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This research investigates how to optimize storage location assignment to decrease the order picking time and the waiting time of orders in the staging area of low-temperature logistics centers, with the goal of reducing food loss caused by temperature abuse.

The authors applied ABC clustering to the products in a simulated database of historical orders modeled after the actual order pattern of a large cold logistics company; then, the authors mined the association rules and calculated the sales volume correlation indices of the ordered products. Finally, the authors generated three different simulated order databases to compare order picking time and waiting time of orders in the staging area under eight different storage location assignment strategies.

All the eight proposed storage location assignment strategies significantly improve the order picking time (by up to 8%) and the waiting time of orders in the staging area (by up to 22%) compared with random placement.

The results of this research are based on a case study and simulated data, which implies that, if the best performing strategies are applied to different environments, the extent of the improvements may vary. Additionally, the authors only considered specific settings in terms of order picker routing, zoning and batching: other settings may lead to different results.

A storage location assignment strategy that adopts dispersion and takes into consideration ABC clustering and shipping frequency provides the best performance in minimizing order picker's travel distance, order picking time, and waiting time of orders in the staging area. Other strategies may be a better fit if the company's objectives differ.

Previous research on optimal storage location assignment rarely considered item association rules based on sales volume correlation. This study combines such rules with several storage planning strategies, ABC clustering, and two warehouse layouts; then, it evaluates their performance compared to the random placement, to find which one minimizes the order picking time and the order waiting time in the staging area, with a 30-min time limit to preserve the integrity of the cold chain. Order picking under these conditions was rarely studied before, because they may be irrelevant when dealing with temperature-insensitive items but become critical in cold warehouses to prevent temperature abuse.

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.

The purpose of this paper is to illustrate an original decision-support tool (DST) that aids 3PL managers to decide on the proper warehouse management system (WMS) customization. The aim of this tool is to address to the three main issues affecting such decision: the cost of the information sharing, the scarce visibility of the client’s data and the uncertainty of quantifying the return from investing into a WMS feature.

The tool behaves as a digital twin of a WMS. In addition, it incorporates a set of WMS’s features based both on heuristics and optimization techniques and uses simulation to perform what-if multi-scenario analyses of alternative management scenarios. In order to validate the effectiveness of the tool, its application to a real-world 3PL warehouse operating in the sector of biomedical products is illustrated.

The results of a simulation campaign along an observation horizon of ten months demonstrate how the tool supports the comparison of alternative scenarios with the as-is, thereby suggesting the most suitable WMS customization to adopt.

The tool supports 3PL managers in enhancing the efficiency of the operations and the fulfilling of the required service level, which is increasingly challenging given the large inventory mix and the variable clients portfolio that 3PLs have to manage. Particularly, the choice of the WMS customization that better perform with each business can be problematic, given the scarce information visibility of the provider on the client’s processes.

To the author’s knowledge, this paper is among the first to address a still uncovered gap of the warehousing literature by illustrating a DST that exploits optimization and simulation techniques to quantify the impacts of the information availability on the warehousing operations performance. As a second novel contribution, this tool enables to create a digital twin of a WMS and foresee the evolution of the warehouse’s performance over time.

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.

Third-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.

This 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.

This 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.

The 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.

Instead 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.

This purpose of this study is to develop a simple framework for designing a warehouse incorporating lean principles. Multiple objectives like resource planning, material handling, storage, inventory management, including internal and external logistics, are considered.

A design procedure to incorporate lean principles for designing a warehouse for a complex multi-model production line has been proposed. The preferred standards and factors affecting warehouse design, the inputs and outputs of process flow characteristics, are incorporated into the design. Current and future state value stream mappings are drawn to bring out the challenges in the value flow.

The framework for designing a lean warehouse have been implemented and validated in a heavy machinery manufacturer. This framework will ease the work of the future lean-based warehouse designers to apply simple step-by-step processes to achieve the goal with the nearest accuracy. The steps followed can be summarized as defining the lean processes, making the lean process as the design base, collecting inputs like stock-keeping unit master, inventory and space details, and building the lean warehouse design with the step-by-step processes.

Practical tips on warehouse design have been explained focusing on the part volume, quantity handled, inventory and throughput. This will assist the practitioners in designing a lean warehouse and leading to an improved operational performance.

A simplified design procedure for designing a lean warehouse, along with a real-time case study has been enumerated in detail. Effective use of space and resources with lean tools and techniques lead to better storage and picking efficiency resulting in an overall reduction in cost.

This study focuses on a specific method of meat production that involves carcass purchase and meat production by packing facilities with a novel two-stage model that simultaneously considers location-routing and inventory-production operating decisions. The considered problem aims to reduce variable and fixed transportation and production costs, inventory holding cost and the cost of opening cold storage facilities.

The proposed model encompasses a two-stage model consisting of a single-echelon and a three-echelon many-to-many network with deterministic demand. The proposed model is a mixed-integer linear programming (MILP) model which was tested with the general algebraic modelling system (GAMS) software for a real-world case study in Iran. A sensitivity analysis was performed to examine the effect of retailers' holding capacity and supply capacity at carcass suppliers.

In this research, the number of products transferred at each level, the number of products held, the quantity of red meat produced, the required cold storage facilities and the required vehicles were optimally specified. The outcomes indicated a two percent (2%) decrease in cost per kg of red meat. Eventually, the outcomes of the first and second sensitivity analysis indicated that reduced retailers' holding capacity and supply capacity at carcass suppliers leads to higher total costs.

This research proposes a novel multi-period location-inventory-routing problem for the red meat supply chain in an emerging economy with a heterogeneous vehicle fleet and logistics decisions. The proposed model is presented in two stages and four-echelon including carcass suppliers, packing facilities, cold storage facilities and retailers.

This paper aims to propose a logistical network design framework with robustness and complexity considerations.

The paper defines robustness, complexity, and normalised efficiency of a logistical network. A mathematical model is then constructed based on the conceptual framework and applied to a hypothetical case study with varying robustness requirements. The mathematical model is formulated as an Mixed‐Integer Linear Programming problem. Furthermore, the paper introduces a graph‐theoretic view of the logistical network and presents its topological properties such as average path length, clustering coefficient, and degree distribution.

The results show that logistical network configurations can be obtained with desirable robustness levels whilst minimising cost. The relationships of robustness versus normalised efficiency and complexity are also presented. The results show that relationships between logistical network topological properties and robustness exist, as in other real world natural and man‐made complex networks.

Logistical network design is one of the earliest strategic decisions in supply chain management that supply chain managers have to make. Practitioners and researchers typically focus on optimising efficiency and/or responsiveness of logistical networks. It is argued that logistical network designers should also consider robustness and complexity as they are important characteristics of logistical network functionality. The logistical network design frame work successfully incorporates robustness and complexity into design considerations.

This paper newly introduces other important performance measures, robustness and complexity, into the logistical network design objective. The design framework is highly relevant and adds value to logistical network designers and managers.

In order-picking activities, the performance of the system can be influenced by different variables such as the order to be fulfilled, the distance to be covered or the experience of operators. Usually, this kind of activity is performed by operators rather than machines to assure flexibility. Consequently, their fatigue accumulation can decrease the performance of the overall system. The purpose of this paper is to define the kind of device to be used in an order-picking context, to obtain data which can be utilized for the evaluation of the level of fatigue and to improve the performance of the picking system.

The paper presents a comparison between existing fatigue methods which can be applied in a picking context. In addition, an analysis of the physiological literature for the evaluation of a new device for the monitoring of fatigue level is carried on and its practical use is shown.

The proposed research identifies in the heart rate monitor the device that, thanks to its advantages, can be the best one to be used in an industrial context for monitoring the physical fatigue of operators.

This study considers the importance of human factors in picking activities such as physical fatigue of operators and the need to have validated tools to monitor and to define the level of fatigue accumulation in each activity of different rate and duration.

This paper presents a framework for the design of warehouse layout to organize the design process, facilitate the task of designers, and highlight important design issues to help warehouse managers make informed decisions. The framework accounts for several factors and operations of warehousing in the design, and addresses design decisions required to respond to them. It attempts to develop a layout that has several characteristics such as modularity, adaptability, compactness, accessibility, flexibility, and distribution of movement to enable it to respond to changing conditions, improve space utilization, and reduce congestion and movement.