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Abstract Purpose Blood donors are crucial in maintaining the blood supply chain. This study aims to improve the location and distribution of blood donation centers by focusing on two main objectives: minimizing costs and maximizing quality. Minimizing costs includes setting up and transporting blood efficiently while maximizing quality to ensure that blood products are delivered to hospitals promptly and in the right quantities. Methods A multi-objective mathematical model is proposed to address the placement of both fixed and mobile blood donation centers. The first objective focuses on minimizing the costs of setting up centers and transporting blood. The second objective aims to maximize quality by ensuring timely deliveries and meeting hospitals’ blood demand. The model utilizes real-world traffic and blood donation data from urban settings to simulate its effectiveness and applicability in practice. The model uses the $$\varepsilon$$ ε constraint method to optimize both objectives simultaneously. Results The model was tested in various scenarios, optimizing cost and quality separately. The algorithm determined the ideal locations for blood donation centers to meet demand by exploring different options. It also accounted for factors that reduce quality, such as delayed deliveries and product returns, and showed that these issues could be minimized. Conclusion This study highlights the need to balance cost and quality when determining the locations of blood donation centers. Using the $$\varepsilon$$ ε constraint method, the model successfully optimized both objectives, offering valuable insights for improving the efficiency and effectiveness of blood donation operations.

Abstract Emergency medical services (EMS) is a system that provides emergency medical care for incidents involving serious illness or injury. The location of EMS stations plays an essential role in delivering effective and efficient medical services. Numerous location models have been developed for locating and sizing EMS stations. However, it remains challenging to satisfy all EMS planning criteria within a single location model. In this study, a multi-period capacitated facility location problem with maximum travel time and backup service (EMSLSP) is proposed for locating and sizing EMS stations. The most important criteria for EMS planning are taken into account in EMSLSP: the demand changes due to population mobility, the maximum service capacity of an ambulance, the maximum number of ambulances at each EMS station, the maximum travel time from each EMS station to the locations it serves, the full coverage of dynamic demand, the minimum percent of population covered by EMS service in a specific travel time, and a backup station for each demand location in case of need. A case study in Zhengzhou, a large city in China, demonstrates that effective and efficient locations and sizes of EMS stations can be determined by solving the EMSLSP with various planning parameters. Compared with the existing EMS systems, the average ambulance travel time and the percentage of the population served are significantly improved. Simulations of ambulance scheduling confirm that the relocated and resized EMS stations perform better than those in the existing system. The evaluation-optimization-simulation method outlined in this paper provides a comprehensive and effective approach for EMS station planning.

The present locations of city benches show the required continuous walking distance, which can be defined as the distance from a city bench to the furthest neighboring one, called the furthest neighbor distance (FND). However, we lack a method for identifying where additional city benches should be installed incrementally to reduce the FND. The aim of this paper is thus (1) to develop a new method for determining the priority of the locations of new benches, subject to the number of city benches; and (2) to investigate the relationship between the priority and the reduction of the FND. The reduction of the FND is quantified based on the probability of an FND greater than 100 m. The empirical study around Tokyo Central Station showed that if reducing the probability of the FND being greater than 100 m is the top priority, additional city benches should be installed simultaneously. [ABSTRACT FROM AUTHOR]

We address in this paper a variant of the scheduling-location (ScheLoc) problem on tree networks with interval edge lengths where the total deviation of the uncertain data cannot exceed a threshold. We further use the minmax regret concept to deal with the corresponding uncertainty. In order to solve the problem, we investigate the structure of the schedule which leads to the maximum regret value at a fixed point. Then we consider the machine location belonging to a specific edge of the tree and partition the underlying edge into regions with linear maximum regret function. Finally, we develop a combinatorial algorithm that solves the minmax regret ScheLoc problem in polynomial time based on a finite dominating set approach. [ABSTRACT FROM AUTHOR]

Emergency medical services (EMS) is a system that provides emergency medical care for incidents involving serious illness or injury. The location of EMS stations plays an essential role in delivering effective and efficient medical services. Numerous location models have been developed for locating and sizing EMS stations. However, it remains challenging to satisfy all EMS planning criteria within a single location model. In this study, a multi-period capacitated facility location problem with maximum travel time and backup service (EMSLSP) is proposed for locating and sizing EMS stations. The most important criteria for EMS planning are taken into account in EMSLSP: the demand changes due to population mobility, the maximum service capacity of an ambulance, the maximum number of ambulances at each EMS station, the maximum travel time from each EMS station to the locations it serves, the full coverage of dynamic demand, the minimum percent of population covered by EMS service in a specific travel time, and a backup station for each demand location in case of need. A case study in Zhengzhou, a large city in China, demonstrates that effective and efficient locations and sizes of EMS stations can be determined by solving the EMSLSP with various planning parameters. Compared with the existing EMS systems, the average ambulance travel time and the percentage of the population served are significantly improved. Simulations of ambulance scheduling confirm that the relocated and resized EMS stations perform better than those in the existing system. The evaluation-optimization-simulation method outlined in this paper provides a comprehensive and effective approach for EMS station planning. [ABSTRACT FROM AUTHOR]

Given a set of existing points with their associated positive weights on the plane, the classical Fermat–Weber problem aims to find a new point that minimizes the overall weighted distances to the existing points. We investigate, in this paper, the Fermat–Weber problem with budgeted-uncertain interval point weights. The minmax regret criterion is applied to identify the meaningful solution. We first study some structural properties of the problem. Then, we develop an efficient algorithm to solve the corresponding problem, where the swarm-oriented algorithms are applied as subroutines. Computational results show the benchmarking results for these proposed methods. An examination is presented on identifying the minmax regret Fermat–Weber point concerning established a new airport in Vietnam, alongside its application in product transfer scenarios. [ABSTRACT FROM AUTHOR]

We address the problem of modifying vertex weights of a tree in such an optimal way that a given facility (vertex) becomes a 1-median in the modified tree. Here, each modifying cost receive any value within an interval. As the costs ar.e not exactly known, we apply the concept of absolute robust and minmax regret criteria to measure the cost functions. We first consider the absolute robust inverse 1-median problem with sum objective function. The duality of the problem helps to know the convexity of the induced univariate minimization problem. Based on the convexity, an O (n log 2 n) time algorithm is developed, where n is the number of vertices on the underlying tree. We also apply the minmax regret criteria to the uncertain inverse 1-median problem with Chebyshev norm and bottleneck Hamming distance. It is shown that in the optimal solution there exists exactly one cost coefficient attaining the upper bound and the others attaining their lower bounds. Hence, we develop strongly polynomial-time algorithms for the problems based on this special property. [ABSTRACT FROM AUTHOR]

Considering the increasing competition among manufacturers and the diversification of customer orders, it is important to use the optimal production system. Hybrid manufacturing systems have proven to be a viable solution to achieve a satisfactory level of customization while maintaining adequate production volumes. This article determines the optimal strategy for decision makers in order to choose the appropriate method of supplying customers’ orders. In this article, a hybrid MTS-MTO production system has been studied in two different scenarios. For this purpose, a mixed-integer linear bi-objective model with product substitution and replaceable materials has been developed and solved by a Lagrangian Relaxation algorithm. The findings of this study reveal that enabling product substitution by manufacturers leads to an increase in the number of accepted orders, along with resulting in a higher average level of customer dissatisfaction. The system prioritizes the fulfillment of orders from permissive customers. Also, with the increase of uncertainty in each of the supplying methods, manufacturers are using other sources to supply raw materials to satisfy the accepted orders. [ABSTRACT FROM AUTHOR]

The challenge of optimizing the distribution path for location logistics in the cold chain warehousing of fresh agricultural products presents a significant research avenue in managing the logistics of agricultural products. The goal of this issue is to identify the optimal location and distribution path for warehouse centers to optimize various objectives. When deciding on the optimal location for a warehousing center, various elements like market needs, supply chain infrastructure, transport expenses, and delivery period are typically taken into account. Regarding the routes for delivery, efficient routes aim to address issues like shortening the overall driving distance, shortened travel time, and preventing traffic jams. Targeting the complex issue of optimizing the distribution path for fresh agricultural products in cold chain warehousing locations, a blend of this optimization challenge was formulated, considering factors like the maximum travel distance for new energy trucks, the load capacity of the vehicle, and the timeframe. The Location-Route Problem with Time Windows (LRPTWs) Mathematical Model thoroughly fine-tunes three key goals. These include minimizing the overall cost of distribution, reducing carbon emissions, and mitigating the depletion of fresh agricultural goods. This study introduces a complex swarm intelligence optimization algorithm (MODRL-SIA), rooted in deep reinforcement learning, as a solution to this issue. Acting as the decision-maker, the agent processes environmental conditions and chooses the optimal course of action in the pool to alter the environment and achieve environmental benefits. The MODRL-SIA algorithm merges a trained agent with a swarm intelligence algorithm, substituting the initial algorithm for decision-making processes, thereby enhancing its optimization efficiency and precision. Create a test scenario that mirrors the real situation and perform tests using the comparative algorithm. The experimental findings indicate that the suggested MODRL-SIA algorithm outperforms other algorithms in every computational instance, further confirming its efficacy in lowering overall distribution expenses, carbon emissions, and the depletion of fresh produce in the supply chain of fresh agricultural products. [ABSTRACT FROM AUTHOR]

The location planning of relief distribution centres (DCs) is crucial in humanitarian logistics as it directly influences the disaster response and service to the affected victims. In light of the critical role of facility location in humanitarian logistics planning, the study proposes a two-stage relief distribution location problem. The first stage of the model determines the minimum number of relief DCs, and the second stage find the optimal location of these DCs to minimize the total cost. To address a more realistic situation, restrictions are imposed on the coverage area and capacity of each DCs. In addition, for optimally solving this complex NP-hard problem, a novel two-phase algorithm with exploration and exploitation phase is developed in the paper. The first phase of the algorithm i.e., exploration phase identifies a near-optimal solution while the second phase i.e. exploitation phase enhances the solution quality through a close circular proximity investigation. Furthermore, the comparative analysis of the proposed algorithm with other well-known algorithms such as genetic algorithm, pattern search, fmincon, multistart and hybrid heuristics is also reported and computationally tested from small to large data sets. The results reveal that the proposed two-phase algorithm is more efficient and effective when compared to the conventional metaheuristic methods. [ABSTRACT FROM AUTHOR]

Urban mobility planning is increasingly characterized by the pressure to establish low-emission, space-efficient, and socially inclusive transport services. One such planning task for decision-makers in public institutions is the efficient design of bicycle-sharing networks. This study examines the factors that influence decision-making in this process, both among practitioners and within the research literature on decision support systems (DSS). Employing a dual approach of academic literature review and 16 qualitative interviews with public sector planners, we identify and compare different dimensions of key factors affecting location choice. The results reveal significant contrasts, for instance, between the emphasis placed by academic researchers on ensemble optimization of quantifiable factors and the more target-complex, intuitive approaches pursued by public planning practitioners. We blend core characteristics from both perspectives in a synthesis analysis. We argue that future efforts should aim for more vertical planning assistance, citizen involvement for diversified demand indications, integration of local factors into DSS, and standardized data formats to enable better DSS integration. Our study offers a rare qualitative insight into a complex choice problem faced by public decision-makers, linked with predominantly quantitative research.

We present an innovative extension of the Set Covering Problem, transitioning from a traditional radial covering to an angular covering structure. The decisions are based on locating the facilities and identifying the directional servers installed in each, covering a set of points in a geographic area. The strategic placement of surveillance cameras inspired this novel approach. We aim to minimize the cost of covering demand points by strategically installing directional servers on facilities. We propose an integer linear model and an initial approach based on column generation decomposition. We conducted extensive computational experiments on different test instances, including a practical case study involving positioning security cameras for surveillance in Valparaíso, Chile. The results highlight the effectiveness of column generation in achieving optimal solutions or significantly improving solution quality compared with solving the model directly with standard optimization solvers, especially for larger instances. In particular, the column generation algorithm achieves up to a 67% improvement in solution quality compared to the optimization model for real-world size instances, demonstrating its practical applicability and potential for enhancing surveillance infrastructure design.

This paper deals with p-maxian problem on cycles with an upper bound on the distances of all facilities. We consider the case of p = 2 and show that, in the worst case, the optimal solution contains at least one vertex of the underlying cycle, which helps to develop an efficient algorithm to solve the constrained 2-maxian problem. Based on this property, we develop a linear time algorithm for the constrained 2-maxian problem on a cycle. We also discuss the relations between the constrained and unconstrained 2-maxian problems on which the underlying graphs are cycles. [ABSTRACT FROM AUTHOR]

Thedistribution of products stands out as one of the pivotal activities for logistics companies in recent years, particularly in the aftermath of the COVID-19 pandemic and other geopolitical events. Intense competition compels companies to efficiently execute their logistical processes, with cross-docking emerging as a frequently applied solution. However, the location of cross-dock terminals in urban areas remains a problem insufficiently addressed in the literature, with a dearth of studies and models tackling this issue. This paper introduces a novel and innovative model for locating cross-dock terminals based on the CI-DEA–IDOCRIW–MABAC (Composite Indicators–Data Envelopment Analysis-Integrated Determination of Objective Criteria Weights–Multi-Attributive Border Approximation Area Comparison) methods. In the process of defining input indicators, the following three sources were utilized: relevant literature, practical insights from logistics experts, and the knowledge and experience of the authors. Eight inputs and three outputs were considered (the number of users in the observed channel; the area served by the channel; the average distance a vehicle travels in one delivery; the required number of vehicles; labor availability; competition; construction, and expansion possibilities; proximity to the main infrastructure and traffic facilities; the average number of deliveries; average delivered quantity; and service level). The model underwent testing in a case study analyzing nine distribution channels (areas within the observed urban zone). The results indicated that alternative A4 (in the southwest area) ranked the highest since it was the best-ranked in accordance with the most important criteria, suggesting that the terminal is best located in the southwest zone. The accuracy of the results was confirmed by company management. By developing a completely new model and addressing the identified gap in the literature, this paper provides unequivocal scientific contributions. [ABSTRACT FROM AUTHOR]

For minimizing a sum of finitely many proper, convex and lower semicontinuous functions over a nonempty closed convex set in an Euclidean space we propose a stochastic incremental mirror descent algorithm constructed by means of the Nesterov smoothing. Further, we modify the algorithm in order to minimize over a nonempty closed convex set in an Euclidean space a sum of finitely many proper, convex and lower semicontinuous functions composed with linear operators. Next, a stochastic incremental mirror descent Bregman-proximal scheme with Nesterov smoothing is proposed in order to minimize over a nonempty closed convex set in an Euclidean space a sum of finitely many proper, convex and lower semicontinuous functions and a prox-friendly proper, convex and lower semicontinuous function. Different to the previous contributions from the literature on mirror descent methods for minimizing sums of functions, we do not require these to be (Lipschitz) continuous or differentiable. Applications in Logistics, Tomography and Machine Learning modelled as optimization problems illustrate the theoretical achievements [ABSTRACT FROM AUTHOR]

Drone logistics is a novel method of distribution that will become prevalent. The advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption, resulting in cost savings for the company's transportation operations. Logistics firms must discern the ideal location for establishing a logistics hub, which is challenging due to the simplicity of existing models and the intricate delivery factors. To simulate the drone logistics environment, this study presents a new mathematical model. The model not only retains the aspects of the current models, but also considers the degree of transportation difficulty from the logistics hub to the village, the capacity of drones for transportation, and the distribution of logistics hub locations.Moreover, this paper proposes an improved particle swarm optimization (PSO) algorithm which is a diversity-based hybrid PSO (DHPSO) algorithm to solve this model. In DHPSO, the Gaussian random walk can enhance global search in the model space, while the bubble-net attacking strategy can speed convergence. Besides, Archimedes spiral strategy is employed to overcome the local optima trap in the model and improve the exploitation of the algorithm. DHPSO maintains a balance between exploration and exploitation while better defining the distribution of logistics hub locations Numerical experiments show that the newly proposed model always achieves better locations than the current model. Comparing DHPSO with other state-of-the-art intelligent algorithms, the efficiency of the scheme can be improved by 42.58%. This means that logistics companies can reduce distribution costs and consumers can enjoy a more enjoyable shopping experience by using DHPSO's location selection. All the results show the location of the drone logistics hub is solved by DHPSO effectively. [ABSTRACT FROM AUTHOR]

The implementation of medical drones can quickly and efficiently expand the coverage range of an area, allowing for a faster response to incidences of out-of-hospital cardiac arrest and improving the subsequent survival rate of such incidences, while promoting sustainable health development goals under the configuration standards for automatic external defibrillators in China. In response to the problem of the selection of locations for medical drone vertiports (for take-off and landing) that are equipped with automatic external defibrillation facilities, a survival function was introduced to establish a model for site selection, with the primary optimization objective of maximizing the average survival rate of patients and taking the operating costs of a system into account. At the same time, considering the constraints of drone phase operation time, energy consumption, coverage range, etc., a medical drone vertiport site selection model was established for emergency cardiac arrest needs. An improved immune algorithm was applied to the model's calculations and the analysis of the results, using the Jinnan District in Tianjin as an example. The results show that the proposed model and algorithm are feasible and applicable. The Jinnan District in the city of Tianjin requires a total of 24 medical drone vertiports in order to achieve full coverage of an area under the "golden 4-minute" rescue time. When the average survival rate of patients is 0.9, the operation results are deemed optimal, and the average survival rate of patients is 64.06%. Compared to ground ambulances currently used in hospitals, the implementation of medical drones could significantly shorten response time, improve the average survival rate of patients by 41.96%, and effectively improve the existing low survival rate and the accessibility of medical services. The results of this study can provide decision-making support for the planning of automatic external defibrillators in public places and the construction of sustainable and efficient emergency medical service systems. [ABSTRACT FROM AUTHOR]

Locating facilities such as factories or warehouses is an important and strategic decision for any organization. Transportation costs, which often form a significant part of the price of goods offered, are a function of the location of the plans. To determine the optimal location of these designs, various methods have been proposed so far, which are generally definite (non-random). The main aim of the study, while introducing these specific algorithms, is to suggest a stochastic model of the location problem based on the existing models, in which random programming, as well as programming with random constraints are utilized. To do so, utilizing programming with random constraints, the stochastic model is transformed into a specific model that can be solved by using the latest algorithms or standard programming methods. Based on the results acquired, this proposed model permits us to attain more realistic solutions considering the random nature of demand. Furthermore, it helps attain this aim by considering other characteristics of the environment and the feedback between them.

The challenge of optimizing the distribution path for location logistics in the cold chain warehousing of fresh agricultural products presents a significant research avenue in managing the logistics of agricultural products. The goal of this issue is to identify the optimal location and distribution path for warehouse centers to optimize various objectives. When deciding on the optimal location for a warehousing center, various elements like market needs, supply chain infrastructure, transport expenses, and delivery period are typically taken into account. Regarding the routes for delivery, efficient routes aim to address issues like shortening the overall driving distance, shortened travel time, and preventing traffic jams. Targeting the complex issue of optimizing the distribution path for fresh agricultural products in cold chain warehousing locations, a blend of this optimization challenge was formulated, considering factors like the maximum travel distance for new energy trucks, the load capacity of the vehicle, and the timeframe. The Location-Route Problem with Time Windows (LRPTWs) Mathematical Model thoroughly fine-tunes three key goals. These include minimizing the overall cost of distribution, reducing carbon emissions, and mitigating the depletion of fresh agricultural goods. This study introduces a complex swarm intelligence optimization algorithm (MODRL-SIA), rooted in deep reinforcement learning, as a solution to this issue. Acting as the decision-maker, the agent processes environmental conditions and chooses the optimal course of action in the pool to alter the environment and achieve environmental benefits. The MODRL-SIA algorithm merges a trained agent with a swarm intelligence algorithm, substituting the initial algorithm for decision-making processes, thereby enhancing its optimization efficiency and precision. Create a test scenario that mirrors the real situation and perform tests using the comparative algorithm. The experimental findings indicate that the suggested MODRL-SIA algorithm outperforms other algorithms in every computational instance, further confirming its efficacy in lowering overall distribution expenses, carbon emissions, and the depletion of fresh produce in the supply chain of fresh agricultural products.

Parcel lockers have emerged as an effective solution to address inefficiencies in last-mile delivery. However, the selection of an appropriate location plays a crucial role in ensuring optimal service and enhanced logistic performance. To address this challenge, this work aims to define the best location for parcel lockers in the capital of Ireland, Dublin by conducting a novel decision-making model. The Spherical Fuzzy Analytic Hierarchy Process (SF-AHP) has been adopted to deal with the uncertainty in the decision maker preferences, while the Euclidean Distance-Based Aggregation Method (EDBAM) has been conducted to aggregate the decision makers preferences. The proposed integrated model is an applicable approach to elude AHP deterministic disadvantages, moreover, it is the first application of SF-AHP and EDBAM to select parcel lockers’ location. The findings, including the identification of the top three optimal locations, namely “Post office,” “On commercial area,” and “High population density area,” along with their respective weight scores, provide valuable insights for policymakers making strategic decisions regarding parcel locker placements in Dublin city.

This article examines genetic algorithms that are built on the "survival of the fittest" principle enunciated by Charles Darwin. By applying genetic algorithms to solving optimization problems, it is not always possible to guarantee the determination of the global optimum in polynomial time. This fact does not occur because only brute force search methods allow us to find the global optimum. Instead the genetic algorithm allows selecting good decisions, in a reasonable time, compared to other well-known deterministic or heuristic search engine optimization algorithms. The authors of this article develop an algorithm of solving the optimization problem of locations graph vertices in the line.

The effective distribution of perishable food items is a critical aspect of managing the food industry's supply chain, given their physical–chemical, biological characteristics and composition, which make them highly susceptible to rapid deterioration. This research presents a transport model incorporating a cross-dock system to efficiently deliver goods from production plants to markets. The model incorporates a vehicle routing model that considers time windows for pick-ups and deliveries, optimal cross-dock center locations, a heterogeneous vehicle fleet of limited capacity, and scheduling product collections, arrivals, and departures. The model is a mixed-integer non-linear optimization model that effectively minimizes logistics costs and environmental impacts by considering various parameters such as speed, waiting times, loading and unloading times, and costs associated with the entire operation. The findings demonstrate that the cross-dock structure is highly conducive to distributing perishable goods, achieved by minimizing collection and distribution operations, adhering to designated time windows, and efficiently allocating resources. The GAMS 23.6.5 software is used to program the model, employing various solution strategies, including experimental tests with scenarios, as well as the "posterior," "Pareto optimization," and "weighted sum" methods. The case study in Sincelejo (Sucre, Colombia) reported the best solution, representing 60% of logistics and 40% of environmental costs. The results show complete compliance with routes, no inventory generation, and the necessity of two inbounds and two outbound vehicles for collection from suppliers and delivery to retailers. This study presents an efficient model for managing the transportation of perishable goods, contributing to sustainable distribution activities, and environmental conservation in the food industry's supply chain. [ABSTRACT FROM AUTHOR]

Air transportation and airports are indispensable means of the modern world, where well-being and travel time reliability are pillars of strength. In the past few years, passengers traveling into, within, or out of Africa has enormously increased. However, the region lacks the basic facilities of linking African countries to the outside world. The research studies the possibility of assigning the optimal hub airport location in five North African countries, based on five main criteria. The criteria include airport pricing, hard and soft infrastructure, Catchment and landside access, as well as other aspects such as markets and airline partners. The study uses a hybrid grey-CODAS approach to decide the final priority of different decision alternatives. The method was implemented in steps to determine the criteria weights. Four experts participated in the evaluation to determine the importance of each criterion used for the ranking of suggested airport sites. The suggested sites include Cairo airport, Tripoli airport, Tunisia-Carthage airport, Algeria-Houari Boumediene airport, and Morocco-Mohammed V International airport. Model ranking suggested Morocco as the best alternative to locate a hub airport in North Africa. [ABSTRACT FROM AUTHOR]

Optimal location selection plays a crucial role in enhancing logistic performance and addressing inefficiencies in last-mile delivery. Parcel lockers have emerged as an efficient solution in this regard. In the context of optimizing last-mile logistics in Dublin, Ireland, this research strives to identify the ideal locations for parcel lockers. It does so by pioneering the application of a unique decision-making model. To navigate the inherent uncertainties in decision-makers' preferences, the study harnesses the power of the Analytic Hierarchy Process (AHP) in Spherical Fuzzy environment with the Euclidean Distance-Based Aggregation Method (EDBAM) for the selection of parcel locker locations. The harmonious integration of these methodologies serves to mitigate the deterministic shortcomings typically associated with the AHP approach, presenting an innovative and robust solution for enhancing last-mile delivery efficiency. The outcomes of this research will provide valuable insights to policymakers in making informed strategic decisions regarding the selection of parcel locker locations in Dublin City.

In many complex systems, fuzziness and randomness simultaneously appear in a system. Then the fuzzy random variable such that it can be an effective tool to determine some high-uncertainty phenomena. Moreover, in the real-world situation, the parameters of a location problem can be randomness and fuzziness at the same time. This means that, available random data of a location problem may be unsatisfactory, therefore the fuzzy information must be integrate with the random data. This paper deals with the classical and inverse median location problems on uncertain networks in which the vertex weights are fuzzy random variables. We introduce the new criteria probability-possibility, probability-necessity and probability-credibility to convert the fuzzy random p-median location problem into a quadratic programming problem on uncertain networks. For this purpose, new formula are introduced for the possibility, necessity and credibility criteria that are used for converting fuzzy variables into deterministic variables. Also, the inverse 1-median location problem with the fuzzy random vertex weights is reformulated as the deterministic inverse 1-median location problem under the probability-possibility, the probability-necessity and the probability-credibility criteria. Finally, using these criteria, the linear time algorithms are presented for the inverse 1-median location problems on the uncertain tree networks. [ABSTRACT FROM AUTHOR]

Many studies have incorporated particular models with various methods and algorithms to resolve the site selection problem for electric vehicle charging stations (EVCS). This paper systematically reviews research that evaluates geographic information systems (GIS) based EVCS location techniques and the variables used for decision making. We classify and characterize those techniques and variables to determine important linkages within the literature. A variety of databases were referenced to extract research published from 2010 to March 2023 pertinent to this specific location optimization problem, and 74 papers were selected after thorough evaluation. The models used in each paper were examined along with the methods for selecting variables and ranking alternate locations. Site selection for EVCS requires a multi-criteria decision making approach to meet the sustainability, efficiency, and performance goals of communities adopting electric vehicle mobility. Our results indicate that map algebra and data overlay methods have been used more frequently with GIS-based analysis than other techniques, while geographic and demographic variables are commonly the most significant site selection characteristics. The reviewed methods have most often been applied to urban locations; however, the transfer of these techniques to a rural EVCS site selection problem has been rarely explored in the current literature. This research assessment contributes relevant guidance for the application of methodologies useful in policymaking and provides recommendations for future research based on these findings. [ABSTRACT FROM AUTHOR]

The classical reverse 1-median problem on trees is to adjust the edge lengths within a budget so as to reduce the 1-median function at a predetermined vertex as much as possible. This paper concerns the corresponding problem with uncertain vertex weights presented by linear functions. Moreover, we use the minmax regret criterion to measure the maximum loss of a feasible solution with respect to the worst-case scenario. The regarding problem is called the minmax regret reverse 1-median problem on trees. We first partition the set of scenarios into parts such that the optimal solution of the corresponding reverse 1-median problem does not change in each part. Then the problem can be reformulated as the minimization of a quadratic number of affine linear functions. We finally develop a greedy algorithm that solves the problem in O (n 3) time where n is the number of vertices in the underlying tree. [ABSTRACT FROM AUTHOR]

The minimum covering location problem with distance constraints deals with locating a set of undesirable facilities on a geographical map, where there is a given minimum distance between any pair of located facilities. A covering radius is defined within which the population node is fully covered and beyond that it is not covered at all. This setting may not be applicable in practice because usually the coverage gradually decreases with an increase in the distance. Additionally, undesirable facilities may have a cooperative adverse impact on the nearby population. We introduce the gradual coverage to the problem that extends the classic definition of the coverage and is more suitable for modelling real-world applications. We name the problem the gradual minimum covering location problem with distance constraints (GMCLPDC). We propose a mixed-integer program for GMCLPDC where cooperation of facilities is also considered. We propose a threshold accepting heuristic as the solution method. We conduct computational experiments on instances with up to 10,000 nodes. The outcomes indicate that the heuristic delivers quality solutions and outperforms the solver Gurobi. We also show an application of our model in Sydney metropolitan area. [ABSTRACT FROM AUTHOR]

This paper presents some variants of minimal point theorem together with corresponding variants of Ekeland variational principle. In the second part of this article, there is a discussion on Ekeland variational principle and minimal point theorem relative to it in uniform spaces. The aim of these series of important results is to highlight relations between them, some improvements and specific applications.

Healthcare centers are one of the most important municipal facilities that are responsible for providing personal and social health. Locating medical centers is an important strategy to improve the performance of these facilities and reduce any delay in the relief process. During disasters, these centers play a critical role in reducing the risk of human casualties. In this regard, the current study aims to establish a new robust mathematical model to simultaneously locate and allocate healthcare facilities, including different levels of medical care services, considering the characteristics in normal and disaster situations. Further, to help victims and prevent overcrowding at medical centers during disasters, establishment of temporary and outpatient centers was suggested for emergency provision of basic services. In addition, the possibility of delivering the needed equipment and medical teams to these centers was incorporated in the proposed modeling. To solve the problem, two metaheuristic algorithms, harmony search algorithm and hybrid tabu search combined with variable neighborhood search algorithm, and a lower bound based on Lagrangian relaxation method were employed. To assess the performance of the proposed algorithms, sample problems were randomly generated. The results obtained from the exact solutions and lower bound from the Lagrangian relaxation method were compared with those from the meta-heuristic algorithms, confirming the good performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This research applies and modifies K-means clustering analysis from Data Mining to solving the location problem. First, a case study of Thailand's convenience store franchise in locating distribution centers (DCs) is conducted. Then, the final centroids are served at suggested DC locations. Besides the typical distance, Euclidean, used in K-means, Manhattan, and Chebyshev, is also experimented with. Moreover, due to the stores' different demands, a modification of the centroid calculation is needed to reflect the center-of-gravity effects. For the proposed centroid calculation, the above three distance metrics incorporating the demands as weights give rise to another three approaches and are thus named Weighted Euclidean, Weighted Manhattan, and Weighted Chebyshev, respectively. Besides the optimal locations, the effectiveness of these six clustering approaches is measured by the expected total distribution cost from DCs to their served stores and the expected Davies-Bouldin index (DBI). Concurrently, the efficiency is measured by the expected number of iterations to the final clusters. All these six clustering approaches are then implemented in the case study of locating eight DCs to distribute to 260 convenience stores in Eastern Thailand. The results show that though all approaches yield locations in close proximity, the Weighted Chebyshev is the most effective one having both the lowest expected distribution cost and lowest expected DBI. In contrast, Euclidean is the most efficient approach, with the lowest expected number of iterations to the final clusters, followed by Weighted Chebyshev. Therefore, the DC locations from Weighted Chebyshev could, ultimately, be chosen for this Thailand's convenience store franchise. [ABSTRACT FROM AUTHOR]

The popularity of unmanned aerial vehicle (UAV) technology has made UAV logistics transportation possible. However, based on the current development status of logistics UAVs, there are difficulties in using UAVs directly for door-to-door logistics transportation. Therefore, it is necessary to establish UAV parcel-receiving stations that can gather logistics needs in a small area. The construction of stations allows the UAVs to transport back and forth between the distribution warehouse and the established stations, enabling customers to send and receive packages at the more convenient stations. Based on the current situation, it is a more appropriate air–ground cooperative transport mode to solve the "last-mile" cargo transportation problem. In this paper, a bi-layer UAV parcel-receiving station and public air-route planning method is proposed to explore the interaction between station location and public route planning, and is solved with a genetic algorithm and max–min ant system (GA-MMAS). The model proposed in this paper can determine the location of the stations and plan the public air routes between the warehouse and stations simultaneously. Simulation results show that the planning results of the bi-layer optimization model proposed in this paper meet the requirements of station location and public air-route planning. Compared with the layered planning results, the cost of the upper-layer model is reduced by 5.12% on average, and the cost of the lower-layer model is reduced by 4.48%. [ABSTRACT FROM AUTHOR]

In recent years there is the tendency to charge Electric Vehicles (EVs) with clean sources of energy. Due to this tendency, scientific literature focused on problems of locating and sizing Photovoltaic (PV) Charging Stations (CSs). In this paper, we studied the case of the localization and sizing of PV powered CSs in the region of Valle de Aburrá, Colombia, which is the region of the country with the fastest growth of EV adoption. Looking to utilize the largest amount of PV power, we considered level 1 (low-power) PV-grid CSs. The use of other PV charging systems, like level 3 (high-power) charging, would require covering bigger roof areas with PV panels, more than the available on an urban landscape. Considering that this problem takes into account low-power CSs, candidate CS locations are places where people go for extended periods of time, with large roof areas and parking availability. In this problem, the decision variables are the location of the CSs, the amount of charging spots at each selected location, and the size of the PV system. These decisions aim to minimize the added annualized cost of the PV systems, the annualized cost of the charging spots in the selected CSs, and the cost of energy purchased from the grid in a year. For calculating the amount of power that the PV systems are capable of generating, we obtained the typical irradiation curves for each candidate CS location. For solving the studied problem, we propose a Mixed Integer Linear Programming (MILP) formulation. [ABSTRACT FROM AUTHOR]

Due to the growth and increasing competition in e)commerce in recent years, the last mile logistics activities are getting more and more complex and the costs related to these activities are increasing. For this reason, cargo companies adopt alternative delivery methods to reduce delivery costs and increase service quality. One of these alternatives is parcel locker delivery. Parcel lockers provide significant savings in delivery costs while providing flexibility in terms of delivery time. However, since parcel lockers are located into the central areas of the cities, they incur high rental costs. In this context, design of a parcel locker network emerges as an important facility location problem. In this study, a mathematical model has been developed for the cargo vending machine location problem. By using the proposed model, the locations of five parcel lockers in Buca district of İzmir province were determined. In addition, the proposed model is analyzed for different parameter values. According to the results obtained from the analyzes, the number of parcel lockers has a significant effect on the demand fill rate. In addition, the results of the analysis showed that with the changes in parcel locker capacity and discount system, the filled demand can be increased significantly without investing in a new parcel locker [ABSTRACT FROM AUTHOR]

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The increasing environmental pollution has led to the need to accelerate interest in electric vehicles. It is crucial to specify locations for electric vehicle charging stations (EVCSs) to meet the charge demand. The question that arises here is how to make a comprehensive evaluation of the alternative EVCS locations regarding sustainability. This study presents a new integrated group multi-criteria decision making (MCDM) approach for a robust evaluation of alternative EVCS locations. Two different group aggregation techniques (GATs) are applied to obtain the aggregated weights with AHP (analytical hierarchy process): aggregating individual judgments and aggregating individual priorities. For ranking alternative locations, two MCDM methods, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) were applied for both aggregated weights. Furthermore, we introduce two types of rankings from the sensitivity analysis based on the most selected alternatives for each rank position and the most selected rank position for each alternative. Finally, an integrated ranking is obtained by combining the results of group MCDM methods and sensitivity analysis to investigate the impact of GATs and MCDM methods. The proposed methodology is applied to rank the EVCS locations in Bursa, Turkey, with four main criteria and eight sub-criteria. The similarity measure results indicate that the GAT and the MCDM method have an impact on the evaluation scores and the rankings. The integrated group MCDM approach provides a comprehensive evaluation of the alternatives. [ABSTRACT FROM AUTHOR]

The paper presents a study of equilibrium in a two-sided market for network platforms with cross externalities between buyers and sellers. The proposed model is a generalization of Armstrong's (2006) monopoly model for the case of a duopoly in a two-sided market for network platforms located on a plane. The paper solves the problem of optimal pricing and investigates the question of the optimal location of platforms in the market, provided that the heterogeneous utility of agents of both groups (buyers and sellers) is formed taking into account the Hotelling specification with the Manhattan metric. [ABSTRACT FROM AUTHOR]

This paper is concerned with the robust 1-center location problems on the tree networks under minmax and minmax regret criteria, where the vertex weights and edge lengths of the underlying tree are considered as dynamic data or discrete set of scenarios. In the problem under minmax criterion, the aim is to find a point on the tree such that it minimizes the maximum cost. The problem under minmax regret asks to find a point on the tree such that minimizes the maximum regret. We develop the first optimal solution algorithms with polynomial time complexities for the problems under investigation. [ABSTRACT FROM AUTHOR]