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The performance of Ardalan′s heuristic is compared with that of Teitz and Bart for the location of service facilities, where performance is assessed in terms of the accuracy of solutions. The comparison is made considering two kinds of location problem: p‐median and p‐median with maximum distance constraints. The results indicate that the Teitz and Bart method generally produces a better solution than the Ardalan method for both problems.

The p‐median problem involves the locating of a fixed number of facilities to serve a set of customers such that the aggregate distance travelled is minimized. Considers the p‐median problem with and without distance constraints. Solves the two versions of the p‐median problems utilizing the heuristic proposed by Teitz and Bart using three different data sets. Also provides optimal solutions to these problems using the Lagrangian relaxation and subgradient methods in the branch‐and‐bound procedure. Shows that the heuristic performs quite well except for the cases where the constraints are tight (few facilities and/or small maximum distance).

The purpose of this paper is to illustrate the use of the p-median model to construct optimal patrol areas. This can improve both time spent traveling to calls, as well as equalize call load between patrol areas.

The paper provides an introduction to the use of integer linear programs to create optimal patrol areas, as many analysts and researchers in the author’s field will not be familiar with such models. The analysis then introduces a set of linear constraints to the p-median problem that are applicable to police agencies, such as constraining call loads to be equal and making patrol areas geographically contiguous.

The analysis illustrates the technique on simplified simulated examples. The analysis then demonstrates the utility of the technique by showing how patrol areas in Carrollton, TX can be made both more efficient and equalize the call loads given the same number of patrol beats as currently in place.

Unlike prior applications of creating patrol areas, this paper introduces linear constraints into the p-median problem, making it much easier to solve than programs that have non-linear or multiple objective functions. Supplementary code using open source software is also provided, allowing other analysts or researchers to apply the model to their own data.

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.

The purpose of this paper is to study the optimization of the geographical location of a network of points of sale, so that each retailer can have access to a potential geographic market. In addition, the authors study the importance of the distance variable in the commercial viability of a point of sale and a network of points of sale, analysing if the best location for each point (local optimum) is always the best location for the whole (global optimum).

Location-allocation models are applied using p-median algorithms and spatial competition maximization to analyse the actual journeys of 64,740 car buyers in 1240 postal codes using a geographic information system (GIS) and geomarketing techniques.

The models show that the pursuit of individual objectives by each concessionaire over the collective provides poorer results for the whole network of points of sale when compared to coordinated competition. The solutions provided by the models considering geographic and marketing criteria permit a reduction in the length of journeys made by the buyers. GIS allows the optimal control of market demand coverage through the collaborative strategies of the supplying retailers, in this case, car dealerships.

The paper contributes to the joint research of geography and marketing from a theoretical and practical point of view. The main contribution is the use of information on actual buyer journeys for the optimal location of a network of points of sale. This research also contributes to the analysis of the correlation between the optimum local and optimum global locations of a commercial network and is a pioneering work in the application of these models to the automotive sector in the territorial area of the study.

Although the adoption of metal additive manufacturing (AM) for production has continuously grown, in-house access to production grade metal AM systems for small and medium enterprises (SMEs) is a major challenge due to costs of acquiring metal AM systems, specifically powder bed fusion AM. On the other hand, AM technology in directed energy deposition (DED) has been evolving in both: processing capabilities and adaptable configuration for integration within existing traditional machines that are available in most SME manufacturing facilities, e.g. computer numerical control (CNC) machining centers. Integrating DED with conventional processes such as machining and grinding into Hybrid AM is well suited for remanufacturing of metal parts. The paper aims to discuss these issues.

Classical facility location models are employed to understand the effects of SMEs adopting DED systems to offer remanufacturing services. This study identifies strategically located counties in the USA to advance hybrid AM for reverse logistics using North American Industry Classification System (NAICS) data on geographical data, demand, fixed and transportation costs. A case study is also implemented to explore its implications on remanufacturing of high-value parts on the reverse logistics supply chain using an aerospace part and NAICS data on aircraft maintenance, repair and overhaul facilities.

The results identify the candidate counties, their allocations, allocated demand and total costs. Offering AM remanufacturing services to traditional manufacturers decreases costs for SMEs in the supply chain by minimizing expensive new part replacement. The hubs also benefit from hybrid AM to repair their own parts and tools.

This research provides a unique analysis on reverse logistics through hybrid AM focused on remanufacturing rather than manufacturing. Facility location using real data is used to obtain results and offers insights into integrating AM for often overlooked aspect of remanufacturing. The study shows that SMEs can participate in the evolving AM economy through remanufacturing services using significantly lower investment costs.

This paper aims to present an application of location optimization techniques for deploying police facilities subject to budgetary and feasibility constraints. The objectives considered included minimizing the distances and maximizing the coverage of police stations over potential crime spots.

The optimization consists of two stages. In Stage 1, a minimum distance model is used to determine the locations of police stations. Given the locations of police stations, Stage 2 uses a maximum coverage model to determine the police patrol area. The framework is applied to a case in the Greater London Area. The authors also evaluate the resilience of the optimal solutions with the terrorist attack scenario on 7 July 2005 in Central London.

With the optimization models, it is shown that the average distance between police stations and potential crime spots is reduced by 19 per cent. The coverage percentage of potential crime spots is also increased from the existing 91.99 per cent to a nearly perfect 99.82 per cent. Nevertheless, the results reveal that the optimal police resources deployment is less resilient with respect to the existing one. The findings herein suggest the importance of incorporating measures of resilience into the optimization framework and the authors leave this topic for further investigation.

The study highlights the value of location optimization to police force deployment in terms of finding the optimal locations of police force with respect to the spatial distribution of crimes. In particular, the authors investigate its implication on urban resilience, which is among the first study of this kind.

The purpose of this paper is to present a spatial decision support system (SDSS) to be used by the local authorities of a city in the planning and response phase of a disaster. The SDSS focuses on the management of public spaces as a resource to increase a vulnerable population’s accessibility to essential goods and services. Using a web-based platform, the SDSS would support data-driven decisions, especially for cases such as the COVID-19 pandemic which requires special care in quarantine situations (which imply walking access instead of by other means of transport).

This paper proposes a methodology to create a web-SDSS to manage public spaces in the planning and response phase of a disaster to increase the access to essential goods and services. Using a regular polygon grid, a city is partitioned into spatial units that aggregate spatial data from open and proprietary sources. The polygon grid is then used to compute accessibility, vulnerability and population density indicators using spatial analysis. Finally, a facility location problem is formulated and solved to provide decision-makers with an adaptive selection of public spaces given their indicators of choice.

The design and implementation of the methodology resulted in a granular representation of the city of Lima, Peru, in terms of population density, accessibility and vulnerability. Using these indicators, the SDSS was deployed as a web application that allowed decision-makers to explore different solutions to a facility location model within their districts, as well as visualizing the indicators computed for the hexagons that covered the district’s area. By performing tests with different local authorities, improvements were suggested to support a more general set of decisions and the key indicators to use in the SDSS were determined.

This paper, following the literature gap, is the first of its kind that presents an SDSS focused on increasing access to essential goods and services using public spaces and has had a successful response from local authorities with different backgrounds regarding the integration into their decision-making process.

Liquefied petroleum gas (LPG) transportation risks depend on aspects such as the total length of the trip and population density along the route. Choosing to deliver the product on non-busy days and reducing distances travelled may help to reduce these risks and lower the level of air pollution generated by the transportation trucks. The purpose of this paper is to reduce LPG delivery impact.

A three-stage methodology is proposed. First, rules are created in order to choose which clients have to be visited each day to avoid deliveries in downtown areas during business days. Second, an Iterated Local Search (ILS) metaheuristic is proposed for the capacitated p-median problem to group the chosen customers. Finally, another ILS is proposed to solve the Travelling Salesman Problem, for each truck to follow a better route while visiting its customers.

The methodology resulted in a 24.8 per cent reduction in distances travelled, representing an annual reduction of 32,716 kg in CO₂ emissions. The average amount of product sold per kilometre travelled improved by 72 per cent.

The literature shows a clear need for companies to consider sustainability in their daily decisions. However, especially in developing countries, there is a fear that protecting the environment may cost money. This main contribution of this paper is that it presents a real solution, serving as a guide for companies to improve their transportation system, resulting in environmental and economic benefits.

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.