Abstract In the context of disaster management systems, effective waste management processes represent a critical component in terms of environmental sustainability, public health, occupational health, and safety. The objective of the present study is to ascertain the most suitable locations for the provisional storage of debris waste that will be generated following a severe and destructive earthquake for the city of Istanbul, which is highly susceptible to seismic activity, to minimize the financial costs of waste management and the harmful effects of waste. This study integrates geographic information systems (GIS) with the RANking Comparison Method (RANCOM), a multi-criteria decision-making (MCDM) method. The most suitable candidate sites for temporary storage areas (TSAs) for debris waste were determined based on criteria established in accordance with expert opinions, literature studies, and guidelines prepared by international organizations such as the United Nations (UN), the United States Federal Emergency Management Agency (FEMA), and the Environmental Protection Agency (EPA). 15 potential TSAs were identified for the entire Istanbul Province based on expert opinions regarding the specified criteria. In the accuracy assessment of the GIS-based maps obtained, the overall accuracy was calculated as 92%, with a kappa value of 0.94. It is assumed that the developed hybrid methodology can be applied to all other disaster scenarios.

This study addresses the site selection problem for energy storage systems (ESSs) as a multi-criteria decision-making problem (MCDM) under conditions of uncertainty. First, potential candidate locations were identified using the K-means clustering algorithm based on the geographic coordinates of existing solar power plants (SPPs). As a result, six alternative locations representing spatial concentration were identified. These alternatives were then evaluated using the fuzzy TOPSIS method, a multi-criteria decision-making method (MCDM), taking into account the ten criteria defined for this study. Expert assessments were expressed and transformed into triangular fuzzy numbers to capture uncertainty and subjectivity in the decision-making process. The results show six alternative options, ranked from the one with the highest proximity coefficient to the one with the lowest. The findings demonstrate that the integrated use of machine learning (ML) and fuzzy TOPSIS methods provides an effective and robust decision support framework for ESS location selection problems. This approach also serves as a guide for other renewable energy planning practices.

The continuous growth of private vehicle usage in Indonesia has led to a significant increase in fuel demand, making the strategic placement of gas stations a critical issue for transportation infrastructure planning. However, inappropriate site selection may result in uneven service coverage, affecting increased operational costs and reduced accessibility for road users. Therefore, a systematic and objective decision-making approach is required to support gas station location planning. Motivated by this challenge, this study develops an integrated decision-support framework to evaluate and select strategic gas station sites based on multiple criteria. The framework combines the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The AHP method is employed in the first stage to determine the relative importance weights of the evaluation criteria based on expert judgments. In the second stage, the TOPSIS method is implemented to rank candidate locations and identify the alternative closest to the ideal solution. To validate the proposed framework, a case study involving multiple candidate locations is experimented with. Experimental results demonstrate that the proposed AHP–TOPSIS approach is a practical tool for selecting gas station site location, with location L3 identified as the most strategic site for gas station construction.

Multi-agent systems of large language models as weight assigners: An approach to collaborative weighting in spatial multi-criteria decision-making

This paper proposes an integrated ranking approach to assess and rank the locations for solar energy plant establishment by combining improved symmetry point of criterion (SPC), pivot pairwise relative criteria importance assessment (PIPRECIA) and multi-attribute multi-objective optimisation based on the ratio analysis (MULTIMOORA) within intuitionistic fuzzy information (IFI) called as the ‘IF-modified SPC-PIPRECIA-MULTIMOORA’. We first estimate the weights of decision experts (DEs) using an IF-score function-based model, and in this line, a new score function is developed with some elegant axioms on IFI. Second, weights of criteria are computed with a combined objective-subjective weighted model, which contains the IF-improved SPC for objective weight and the IF-PIPRECIA for subjective weight of each criterion. Third, a new exponential distance measure for IFI is developed to evade the limitation of extant IF-distance measures and also presents some interesting properties. To confirm the feasibility and effectiveness of the proposed ranking framework, it is implemented in a case study of the solar energy plant site selection problem. Sensitivity assessment is made to see the impact of variation of weighting factor for prioritizing the solar energy plant sites, which depends on the weights of different criteria. Moreover, a comparison with various existing methods is discussed to show the superiority of the developed framework on IFI.

Abstract. The rapid growth of electric vehicles (EVs) necessitates the strategic placement of charging stations to support widespread adoption and ensure sustainable transportation. This study employs Geographic Information Systems (GIS) and spatial optimization techniques to address the site selection problem for electric vehicle charging stations (EVCS) in the state of Minnesota, United States. Specifically, it investigates how many and where new EVCSs should be added to complement the existing network and meet demand. Unlike previous studies focusing on urban areas, this research conducts a statewide analysis, accounting for spatial variations in EVCS distribution by applying different coverage radii for metropolitan and non-metropolitan areas. The results indicate that the selected sites enhance coverage, particularly in metropolitan regions. This research provides transportation practitioners with an adaptable framework validated through a case study in Minnesota, offering valuable insights into GIS-based site selection methods for EVCS.

The site selection of catalyst elements plays a crucial role in urban micro-renewal. Existing site selection models are incapable of configuring multiple types of elements in parallel and exhibit limited capacity in translating urban spatial structures and balancing conflicting stakeholder interests, failing to meet the comprehensive and complex requirements inherent in catalyst element site selection problems. Drawing on the perspectives of urban planning, operations research, and computer science, this study proposes a Multi-objective Ccrossover Parallel Combinatorial Optimization (MCPCO) model for the site selection of catalyst elements, along with a corresponding optimization method. This model uses concise and universal model architecture to map complex and specific real-world problems, optimizing the simultaneous configuration of multiple types of catalyst elements under multiple and conflicting objectives. An empirical study, using the renewal of the Liuhe Confucian Temple historical area in Nanjing as a case study, demonstrates that the model effectively maps and solves the site selection problem of catalyst elements in urban micro-renewal, providing a useful reference for similar problems especially characterized by parallel site selection of multiple types of elements.

The decarbonisation of energy systems has been a key driver for the advancement of renewable energy sources, including offshore wind, tidal, wave, and ocean thermal energies. As these resources increasingly become integral to national energy matrices, particularly in marine energy, it is crucial to analyse them using Multi-Criteria Decision-Making (MCDM) methods. This study aims to identify the primary topics addressed in MCDM applications related to Marine Renewable Energies (MREs) through a systematic literature review spanning the years 2000–2024. The research process involved five stages: (1) defining the problem, (2) surveying relevant papers, (3) conducting a preliminary analysis of the papers, (4) performing a detailed review, and (5) selecting the final sample of articles for analysis. The findings reveal a strong focus on offshore wind, wave, tidal, and ocean thermal energy studies, often combining multiple technologies. A classification of the research topics indicates that the majority of publications address site selection and spatial planning problems, employing methods such as AHP, PROMETHEE, TOPSIS, and Fuzzy techniques, frequently integrated with GIS tools. Conversely, research on governance, legal, and licencing aspects of these technologies within the broader marine sector remains limited. While the development of MREs is recognised as a significant opportunity, challenges such as technological maturity, design standardisation, reliability, accessibility, and governance must be addressed to enable their widespread adoption.

The scientific site selection for pallet pooling systems is pivotal to enhancing logistics efficiency and environmental performance. However, previous studies mainly adopt single-objective optimization approaches, which fail to simultaneously account for economic, environmental, and operational performance factors. The contribution of this paper lies in proposing an integrated decision-making method based on BWM-GIS-DEA to address the site selection problem for pallet pooling service centers. First, the Best-Worst Method (BWM) determines the weights of 13 criteria across 5 dimensions: economic, transportation, geographical location, technological, and service coverage. These criteria include factors such as the distribution density of pallet manufacturers and potential customers. Then, suitability maps are generated using Geographic Information System (GIS) spatial overlay technology to identify 6 alternative cities. Finally, a two-layer Data Envelopment Analysis (DEA) model is applied to measure the efficiency of the alternative sites. This method is applied in Inner Mongolia, China, and Ejin Horo Banner is identified as the optimal site with an efficiency score of 1.156, demonstrating superior resource allocation characterized by lower land costs and higher pallet turnover rates. The proposed framework not only fills a methodological gap in sustainable facility location research but also provides a replicable and policy-ready tool to guide practical decision-making.

The study of site selection problems has rapidly advanced with societal progress, evolving into a systematic science. This paper reviews existing theoretical methods for site selection optimization, noting their maturity and widespread application in modern society, and specifically references their relevance to the strategic placement of express cabinets. The optimal placement of express cabinets directly impacts customer satisfaction and logistics efficiency, while also considering urban planning and traffic conditions. This research leverages a Genetic Algorithm for the optimization, demonstrating how computational intelligence, a key facet of artificial intelligence, can effectively address complex real-world logistical challenges. The integration of AI-driven approaches, such as advanced metaheuristics, is crucial for developing robust and adaptive logistics networks that can respond to dynamic urban environments and evolving consumer demands.

Since the introduction of fuzzy sets by Zadeh in 1965 [1], a lot of new theories regarding imprecision and uncertainty have been introduced. Some of these theories are extensions of fuzzy set theory, other try to handle imprecision and uncertainty in different way. The extensions of ordinary fuzzy sets are classified into two broad categories: 1. Intuitionistic fuzzy sets [2] and their versions, 2. Neutrosophic sets [3] and their versions. The first group extensions can be defined by a membership degree and a non-membership degree, whereas the second class of extensions can be defined by a membership degree (truthiness), a non-membership degree (falsity), and a hesitancy degree (indeterminacy). Spherical and picture fuzzy sets fall into the same group because of the definition of membership functions. The squared sum of membership, nonmembership, and hesitancy degrees is equal to or less than 1.0 in spherical fuzzy sets whereas it is valid for the first degree sum in picture fuzzy sets. In this paper, we unify the concepts of picture fuzzy set and spherical fuzzy set into a broad class and name it as spherical picture fuzzy set (SP F S). In SP F Ss, every element of the universe is represented by a sphere. This unique geometrical representation is more adaptable and adequate for handling ambiguity in multi criteria decision-making. A new distance measure of spherical picture fuzzy sets is illustrated, and it is shown that it satisfies conditions of the distance measure. Besides investigating the structural properties of SP F S, set-theoretical operations along with some basic algebraic operations and aggregation operators are discussed. Oneof the most popular multi-criteria decision-making techniques, T OP SIS, is expanded to its SP F S form. To demonstrate the effectiveness and feasibility of the proposed SP F S-T OP SIS methodology for managing inherent vagueness in the given data, a numerical case study is analyzed wherein the methodology is applied to the pandemic hospital site selection problem.

To promote the effective utilization of distributed power sources after grid connection and achieve the goal of maximizing energy transmission efficiency and minimizing cost, this paper proposes a scheme based on the integration of the improved particle swarm optimization algorithm and the improved ant colony optimization algorithm (IPSOACO). This scheme first adopts the reactive power correction method to process various types of nodes. Secondly, the traditional particle swarm optimization (PSO) and ant colony optimization (ACO) algorithms are improved to solve problems such as slow optimization speed in the early and late stages of optimization, premature convergence, and being prone to fall into local optimum. The optimal solution of the improved PSO algorithm is combined with the initial value of the ant colony algorithm and deployed in the IEEE33 node system for site selection. Compared with the traditional particle swarm optimization algorithm fused with Ant colony optimization algorithm (PSOACO), the improved algorithm is more prominent in reducing power loss and improving voltage quality. It solves problems such as poor voltage quality, high network loss and limitations of the optimization algorithm in the IEEE 33-node system, and improves the computational efficiency and stability of the system to a certain extent. It provides a better solution for the research on the location and capacity of distributed power sources in the distribution network.

A vast majority of decision problems consist of conflicting criteria and non-dominating alternatives. To solve such kinds of decision problems, multiple criteria decision-making (MCDM) techniques have widely been used in many fields, so far. Moreover, hybrid decision-making approaches are also developed by the integration of different MCDM techniques in order to utilize the benefits of those methods. The main aim in this study is to introduce an integrated decision-making method for decision problems under uncertainty. Within this approach, Hesitant Fuzzy Linguistic Term Sets (HFLTS) and Hesitant Fuzzy Technique for Order Preference by Similarity to Ideal Solution (HF-TOPSIS) are integrated. The HFLTS method is used for the determination of criteria weights and decision alternatives are evaluated by using the HF-TOPSIS technique. The applicability of the method is demonstrated on an application of the wind turbine location problem.

Space-to-ground laser communication (SGLC) offers a paradigm-shifting solution to overcome the bandwidth constraints of radio frequency systems by leveraging laser beams for ultra-high data throughput, although its link availability probability is significantly affected by atmospheric conditions such as cloud cover. Existing ground station (GS) placement methods decouple site selection from downlink scheduling, failing to effectively quantify the data throughput of candidate sites. This study proposes a data throughput-driven joint optimization framework that integrates downlink scheduling into the site selection model for the first time. Additionally, the site selection model also incorporates equipment cost constraints and service capacity limitations by introducing an integer variable Q to characterize the deployment scale of laser communication terminals (LCTs) at each GS. Through auxiliary variable linearization techniques, the site selection problem is transformed into a tractable integer linear programming (ILP) formulation. A branch-and-bound algorithm is proposed to achieve global optimal solution search. Numerical results demonstrate that the proposed approach improves data throughput compared to the existing method.

Abstract Flexible interconnection, as a novel power system architecture, effectively enhances system reliability and economy. The problem of site selection and capacity optimization for flexible interconnection represents a significant component of flexible interconnection planning. Its objective is to identify the most advantageous locations and capacities for flexible interconnection devices while adhering to the constraints of system security and economic viability. This paper proposes a solution method based on an improved Crested Porcupine Optimizer (CPO) to address this problem. By introducing adaptive crossover strategies and elite strategies, the improved CPO enhances its global search ability and convergence speed. Furthermore, a novel fitness function is adopted to better measure the comprehensive benefits of flexible interconnection site selection and capacity optimization schemes. The results of the simulation indicate that this methodology is capable of effectively addressing the challenges associated with flexible interconnection site selection and capacity optimization. This approach has demonstrated remarkable performance, with an overall efficiency enhancement surpassing 40%.

Wind energy is a promising alternative energy source to cover large amounts of electricity demand in African countries. Djibouti’s proximity to the Red Sea and its arid and semi-arid climate generate consistent and robust winds, contributing to its potential for wind energy. Notwithstanding its considerable potential, Djibouti has not been adequately examined in earlier studies to determine suitable sites for wind farms. The objective of this study is to develop a model by integrating CRiteria Importance Through Intercriteria Correlation and Combined Compromise Solution methods into a Geographic Information System-based decision-support system to establish a comprehensive framework for the selection of wind farm sites in Djibouti. Following an in-depth review of the literature, seven main criteria were identified to assess the suitability of potential sites for wind farm construction: wind velocity, changes in wind direction, ground slope, distance to urban areas, distance to road network, distance to energy transmission networks, and land use. The CRiteria Importance Through Intercriteria Correlation method objectively determines the relative importance of the criteria, identifying wind speed and proximity to power transmission networks as the most important, and ground slope and land use as less important than the other criteria. The Combined Compromise Solution method is employed to prioritize potential sites for wind farms, considering seven specified criteria. To enhance the reliability of the results derived from the Combined Compromise Solution method, validation was conducted utilizing the Multi-Attribute Ideal–Real Comparative Analysis method. The comparative analysis revealed a robust correlation between the results of the two methods, providing convincing evidence for the accuracy and reliability of the proposed decision-support system employed to determine the most suitable sites for wind farms in Djibouti. This study is expected to assist professionals and researchers in dealing with the wind farm site selection problem on an unprecedented scale and with exact coordinates through a decision-support system that concurrently integrates the most recent multi-criteria decision-making methods and Geographic Information System tools.

Abstract Solar energy is a renewable source that is suitable for local applications due to its low operating costs, its environmentally friendly structure, its simpler technology infrastructure, and its clean and inexhaustible supply. In Türkiye, the installation of solar power plants (SPP) is increasing in direct proportion to the demand for renewable energy. Within this scope, the site selection problem is vital in order to effectively solve the energy problems, minimize the existing costs, and ensure sustainability. The final decision of the SPP location selection involves numerous subjective and objective evaluation factors, making it a challenging and complex decision-making process. In this study, a new integrated model is introduced that combines fuzzy set theory, analytic hierarchy process (AHP), geographic information systems (GIS), and information axiom methods to address the SPP site selection problems. In this paper, an application has been presented to illustrate the effectiveness of the proposed model. In the context of this study, the most suitable location for a solar power plant (SPP) was identified among six different provinces in Türkiye (Erzurum, Kayseri, Mersin, Mugla, Sanliurfa, and Van) by considering both quantitative and qualitative criteria. These provinces have different characteristics and are located in different regions of Türkiye. The result of this investigation was that Mersin was determined to be the most appropriate location for the SPP. Furthermore, a sensitivity analysis has been performed in order to examine the impact of the weights of the evaluation criteria on the results of the application.

Waste collection and management represents critical strategic focal points in urban development planning. The establishment and maintenance of such systems contribute significantly to policymakers' pursuit of sustainable development objectives. The efficient collection, categorization, and disposal of diverse types of waste pose formidable challenges within urban governance. This study proposes a comprehensive framework for group decision analysis employing Analytic Hierarchy Process (AHP) and Combined Compromise Solution (CoCoSo) to address the optimal site selection problem for waste disposal facilities. In order to rigorously and scientifically address collective waste management issues, this paper engages ten experts to score and evaluate criteria for waste management and alternative site locations. Innovatively integrating Fuzzy methodology with AHP-CoCoSo, the authors optimize decision-makers' preference inputs. Through Fuzzy-AHP, decision-makers' weights and criteria weights are calculated, while Fuzzy-CoCoSo is utilized to determine the final collective decision ranking. By synthesizing the ratings from the ten experts, ideal decision outcomes are obtained to aid cities in selecting the most suitable waste disposal sites. This research contributes to the advancement of urban waste management strategies, offering a systematic approach that accounts for the diverse perspectives of stakeholders and the complex dynamics inherent in waste management decision-making.

Underground dam site selection is the process of selecting locations for dams constructed for the storage and management of groundwater to ensure the sustainable use of water resources. Underground dams store groundwater by utilizing underground aquifers, enabling more efficient and effective utilization of water resources. Particularly, the importance of underground dams has been observed to increase with global warming. They play a crucial role in various aspects, especially during periods of drought, in meeting agricultural irrigation and drinking water needs, among others. The construction of underground dams requires the simultaneous consideration of numerous criteria, thus turning the construction process into a decision-making problem. This decision problem is referred to in the literature as a multi-criteria decision-making (MCDM) problem. In this study, the site selection problem for a underground dam to be established in the province of Izmir has been addressed. In the problem at hand, there are five different alternatives consisting of districts within the province of Izmir and ten different criteria. These criteria and alternatives were determined by experts. In the study, the weights of the criteria were determined using the MAIRCA method, and the EDAS method was used for the selection of alternative locations. As a result of the study, Kınık district was identified as the most suitable alternative among the selected districts.

In recent years, shared e-scooters have become increasingly popular as a mode of transportation in urban areas. Shared e-scooters have emerged as a convenient and sustainable transportation option in urban areas, providing users with a flexible and efficient way to travel short distances within a city. Many service providers and local municipalities are interested in implementing shared e-scooter operational models. However, determining which operating model to prefer and what the service areas will be is a significant problem. We aimed to solve the implementation of three different operational models, the site selection problem of station locations, and service areas for Erzurum, the metropolitan city in this study. As shared e-scooter is quite a new transportation mode; information collected to assess the operational models’ sustainability performance may be indeterminate and vague. In this study, the Geographic Information System (GIS)-based hybrid multi-criteria decision-making (MCDM) method is proposed for the solution of implementation, site selection, and service areas problems of three different shared e-scooter operational models. To this end, a four-step scientific and strategic solution approach is developed: (i) the identification and detailed explanation of 5 main and 24 sub-criteria, (ii) the weighting of criteria through the Analytical Hierarchical Process (AHP), Multi-Influencing Factor (MIF), and Best–Worst Method (BWM) in order to increase the sensitivity and robustness of the study, (iii) obtaining a suitability map for the solution of implementation, site selection, and service areas problems of operational models, and (iv) assigning shared e-scooter stations and analyzing their performance levels with COmplex PRoportional ASsessment (COPRAS). The results show that, in Erzurum, the central three districts are the most suitable for service areas. The paper’s solution methodology can help service providers and policymakers invest in sustainable shared e-scooter operational models, even in situations of high uncertainty.