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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.

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.

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.

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.

An integrated model for distribution network design is proposed which explicitly represents the required level of customer service.

Presents an AHP (analytical hierarchy process) decision model for facility location selection from the view of organizations which contemplate locations of a new facility or a relocation of existing facilities. The AHP model provides a framework to assist managers in analysing various location factors, evaluating location site alternatives, and making final location selections. The primary principle of the AHP model is to match decision‐makers’ preferences with location site characteristics. The model requires that a number of potential sites have been proposed. Alternatives are then evaluated and compared under both quantitative and qualitative factors to allow managers to incorporate managerial experiences and judgement in the solution process. Uses an example problem to illustrate the solution process. Addresses managerial implications for future research.

The purpose of this paper is to find a sustainable facility location solution for a closed-loop distribution network in the uncertain environment created by of high levels of product returns from online retailing coupled with growing pressure to reduce carbon emissions.

A case study approach attempts to optimize the distribution centre (DC) location decision for single and double hub scenarios. A hybrid approach combining centre of gravity and mixed integer programming is established for the un-capacitated multiple allocation facility location problem. Empirical data from a major national UK retail distributor network is used to validate the model.

The paper develops a contemporary model that can take into account multiple factors (e.g. operational and transportation costs and supply chain (SC) risks) while improving performance on environmental sustainability.

Based on varying product return rates, SC managers can decide whether to choose a single or a double hub solution to meet their needs. The study recommends a two hub facility location approach to mitigate emergent SC risks and disruptions.

A two-stage hybrid approach outlines a unique technique to generate candidate locations under twenty-first century conditions for new DCs.