and explore its insight in a real-world application. This policy autonomously balances the management of the inventory between the efficiency and stock safety levers and records savings of 12% of the picking travel time and up to 20% of the inventory safety. In conclusion, this policy assesses how the warehouse infrastructure can respond to the demand and weather seasonality in accordance with the efficiency and safety requirements.

An investigation of the effects of storage assignment and picker routing on the occurrence of picker blocking in manual picker-to-parts warehouses

PurposeOrder picking is one of the most costly logistics processes in warehouses. As a result, the optimization of order picking processes has received an increased attention in recent years. One potential source for improving order picking is the reduction of picker blocking. The purpose of this paper is to investigate picker blocking under different storage assignment and order picker-route combinations and evaluate its effects on the performance of manual order picking processes.Design/methodology/approachThis study develops an agent-based simulation model (ABS) for order picking in a rectangular warehouse. By employing an ABS, we are able to study the behaviour of individual order pickers and their interactions with the environment.FindingsThe simulation model determines shortest mean throughput times when the same routing policy is assigned to all order pickers. In addition, it evaluates the efficiency of alternative routing policies–storage assignment combinations.Research limitations/implicationsThe paper implies that ABS is well-suited for further investigations in the field of picker blocking, for example, with respect to the individual behaviour of agents.Practical implicationsBased on the results of this paper, warehouse managers can choose an appropriate routing policy that best matches their storage assignment policy and the number of order pickers employed.Originality/valueThis paper is the first to comprehensively study the effects of different combinations of order picker routing and storage assignment policies on the occurrence of picker blocking.

Manoeuvres in manual person-to-parts order picking systems: Is there a significant influence on travel time and metabolic rates?

In current planning models for travel time calculation in manual person-to-parts order picking systems it is assumed that the order picker travels at constant velocity through the warehouse. Curves and turn manoeuvres that are necessary for changing the aisles are neglected in operating strategies so far. For order pickers that use a cart for transporting the items a more realistic approach would be to consider additional time for those manoeuvres due to a reduced gait velocity as well as additional physical exposure due to higher pushing/pulling forces. The paper at hand investigates if those influences should be further deliberated in travel time calculation and in ergonomic evaluation by the means of a simulation study. First results show that especially for small warehouse dimensions and random storage assignment policies warehouse operators could prefer heuristic routing policies (combined/composite/traversal) instead of the optimal (minimal travel distance) policy when taking curves/turn manoeuvers into account.

A Simple Case of Pallet Setup Features Based Order Picking Routing Optimization

Order picking is the most labour-intensive and costly activities in many warehouses by consuming ca. 55 % of the total operating expenses. Order picking development strategies mostly concentrate on warehouse layout, storage assignment policy, routing, zoning and on batching methods. The main challenges of order picking process improvements are the synchronization of these fields and fit the system with the further influencing factors, like unique demands and product parameters. Researchers of the pallet-loading problem could provide a wider horizon on considerable parameters, but their results are rarely implemented into order picking processes. The physical parameters of the products also have a significant impact on the processes which could strongly influence the picking sequence. \\The aim of this paper is to describe our own developed methodology for modeling the pallet setup rules. We introduce our product classifying and PSF based decision matrix modeling solutions. These will be the basis of our further research on PSF based harmonized routing and SLA optimization. We examine the complexity of a simple pallet setup features based order picking routing case, where the products of the defined pallet setup classes stored in separated zones.

A genetic algorithm for minimizing energy consumption in warehouses

Green supply chain management is generally defined as integration of green thinking and environmental issues into the whole supply chain operations like product design, manufacturing process, warehousing, distribution etc. Within this context green principles should be adopted in warehouse management to minimize negative impact on the environment. In warehouse operations, picking must be analyzed attentively which is widely studied in literature for minimizing service time levels because of its close relation to the higher costs. The efficiency of picking in warehouses mainly depends on storage assignment policy that directly affects picking performance in warehouses. In this paper, picking operation in warehouses is studied to minimize energy consumption with proper storage policy other than service time. Genetic algorithm (GA) is proposed to solve the problem and numerical examples are presented to demonstrate the performance of the GA. Results show that, the GA gives efficient solutions to the problem.

Human energy expenditure in order picking storage assignment: A bi-objective method

Order picking is the most time-consuming and labor-intensive activity in warehousing. Due to the need to frequently handle items, order picking requires high human energy expenditure and poses a risk environment for workers to develop musculoskeletal disorders. The storage assignment policy in use has a significant impact on human energy expenditure and fatigue during the picking process, but this impact is usually not considered in (management-oriented) decision support models for storage assignment.This paper models and analyzes the integration of human energy expenditure as one dimension of ergonomics into the storage assignment problem using a bi-objective approach that considers both total order picking time and human energy expenditure. Time and energy expenditure depend on the main features of the order picking system, such as item characteristics, item popularity, order profiles, and physical dimensions of the shelf and locations. Pareto frontiers are constructed to understand the impact of the storage assignment policy on the objective functions. Subsequently, a quantitative approach is developed to integrate the energy expenditure rate into the time estimation for a general order picking system based on the introduction of rest allowance. Finally, the results of the model are analyzed and suggestions for the practical application of the model are presented.

A proposed hybrid storage assignment framework: a case study

Purpose – A hybrid storage assignment (combination class-volume-based) framework considering quality proximity, customer and material categorization are key distinguished contents of this paper. In spite of using individual storage allocation approach, the hybrid allocation policy performs better under certain environment. The paper aims to discuss these issues. Design/methodology/approach – Although it has been proved that every storage assignment policy has their advantages and limitations, one or more storage assignment policies with combination of zoning and layout design can be used together for further improvement. The authors have conducted this study at warehouse of a manufacturing firm that produce only single product with varieties of material and quality criteria. Picking optimization includes elimination of non-value-added activities like unwanted forklift and package movements, time and distance traveled for retrieval as well as storage. Other allied operations with respect to customer acceptance level and resource utilization are also considered. Findings – The time and distance from manufacturing point to storage location are accountable as it also contributes to picking performance. Originality/value – Quality-based cluster analysis is carried out to find out closeness among customers, which is used to propose algorithm with new layout design, zoning and storage allocation policy.

Unit-load storage assignment strategy for warehouses in seismic areas

The reports from the strong earthquakes that hit seismic regions, as Italy, frequently show the damages occurring to the storage systems and the collapse of their racks, with devastating social and economic consequences. The loads stored in such systems represent the most of their weight, so that the overall structure stability is expected to highly depend on the chosen storage assignment policy. Conventional approaches relate the load assignment to the storage/retrieval (S/R) travel time, affecting the handling costs, neglecting any further issue. This paper faces the so-called unit-load assignment problem (ULAP) for industrial warehouses located in seismic areas presenting an innovative integer linear programming (ILP) model. The model includes both the S/R travel time and an innovative stability condition for the rack collapse prevention during seismic events. A case study, based on a multi-scenario analysis, is discussed to show the benefits coming from the application of the proposed strategy and to assess the impact of the stability issues on the average single-command travel time. The key results outline the effectiveness of the proposed strategy revealing that significant improvements in the structure stability are achievable with little decrease of the S/R efficiency.

A REVIEW OF ORDER PICKING IMPROVEMENT METHODS

As a crucial and one of the most important parts of warehousing, order picking often raises discussion between warehousing professionals, resulting in various studies aiming to analyze how order picking activity can be improved from various perspective. This paper reviews various past researches on order picking improvement, and the various methods those studies analyzed or developed. This literature review is based on twenty research articles on order picking improvement viewed from four different perspectives: Automation (specifically, stock-to-picker system), storage assignment policy, order batching, and order picking sequencing. By reviewing these studies, we try to identify the most prevalent order picking improvement approach to order picking improvement.

Multiple-weight unit load storage assignment strategies for energy-efficient automated warehouses

Energy-efficient manufacturing claims for a change in warehousing operations management, moving from the traditional time-based perspective to the energy-based one. In automated storage and retrieval systems, this new approach leads to revise storage assignment policies, so that the least energy can be consumed by cranes for unit load movements. Dedicated energy-based policies and related zone shapes are analysed with particular attention to the role of unit load weight and the design option of rack stratification from floor to ceiling. Energy performance of dedicated, class-based, and random storage policies are then compared for different demand curves and rack shape. In order to enhance flexibility while maximising energy efficiency, new dynamic energy-based policies are also introduced and evaluated.

Storage policies and optimal shape of a storage system

The response time of a storage system is mainly influenced by its shape (configuration), the storage assignment and retrieval policies, and the location of the input/output (I/O) points. In this paper, we show that the optimal shape of a storage system, which minimises the response time for single command cycles, is independent of the used storage policy and the location of the I/O point. This means decisions on the shape of the storage system and storage policy can be decoupled. This simplifies system design since at the design stage little information on customer demand is available. The storage system can then be designed for optimality assuming a random storage policy. Such a system optimally accommodates other time-saving storage policies such as class-based or full-turnover-based storage without requiring configuration correction.

Proximity Value for a Storage Assignment Policy in a Multi-I/O Point ETV

This paper is focused on an analysis of Elevating Transfer Vehicle (ETV) System installed in an existing air cargo terminal. The operation of ETV System is very similar to that of Automated Storage and Retrieval System (AS/RS), and the existence of multi-input and output (I/O) points in ETVs is assumed. The proximity value (PV) is defined for the procedure of storage assignment in ETVs with multi-I/O points. The validity and usefulness of the PV in the assignment procedure of automated storage are shown through the simulation study. For a multi-I/O point ETV, dynamic time based PVs are more efficient for the storage policy while the class-based storage policy works better for a single I/O point system. Although, this study focuses mainly on an efficient policy for an ETV system, the proposed rule for the operation of the system would work with an AS/RS as well.

On storage assignment policies for unit-load automated storage and retrieval systems

This paper focuses on the management of automated storage and retrieval systems (AS/RS) from a practical standpoint. Most of the articles in the recent AS/RS literature rely on analytical models that are based on tight assumptions. However, some of these assumptions are not clear cut and therefore the validity of the results reported in the theoretical works need to be clearly assessed when applying them to a particular industrial setting. To this end, this paper presents a discrete-event simulator that was designed to accurately reproduce the characteristics of an industrial AS/RS owned by a large manufacturer in the food industry located in Quebec City, Canada. Various scenarios inspired by real data provided by our industrial partner were used to compare the system performance under several storage assignment policies. Our experimental results confirm our hypothesis that the system behaviour deviates from the theoretical expectations as soon as the most simplistic, yet realistic conditions are considered.

Recency-based storage assignment and warehouse configuration for recurrent demands

Storage assignment is an important decision problem in warehouse operation management. In conventional problem settings of distribution warehouses, stock items are stored in bulk but retrieved in small quantities. Storage assignment methods typically make use of demand attribute information of order quantity, order frequency and correlation between demands. In this paper, we address a different problem in which the request for the same stock items is stochastically recurrent. The problem arises when the items are needed in production and, after production, are returned to warehouses for later reuse. Examples of such items include tooling in factory, books in library and digital objects in data warehouses. Utilizing the recurrent characteristics, a salient recency-based storage assignment policy and an associated cascaded warehouse configuration are proposed and analyzed in this paper. This paper has four parts. In the first part, a model of recurrent demand is described. In the second part, the efficiency of the recency-based policy and a traditional ID-based policy is analyzed. In the third part, a mathematical programming model for optimal configuration of cascaded warehouses is presented. Finally, a case study of hospital visits is presented. This paper concludes with recommendations on cascading and zoning the warehouse for applying the recency-based policy.

Storage assignment problem with travel distance and blocking considerations for a picker-to-part order picking system

Order picking is a key operation in managing a warehouse efficiently. Most previous studies on picking only considered single-picker operation; however, many pickers frequently work concurrently in the same region. Since congestion may occur in such a multi-picker system, waiting time must be taken into account together with travel time and distance when evaluating the efficiency of picking operations. The picking model under investigation can be formulated as a queueing network, and a heuristic storage assignment policy that considers both the travel time and the waiting time simultaneously by minimizing the average order fulfillment time is developed in the paper. An approximation method and a simulation model using eM-plant software are presented to implement the proposed heuristic algorithm and to compare the mean travel time for different storage assignment polices as well. The results indicate that the proposed heuristic policy outperforms existing storage assignment policies in a multi-picker warehouse environment.

The adaptive approach for storage assignment by mining data of warehouse management system for distribution centres

Among distribution centre operations, order picking has been reported to be the most labour-intensive activity. Sophisticated storage assignment policies adopted to reduce the travel distance of order picking have been explored in the literature. Unfortunately, previous research has been devoted to locating entire products from scratch. Instead, this study intends to propose an adaptive approach, a Data Mining-based Storage Assignment approach (DMSA), to find the optimal storage assignment for newly delivered products that need to be put away when there is vacant shelf space in a distribution centre. In the DMSA, a new association index (AIX) is developed to evaluate the fitness between the put away products and the unassigned storage locations by applying association rule mining. With AIX, the storage location assignment problem (SLAP) can be formulated and solved as a binary integer programming. To evaluate the performance of DMSA, a real-world order database of a distribution centre is obtained and used to compare the results from DMSA with a random assignment approach. It turns out that DMSA outperforms random assignment as the number of put away products and the proportion of put away products with high turnover rates increase.

Improving the productivity of order picking of a manual-pick and multi-level rack distribution warehouse through the implementation of class-based storage

Determining the proper storage locations for potentially thousands of products is one major task that faces the warehouse manager during the design of a new warehouse or the refurbishment of existing facility. There are many factors affects the storage assignment. For example, order picking method, size and layout of the storage system, material handling system, product characteristics, demand trends, turnover rates and space requirements. Selecting appropriate storage assignment policies (i.e. random, dedicated or class-based) and routing methods (i.e. transversal, return or combined) with regards to above factors is a possible solution to tackle this problem. However, the efficiency of these policies is interdependent. This paper aims to present a simulation study of a real case regarding storage assignment problem of a manual-pick and multi-level rack warehouse. Performance of the case is measured in terms of travel distance and order retrieval time. The results of the study indicate that the key to effective implementation of a storage assignment system is to match the types of warehouse storage system and the variety of items in the customer order. Also, the use of key performance indicator should clearly reflect the needs of the warehouse.

Simulation-based analysis for hierarchical storage assignment policies in a container terminal

This paper describes the structure and employment of a discrete-event simulation model for the real-life detailed processes performed during the handling of import containers. In particular, the model focuses on the storage assignment problem at the operational level in a container terminal with a multiple-berth structure. A novel approach using a hierarchical structure is adapted to partition the assignment problem into two sub-problems and solve each of them using separate decision rules. Suggested storage policies are evaluated in view of the overall performance of the container terminal. Different traffic densities are simulated to reflect the real-time environment. Simulation runs emphasize the bottleneck at the quay cranes. Results confirm that quay-crane efficiency may be improved by using appropriate storage policies. Strategies that adopted the integrated assignment method at the first level, where travel distances and gantry-crane workloads are considered, performed best at coping with this bottleneck. At the second level of the hierarchy, by using a segregated strategy, in which estimated container departure dates are considered, the number of reshuffles is reduced.