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This article presents an integrated methodology for the inclusion of a facility′s interactions with the outside environment, and the impact of those interactions on the location of entrances and exits within the facility. For example, the outside interactions generated by shipping and receiving activities will significantly influence the location of facility ingress/egress points. The “practical layout planning” (PLP) methodology integrates both layout construction and improvement techniques. The principal idea in this methodology is that departments within a facility be “condensed” into a number of “points”. In the layout construction phase, the relative location of these points is determined using multifacility location theory, where the facility′s interactions with the outside environment are represented by “contact points” and the departments in the layout are analogous to the new facilities to be located. Once these relative departmental positions are obtained, the “points” are then adjusted for areas to obtain a constructed layout. In the improvement technique, intra‐departmental flows are optimised by the optimal location of ingress/egress points which are determined by again using multifacility location theory. Interdepartmental flow is optimised by minimising the distance between departments as indicated by their closest ingress/egress points. The objective is to obtain a minimum total flow‐cost by interchanging departmental locations without violating physical constraints. The PLP methodology is demonstrated with a modest example.

Facility location decisions are critical and should be taken after strategic evaluations. Globalization and integration of economies make such decisions further complex and risk prone. The purpose of this paper is to identify and assess the risk factors to be considered while taking new facility location decision associated with global supply chain and device the methodology. A grey-based multi-criteria decision-making (MCDM) approach is used for this purpose, which also takes in to account the uncertainty in decision making. Such approach enables final decision to be more real and practical. The paper also highlighted and discussed the criteria on the basis of which the management can select the best suitable site.

The risk factors related to facility location for a global firm are identified. To select the location of a global facility with least risk, grey-based MCDM approach is formulated. This grey-based MCDM is demonstrated using the hypothetical case of an industrial valve manufacturing global firm. The grey approach is used to analyse location alternatives based on various decision criteria for extracting comparative ranking.

The paper presents a tool for strategic and planning level. It helps supply chain managers to identify the risks related to a candidate location. Then it guides the supply chain manager at strategic level to find the least risky location for a manufacturing facility.

This paper demonstrates the grey-based MCDM approach for determining less risky location to locate a new manufacturing unit so that practitioners can use this approach for taking other strategic decisions. The supply chain configuration can be decided subsequently which will yield more practical results and the decision taken will be more fruitful for firm.

The extensive literature review reveals that there are many models in the literature that addressed the issue of risk minimization in supply chain, but it was also noticed that there are limited number of models that minimize risk in locating a global facility considering the uncertainty of data in decision making. This is the first time that grey-based MCDM approach is formulated and used to find most suitable facility location under risk.

The research in competitive facility location (CFL) is quite dynamic, both from a problem formulation and an algorithmic point of view. Research direction has changed immensely over the years to address various competitive challenges. This study aims to explore CFL literature to highlight these research trends, important issues and future research opportunities.

This study utilises the Scopus database to search for related CFL models and adopts a five-step systematic approach for the review process. The five steps involve (1) Article Identification and keyword selection, (2) Selection criteria, (3) Literature review, (4) Literature analysis and (5) Research studies.

The paper presents a comprehensive review of CFL modelling efforts from 1981 to 2021 to provide a depth study of the research evolution in this area. The published articles are classified based on multiple characteristics, including the type of problem, type of competition, game-theoretical approaches, customer behaviour, decision space, type of demand, number of facilities, capacity and budget limitations. The review also highlights the popular problem areas and dedicated research in the respective domain. In addition, a second classification is also provided based on solution methods adopted to solve various CFL models and real-world case studies.

The paper covers 40 years of CFL literature from the perspective of the problem area, CFL characteristics and the solution approach. Additionally, it introduces characteristics such as capacity limit and budget constraint for the first time for classification purposes.

This paper aims to deal with a real-life strategic conflict in joint operations (JOs) for facility location decision and planning in an oil and gas field that stretches over two countries and tries to develop a basis for mitigating such conflict.

This paper develops a novel approach using integer linear programming (ILP) to determine optimal facility location considering technical, economic and environmental factors. Strategic decision-making in JOs is also influenced by business priorities of individual partner, sociopolitical issues and other covert factors. The cost-related quantitative factors are normalized using inverse normalization function as these are to be minimized, and qualitative factors that are multi-decision-making criteria are maximized, thus transforming both qualitative and quantitative factors as a single objective of maximization in ILP model.

The model identifies the most suitable facility location based on a wide range of factors that would provide maximum benefit in the long term, which will help decision-makers and managers.

The model can be expanded incorporating other quantitative and qualitative factors such as tax incentives by the government, local bodies and government regulations.

The applicability of the model is not limited to JOs or oil/gas field, but is applicable to a wide range of sectors.

The model is transparent and based on rational and scientific basis, which would help in building consensus among the dissenting parties and aid in mitigating strategic conflict. Such type of model for mitigating strategic conflict has not been reported/used before.

A growing body of evidence points to the influence of location and allocation decisions on the structure of healthcare networks. The authors introduced a three-level hierarchical facility location model to minimize travel time in the healthcare system under uncertainty.

Most healthcare networks are hierarchical and, as a result, the linkage between their levels makes it difficult to specify the location of the facilities. In this article, the authors present a hybrid approach according to data envelopment analysis and robust programming to design a healthcare network. In the first phase, the efficiency of each potential location is calculated based on the non-radial range-adjusted measure considering desirable and undesirable outputs based on a number of criteria such as the target area's population, proximity to earthquake faults, quality of urban life, urban decrepitude, etc. The locations deemed suitable are then used as candidate locations in the mathematical model. In the second phase, based on the proposed robust optimization model, called light robustness, the location and allocation decisions are adopted.

The developed model is evaluated using an actual-world case study in District 1 of Tehran, Iran and relevant results and different sensitivity analyses were presented as well. When the percentage of referral parameters changes, the value of the robust model's objective function increases.

The contributions of this article are listed as follows: Considering desirable and undesirable criteria to selecting candidate locations, providing a robust programming model for building a service network and applying the developed model to an actual-world case study.

There have been numerous extensions of the maximum covering location problem that has been developed in the last decade to deal with facility location. Most of the research, however, addresses a single attribute or objective. In the case when a single criterion such as minimizing average response time to access a service facility is insufficient to address the interests of the decision maker, multiple objectives must be employed. Qualitative factors like customer service and market demand as well as quantitative factors like distribution and operating costs need to be appropriately weighted and used in a mathematical programming model. We develop a multi‐objective model for a service facility location problem that simultaneously sites facilities and allocates demand for products from different customer zones. We apply this model to “real‐world” data and show the practical advantages of using this model to solve capacitated service logistics problems.

Logistical facility location decisions can make a crucial difference in the success or failure of a company. Geographical Information Systems (GIS) have recently become a very popular decision support system to help deal with facility location problems. However, until recently, GIS methodologies have not been fully embraced as a way to deal with new facility location problems in business logistics. This research makes a framework for categorizing empirical facility location problems based on the intensity of the involvement of GIS methodologies in decision making. This framework was built by analyzing facility location models and GIS methodologies. The research results revealed the depth of the embracement of GIS methodologies in logistics for determining new facility location decisions. In the new facility location decisions, spatial data inputs are almost always coupled with the visualization of the problems and solutions. However, the usage of GIS capability solely (i.e. suitability analysis) for problem solving has not been embraced at the same level. In most cases, the suitability analysis is used together with special optimization models for choosing among the multiple alternatives.

This study aims to adapt analytical hierarchy process (AHP) for choosing the optimal location for sport facilities. The location of a sports facility contributes significantly to its potential success or failure. Therefore, factors affecting such location-related decisions must be carefully studied and prioritized in a systematic fashion.

This study develops a seven-step framework which may be used to decide on a location from among several alternatives. Through an extensive literature review, this study first determines the factors affecting sports facility location selection and then applies AHP steps by asking several sports facility owners and managers to assess importance of the criteria.

This study determined the sport facility location selection factors as “ease of access,” “facility features,” “financial issues,” “neighborhood” and “market,” and further divided each factor into its subfactors. To illustrate the framework of using AHP as a tool to select the right location for sport facilities, we chose three candidate locations and scored them according to the calculated weight scores of the criteria, identifying the strengths and weaknesses of each location.

This study provides several managerial implications that may guide sport facility investors in choosing the right location.

This study presents a method to evaluate different factors for different actors of sport industry in a systematic way. Private investors can use the method for securing sufficient number of potential customers in a well-selected location. Government institutions and public policymakers can use the method, possibly with different sets of factors, to decide on the location of public sports facilities to maximize the number of visitors or to reach disadvantaged or underserved populations.

This framework of AHP method can help private and public investors and policymakers evaluate and make the optimal decision for choosing sports facility locations. This study contributes both to sport management theory and practice as well as to operation management literature. This study also refined the scattered factors in the literature of selecting a sport facility site in a more understandable and adaptable way.

The facility in an emergency system could be immobilized because of the huge destructive power of an irregular emergency and the uncertainty of the time, place and scale of occurrence. So facility failure scenarios must be considered at the time of location. The purpose of this paper is to establish a location model based on the worst facility failure, the objective of which is to minimize the cost and cover the demand maximally. It is demonstrated that location choice, considering facility failure, has significant meaning when considering economic benefit and covering the demand.

A bi‐level programming model which studies the facility location is established by using the methods of scenario analysis and robust optimization. It is compared with a classic location model, without considering facility failure, from the points of view of economic benefit and maximal covering demand.

Compared to the classic location model, without considering facility failure, it is demonstrated that the location model which considers facility failure can save more costs from the economic benefit point of view and, from the maximal covering of the demand point of view, has a higher covering ratio. So facility failure scenario should be considered in the location of an emergency facility.

The paper studies facility location based on the worst scenario, from the two aspects of economic benefits and maximal covering demand.

The purpose of this study is to discuss the principles and factors that influence the site selection of emergency medical facilities for public health emergencies. This paper discusses the selection of the best facilities from the available facilities, proposes the capacity of new facilities, presents a logistic regression model and establishes a site selection model for emergency medical facilities for public health emergencies in megacities.

Using Guangzhou City as the research object, seven alternative facility points and the points' capacities were preset. Nine demand points were determined, and two facility locations were selected using genetic algorithms (GAs) in MATLAB for programing simulation and operational analysis.

Comparing the results of the improved GA, the results show that the improved model has fewer evolutionary generations and a faster operation speed, and that the model outperforms the traditional P-center model. The GA provides a theoretical foundation for determining the construction location of emergency medical facilities in megacities in the event of a public health emergency.

First, in this case study, there is no scientific assessment of the establishment of the capacity of the facility point, but that is a subjective method based on the assumption of the capacity of the surrounding existing hospitals. Second, because this is a theoretical analysis, the model developed in this study does not consider the actual driving speed and driving distance, but the speed of the unified average driving distance and the driving distance to take the average of multiple distances.

The results show that the method increases the selection space of decision-makers, provides them with stable technical support, helps them quickly determine the location of emergency medical facilities to respond to disaster relief work and provides better action plans for decision makers.

The results show that the algorithm performs well, which verifies the applicability of this model. When the solution results of the improved GA are compared, the results show that the improved model has fewer evolutionary generations, faster operation speed and better model than the intermediate model GA. This model can more successfully find the optimal location decision scheme, making that more suitable for the location problem of megacities in the case of public health emergencies.

The research findings provide a theoretical and decision-making basis for the location of government emergency medical facilities, as well as guidance for enterprises constructing emergency medical facilities.