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Determination of new warehouse locations is an important managerial decision. The problem typically involves a number of considerations such as cost per distance of distribution to and from the warehouse, volume of distribution, fixed and variable site costs, service performance of the warehouse and potential increases in demand (or service), as well as many other factors. The economic significance of warehouse location decisions is increasing, particularly due to expected increases in transportation costs, competition for market share and changing demand patterns.

As logistics and distribution emerge as major disciplines, more rigorous and advanced analysis is required on each of the major decision areas within the total logistic and distribution systems. This article concentrates on the warehouse location problem, reviewing comparatively a taxonomy of quantitative methods applied in logistics management. The paper stresses the advantages and limitations of each of the techniques and their possible practical applications, suggesting a feasible alogarithm for dealing with the “Warehouse Location Problem”.

A very important concern of physical distribution managers is deciding the location of warehouses (distribution centres or depots). It is, therefore, not surprising that this location problem has been receiving considerable research attention and indeed, some impact has been made in actual industrial warehouse location decisions. The interested reader is referred to the expository articles, which describe both the theoretical developments and some practical applications of the theory.

Agricultural sector not only meets the nutritional requirements of all living creatures but also generates the primary source of the raw material provided by various branches of industry to fulfill their functions. It is of great importance to increase studies on oilseeds which have an important role in Turkey's agricultural products. They are grown in almost all of the country, which are vital for the nutrition and many sectors. The main purpose of the study is to offer an integrated approach to determine potential warehouse locations for oilseeds.

This is the first study that integrates Delphi, analytical hierarchical process (AHP), technique for order preference by similarity to ideal solution (TOPSIS), P-Median and Panel data analysis in a real case. This integrated approach consists of the following steps, respectively: (1) The criteria were determined by the Delphi method. (2) The weights of the criteria were calculated by AHP and the provinces with the highest oilseed warehouse potential in seven regions of Turkey were specified by TOPSIS. (3) Oilseed warehouse numbers and locations were obtained by P-Median. (4) In order to answer whether the distribution network is profitable in the future with the determined center locations, a forecast model based on panel data analysis was created. (5) Regional representatives were determined for 2030, and the distribution network was analyzed again. (6) The costs that arose in 2018 and 2030 were computed and compared by cost analysis. (7) The effect of the change in criteria weights on the alternative results was tested by scenario analysis.

The findings indicated that oilseed crop production potential and oilseed crop production area turned out to be the most important criteria. Furthermore, the results showed that this model is robust and suitable for warehouse location selection studies.

The study can serve as a guide for local and central policy makers with both the criteria it uses and the model it develops.

The main contribution of this study is that the integrated approach has been used for the first time in location selection in a real case.

Suggests that, in recent years, remarkable progress has been made in the development of the topological design of logistics networks, especially in the warehouse location problem. Extends the standard warehouse location problem to a generalization of multiproduct capacitated warehouse location problem, as opposed to differentiated variations of a single‐product warehouse location problem, where each warehouse has a given capacity for carrying each product. Presents an algorithm based on cross‐decomposition, to reduce the computational difficulty by incorporating Benders decomposition and Lagrangean relaxation. Computational results of this algorithm are encouraging.

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.

Examines the interdependence between facility location, transportation and inventory issues in a distribution network design problem. Management of inventories, determination of transportation policy, and location of plants and distribution centers are normally carried out by different groups of people in an organization. These activities interact, however, when the transportation is used to replace inventory, an increase in the number of warehouses increases total system inventory or location of warehouses would dictate the type of transportation mode choice or carrier that needs to be used. The proposed model, FLITNET, is expected to provide a more complete representation of the trade‐offs that exist among the location, transportation and inventory cost components, and lead to an optimal solution.

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 study aims to propose a decision-support system to determine the location of a regional disaster logistics warehouse. Emphasizing the importance of disaster logistics, it considers the criteria to be evaluated for warehouse location selection. It is aimed to determine a warehouse location that will serve the disaster victims most efficiently in case of a disaster by making an application for the province of Izmir, where a massive earthquake hit in 2020.

The paper proposes a fuzzy best–worst method to evaluate the alternative locations for the warehouse. The method considers the linguistic evaluations of the decision-makers and provides an advantage in terms of comparison consistency. The alternatives were identified through interviews and discussions with a group of experts in the fields of humanitarian aid and disaster relief operations. The group consists of academics and a vice-governor, who had worked in Izmir. The results of a previously conducted questionnaire were also used in determining these locations.

It is shown how the method will be applied to this problem, and the most effective location for the disaster logistics warehouse in Izmir has been determined.

This study contributes to disaster preparedness and brings a solution to the organization of the logistics services in Izmir.

Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.

The problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.

For the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.

The proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.