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Class-based storage has been studied extensively and proved to be an efficient storage policy. However, few literature addressed how to cluster stuck items for class-based storage. The purpose of this paper is to develop a constrained clustering method integrated with principal component analysis (PCA) to meet the need of clustering stored items with the consideration of practical storage constraints.

In order to consider item characteristic and the associated storage restrictions, the must-link and cannot-link constraints were constructed to meet the storage requirement. The cube-per-order index (COI) which has been used for location assignment in class-based warehouse was analyzed by PCA. The proposed constrained clustering method utilizes the principal component loadings as item sub-group features to identify COI distribution of item sub-groups. The clustering results are then used for allocating storage by using the heuristic assignment model based on COI.

The clustering result showed that the proposed method was able to provide better compactness among item clusters. The simulated result also shows the new location assignment by the proposed method was able to improve the retrieval efficiency by 33 percent.

While number of items in warehouse is tremendously large, the human intervention on revealing storage constraints is going to be impossible. The developed method can be easily fit in to solve the problem no matter what the size of the data is.

The case study demonstrated an example of practical location assignment problem with constraints. This paper also sheds a light on developing a data clustering method which can be directly applied on solving the practical data analysis issues.

This paper aims to propose a simulation model integrated with an empirical regression analysis to provide a new mathematical formulation for automated storage and retrieval system (AS/RS) travel time estimation under class-based storage and different input/output (I/O) point vertical levels.

A simulation approach is adopted to compute the travel time under different warehouse scenarios. Simulation runs with several I/O point levels and multiple shape factor values.

The proposed model is extremely precise for both single command (SC) and dual command (DC) cycles and very well fitted for a reliable computation of travel times.

The proposed mathematical formulation for estimating the AS/RS travel time advances widely applied methodologies existing in literature. As well as, it provides a practical implication by supporting faster and more accurate travel time computations for both SC and DC cycles. However, the regression analysis is conducted based on simulated data and can be refined by numerical values coming from real warehouses.

This work provides a new simulation model and a refined mathematical equation to estimate AS/RS travel time.

This paper aims to develop an approach to design a warehouse that uses class-based storage policy in a way that minimizes both space cost and material handling cost.

The authors argue for and develop an optimization model for joint determination of lane depth, lateral width and product partitions for minimizing the sum of handling and space costs. In doing so, the assumption of perfect sharing is also relaxed. Using computational experiments, the authors characterize the operating conditions based on pick density and cost ratio. The authors further outline an approach to decide the conditions under which it is advantageous to implement multiple classes.

More classes are preferred when both the pick density and cost ratio are higher and vice versa. Factors such as demand skewness, lane depth and stacking height affect the space-sharing dynamics.

The paper gives the practical insights on when the conditions under which it is advisable to partition a warehouse into a certain number of classes instead of maintaining and when to maintain as a single-class block. It also gives a method to estimate the space-sharing factor, given a combination of operating parameters.

Very few studies have seen class-based storage policy in the context of block stacked warehouse layout. Further, block stacking designs have mostly been approached with the objective of minimizing just the space cost. This study contributes to the literature by developing an integrated model, which has the practical utility.

This study is concerned with evaluating the performance of different storage policies and tour construction procedures for multi‐command picking using automated storage‐retrieval systems (AS/RS). The AS/RS machine travel time for each storage policy is derived as a function of the shape of the storage area, the number of picking points and the sequencing algorithm used. The combination of these variables yields various solutions to the problem, thus providing a useful guide in designing effective picking systems. The average travel time of the AS/RS in each configuration is calculated by computer simulation in a full factorial experimental design. A subset of the results derived from this study has been successfully implemented in a case study, involving the BMW AG Italy spare parts distribution warehouse.

PurposeWith the current interest in all aspects of supply chain management, the demands on warehousing have changed significantly within the past few years. In an attempt to meet this challenge, warehouses have become more concerned with proper slotting and storage techniques. This paper seeks to evaluate slotting measures and storage assignment strategies in a simulated manual bin‐shelving (low level picker‐to‐part) warehouse in terms of travel distance and the fulfillment time to complete an order.Design/methodology/approachThe approach utilises Monte Carlo simulation of a manual bin‐shelving pick area.FindingsThe results illustrate that popularity, turnover, and cube‐per‐order index (COI) performed best among slotting measures. Several new storage assignment strategies utilizing the concept of “golden zone” picking, which slots high demand stock‐keeping units (SKUs) at the height between the picker's waist and shoulders, were introduced. Results from the simulation study show that the golden zone storage assignment strategies generated significant savings in order fulfillment time compared to storage policies that ignore the golden zone concept.Originality/valueProvides an evaluation of slotting measures and storage assignment strategies that generated significant savings in order fulfillment time.

Class‐based storage (CBS) partitions stock‐keeping units (SKUs) into storage classes by demand and randomly assigns storage locations within each storage class area. This study compares the performance implications of CBS to both random and volume‐based storage (VBS) for a manual order picking warehouse. In addition, this study considers the effect of the number of storage classes, the partition of storage classes, and the storage implementation strategy applied in the warehouse. The simulation results show that CBS provides savings in picker travel over random storage and offers performance that approaches VBS. Other operational issues having an impact on warehouse performance are examined. The results offer managers insight for improving distribution center operations.

The design of retail backroom storage areas has great impact on in-store operations, customer service level and on store life-cycle costs. Moreover, backroom storage in modern retail grocery stores is critical to several functions, such as acting as a buffer against strong demand lifts yielded by an ever-increasing promotional activity, stocking seasonal peak demand and accommodating e-commerce activities. The purpose of this paper is to propose a framework to design retail backroom storage area. Furthermore, the authors aim to draw attention to the lack of literature on this topic, while clarifying the relationship between this promising research stream and the considerable body of research regarding the design and operations of conventional warehouses, as well as retail in-store operations.

The key literature on backrooms, grocery retail, in-store operations, warehouse design and operations was reviewed. This allowed an understanding of the gap in the literature regarding the design of backrooms. Moreover, a case study methodological approach was conducted in a Portuguese retailer to extend the literature review.

Despite having functions similar to conventional warehouses, backroom storage facilities have particularities that deserve a distinct analysis. Thus, the authors stress these differences and demonstrate how they influence the development of a novel backroom design framework.

This paper fills a gap by proposing a framework to design backroom areas. Furthermore, this research may help practitioners to better design backroom areas, since this process currently lacks a formal and standardized procedure.

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.

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.

The time and distance from manufacturing point to storage location are accountable as it also contributes to picking performance.

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.

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.

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.

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.

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.

To present an innovative approach to the design of a flexible order‐picking systems (OPS). The proposed conceptual framework is the first step to the development of an expert system created in a rapid and innovative manner, i.e. based on the integration of simulation, genetic algorithms and factorial analysis.

The impact of alternative policies and configurations of both picker‐to‐part and part‐to‐picker OPS is quantified, with the aim of designing and optimising robust facilities capable of minimising global costs and maximising their performances in terms of efficiency and customer service quality.

The importance of validation analysis and both virtual and on‐field data collection emerges.

Compared with the studies found in the literature, this article presents an innovative approach with original results that will be of use in the design and control of a flexible OPS.