Multi-criteria Decision-making Approach Research Articles

County-level prioritization for managing the Covid-19 pandemic: a systematic unsupervised learning approach

Purpose The COVID-19 pandemic has posed many challenges in almost all sectors around the globe. Because of the pandemic, government entities responsible for managing health-care resources face challenges in managing and distributing their limited and valuable health resources. In addition, severe outbreaks may occur in a small or large geographical area. Therefore, county-level preparation is crucial for officials and organizations who manage such disease outbreaks. However, most COVID-19-related research projects have focused on either state- or country-level. Only a few studies have considered county-level preparations, such as identifying high-risk counties of a particular state to fight against the COVID-19 pandemic. Therefore, the purpose of this research is to prioritize counties in a state based on their COVID-19-related risks to manage the COVID outbreak effectively. Design/methodology/approach In this research, the authors use a systematic hybrid approach that uses a clustering technique to group counties that share similar COVID conditions and use a multi-criteria decision-making approach – the analytic hierarchy process – to rank clusters with respect to the severity of the pandemic. The clustering was performed using two methods, k-means and fuzzy c-means, but only one of them was used at a time during the experiment. Findings The results of this study indicate that the proposed approach can effectively identify and rank the most vulnerable counties in a particular state. Hence, state health resources managing entities can identify counties in desperate need of more attention before they allocate their resources and better prepare those counties before another surge. Originality/value To the best of the authors’ knowledge, this study is the first to use both an unsupervised learning approach and the analytic hierarchy process to identify and rank state counties in accordance with the severity of COVID-19.

The Future of Energy Storage in Vietnam: A Fuzzy Multi-Criteria Decision-Making Approach to Metal-Ion Battery Assessments

Lithium-ion (Li-ion) batteries, despite their prevalence, face issues of resource scarcity and environmental concerns, prompting the search for alternative technologies. This study addresses the need to assess and identify viable metal-ion battery alternatives to Li-ion batteries, focusing on the rapidly industrializing context of Vietnam. It acknowledges the criticality of developing a sustainable, cost-effective, and resource-efficient energy storage solution that aligns with the country’s growth trajectory. The primary objective is to evaluate the suitability of emerging metal-ion batteries—specifically sodium-ion (SIB), sodium-ion saltwater (SIB-S), magnesium-ion (MIB), and zinc-ion (ZIB)—for Vietnam’s energy storage needs, guiding future investment and policy decisions. A Fuzzy Multiple-Criteria Decision-Making (MCDM) approach is employed, incorporating both quantitative and qualitative criteria. This study utilizes the Fuzzy Best-Worst Method (BWM) to determine the relative importance of various performance indicators and then applies the Bonferroni Fuzzy Combined Compromise Solution (Bonferroni FCoCoSo) method to rank the battery alternatives. The SIBs emerged as the most promising alternative, scoring the highest in the overall evaluation. The MIBs and SIB-saltwater batteries displayed competitive potential, while the ZIBs ranked the lowest among the considered options. This research provides a strategic framework for energy policy formulation and investment prioritization. It contributes to the field by applying a fuzzy-based MCDM approach in a novel context and offers a structured comparative analysis of metal-ion batteries, enhancing the body of knowledge on sustainable energy storage technologies.

An ADAM-based approach to unveiling entrepreneurial ecosystems in selected European countries

PurposeThis study aims to integrate the resource-based view (RBV) with other theories that consider external factors necessary to respond successfully to dynamic and uncertain entrepreneurial business conditions.Design/methodology/approachThe paper introduces an multi-criteria decision-making (MCDM) approach, utilizing the axial-distance-based aggregated measurement (ADAM) method with weights determined by the preference selection index (PSI) method, to rank eight European countries based on the Global Entrepreneurship Monitor (GEM) data. Additionally, the paper extends the existing entrepreneurial ecosystem taxonomy (EET), offering an additional classification.FindingsThe performed analysis emphasizes the importance and necessity of involving different dimensions of EE in assessing the countries' entrepreneurship performance, which facilitates creating adequate policy measures.Research limitations/implicationsThe crucial limitations are assessments based only on the GEM data from a particular period, possibly leading to a certain bias. Future research should involve data from various resources to increase the results' reliability.Originality/valueThe ranking results and country classification obtained using the ADAM-based approach and two distinct taxonomies served as the basis for formulating tailored policy recommendations, aiming to formulate tailored policy implications for increasing the number of new entrepreneurs and improving innovativeness, sustainability and internationalization of existing entrepreneurs for each group of countries.

Woven fabric selection and grading for ladies’ winter shawls using multicriteria decision-making approach

Purpose Quality assessment of textile products is of prime concern to intimately meet consumer demands. The dilemma faced by textile producers is to figure out the stability among quality criteria and efficiently deal with target specifications. Hence, the basic devotion is to attain the optimum value product which entirely satisfies the views and perceptions of consumers. Selection of best fabric among several alternatives in the presence of contradictory measures is a disputing problem in multicriteria decision-making. Design/methodology/approach In the current study, the analytic hierarchy process (AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE) are proficiently used to solve the problem in selection of branded woven shawls. AHP method verifies comparative weights of the criteria selection, while the ranking of fabric alternatives grounded on specific net-outranking flows is executed through PROMETHEE II method. Findings The collective AHP and PROMETHEE approaches are applied for the useful accomplishment of grading of branded shawls based on multicriteria weights, used for effective selection of fabric materials in the textile market. Practical implications In the apparel industry, fabric and garment manufacturers often rely on hit-and-trial methods, leading to significant wastage of valuable resources and time, in achieving the desirable fabric qualities. The implementation of the findings can assist apparel manufacturers in streamlining their fabric selection processes based on multiple criteria. By adopting this method, industry players can make informed decisions, ensuring a balance between quality standards and consumer expectations, thereby enhancing both product value and market competitiveness. Originality/value The methods of Visual PROMETHEE and AHP are assimilated to offer a complete method for the selection and grading of fabrics with reference to multiple selection criteria.

Meta-Analysis and Ranking of the Most Effective Methane Reduction Strategies for Australia’s Beef and Dairy Sector

Although Australia remains committed to the Paris Agreement and to reducing its greenhouse gas emissions, it was late in joining the 2021 Global Methane Pledge. Finding suitable methane (CH4) mitigation solutions for Australia’s livestock industry should be part of this journey. Based on a 2020–2023 systematic literature review and multicriteria decision approach, this study analyses the available strategies for the Australian beef and dairy sector under three scenarios: baseline, where all assessment criteria are equally weighted; climate emergency, with a significant emphasis on CH4 reduction for cattle in pasture and feedlot systems; and conservative, where priority is given to reducing costs. In total, 46 strategies from 27 academic publications were identified and classified as ‘Avoid’, ‘Shift’, or ‘Improve’ with respect to their impact on current CH4 emissions. The findings indicate that ‘Avoid’ strategies of conversion of agricultural land to wetlands, salt marshes, and tidal forest are most efficient in the climate emergency scenario, while the ‘Improve’ strategy of including CH4 production in the cattle breeding goals is the best for the conservative and baseline scenarios. A policy mix that encourages a wide range of strategies is required to ensure CH4 emission reductions and make Australia’s livestock industry more sustainable.

Assessment of thermal and mechanical properties of fly ash based geopolymer blocks with a sustainability perspective using multi-criteria decision-making approach

Owing to the raising consciousness about the environment, geopolymers are slowly gaining the trust of researchers globally as the potential alternatives for the conventional building materials. Though, geopolymer emit far less CO2 than conventional building materials, the distance between the source and production site for a particular raw material can have significant impact on the environment. Thus, apart from meeting the mechanical and physical requirement of a building material, it must satisfy the sustainability requirement as well. Therefore, a combination of experimental and analytical method is adopted to select the optimal design mix to produce sustainable geopolymer block which fulfills the performance requirements as well. While compressive and flexural strength test were performed on the geopolymer block made using fly ash (FA) as the primary precursor and glass powder (GP) and steel slag (SS) as the partial replacement to ascertain its mechanical performance, physical performance was assessed by conducting tests like water absorption, bulk density, and thermal conductivity. The sustainability of geopolymer block is assessed by computing sustainability index based on embodied energy, global warming potential and global temperature potential. Multi-criteria decision making (MCDM) approach is then used to select the most optimal mix for geopolymer block production based on the mechanical, physical and sustainability index of each mix for geopolymer. A combination of FA-SS prepared with 14 M NaOH was found to be most sustainable based on the MDCM approach.

Implementation of Wood-Framed Buildings in the Nordic Region: A MADAMOS (an Integrated Multi-Criteria Decision-Making Approach for Profitable Realization Alternatives) Method

This study optimizes the sustainable implementation of light wood-framed buildings in the Nordic region using multi-criteria decision-making. Integrating prefabrication, transportation logistics, and multi-criteria decision-making (MCDM) methods enhances cost efficiency, time savings, and quality assurance. Significant international and local impact promotes sustainable construction practices. Strong promotion fosters industry-wide adoption. The presented framework enables stakeholders to make informed decisions, enhancing the efficiency and effectiveness of building implementation processes and fostering sustainable development in the construction industry.

Enhancing Sustainable Mobility: Evaluating New Bicycle and Pedestrian Links to Car-Oriented Industrial Parks with ARAS-G MCDM Approach

The aim of this research is to address the challenge of transforming car-oriented industrial parks into pedestrian- and bicycle-friendly environments. Through the implementation of a multi-criteria decision-making (MCDM) approach, the study aims to evaluate alternative pathway connections and assess their potential impact on bicycle and pedestrian traffic volumes. By enhancing the connectivity of the cycling pathway network, the research seeks to demonstrate the potential for substantial increases in cycling and walking within industrial zones. This research leverages a multi-criteria decision-making framework, specifically the ARAS-G method, and integrates geographic information system analysis alongside Python scripting to project future bicycle usage and assess alternative pathway connections. The study underscores the potential for substantial increases in cycling and walking by augmenting the connectivity of the cycling pathway network. The findings hold practical significance for urban planners and industrial zone developers, advocating a holistic approach to sustainable transportation. The research contributes a comprehensive set of criteria encompassing connectivity, safety, accessibility, efficiency, integration within the urban fabric, and cost-effectiveness to evaluate sustainability and prioritize actions and measures for reestablishing industrial zones as bicycle-friendly spaces.

Cloud service ranking with an integration of k-means algorithm and decision-making trail and evaluation laboratory approach

The present research focuses on ranking cloud services by using the k-means algorithm with multi-criteria decision-making (MCDM) approaches that are the prime factor in the decision-making process and have been used to choose cloud services. The tools offered by MCDM can solve almost any decision-making problem. When faced with a selection challenge in the cloud environment, the trusted party would need to weigh the client’s choice against a predetermined list of criteria. There is a wide range of approaches to evaluating the quality of cloud services. The deep learning model has been considered a branch of artificial intelligence that assesses datasets to perform training and testing and makes decisions accordingly. This paper presents a concise overview of MCDM approaches and discusses some of the most commonly used MCDM methods. Also, a model based on deep learning with the k-means algorithm based decision-making trial and evaluation laboratory (kDE-MATEL) and analytic network process (ANP) is proposed as k-means algorithm based decision-making trial and evaluation laboratory with analytic network process (kD-ANP) for selecting cloud services. The proposed model uses the k-means algorithm and gives different levels of priority and weight to a set of criteria. A traditional model is also compared with a proposed model to reflect the efficiency of the proposed approach.

Extension of simple multi-attribute rating technique in uncertainty environment for 5G industry evaluation: Egyptian new administrative capital as a case study

As is well-known, multicriteria decision-making (MCDM) approaches can aid decision-makers in identifying the optimal alternative based on predetermined criteria. However, it is a big challenge to apply this approach in complex applications such as 5th generation (5G) industry assessment because criteria are challenging and trade-offs between them are hard. Also, assessment of the 5G industry involve strong uncertainty. So, this study is the first to evaluate the 5G industry using a new neutrosophic simple multi-attribute rating technique (N-SMART). Since neutrosophic set considers truth-degree, indeterminacy-degree, and falsity-degree, it is a more accurate instrument for evaluating uncertainty. The 5G assessment issue exemplifies the validity and great performance of our proposed method as: (1) its ability to deal with uncertainty phenomena; (2) its simplicity; and (3) its enhanced capacity to discern alternatives. Also, by considering the 5G service provided in the Egyptian New Administrative capital as a case study, the results showed that Ericsson 5G is the best choice and Nokia 5G is the worst choice.

Using multi-criteria decision-making approach for material alternatives in TiO2/P(VDF-TrFE)/PDMS based hybrid nanogenerator as a wearable device

P(VDF-TrFE)/TiO2/PDMS-based hybrid nanogenerators with excellent piezoelectric performance are of particular interest for the development of sensors, wearable devices, and low-cost biomechanical energy harvesters. In this work, hybrid nanogenerator (HNG) is designed to enhance the energy output by integrating piezoelectric and triboelectric effects. P(VDF-TrFE)/TiO2 nanocomposites are synthesized using electrospinning to design hybrid nanogenerator for energy harvesting from a manual activity. The hybrid nanogenerator device generated 5.36µA current across 10 MΩ resistor and 52V electric voltage. An analysis of the effect of varying TiO2 on hybrid nanogenerators is reported here for the first time. The best material for biomechanical energy harvesting applications is selected using a multi-criteria decision-making (MCDM) based material selection and optimization technique TODIM, along with a sensitivity study. SA-4 type nanocomposite is the most suitable material as per the TODIM approach. This nanogenerator shows better performance than most nanogenerators designed with complex and sophisticated approaches.

Evaluating the factors influencing the sustainable refrigerant selection by fuzzy decision making approach

Considering that cooling in cooling systems is more costly than heating, the importance of refrigerant selection in cooling systems is even more obvious. Due to the complexity of the refrigerant selection problem, a multi-criteria decision approach must be used to implement a thorough and organized evaluation of the factors. The purpose of this study is to evaluate the criteria to be considered when choosing refrigerants using the interval type-2 trapezoidal fuzzy Analytic Hierarchy Process (AHP). As a result, the most important and least crucial refrigerant selection criteria are determined by calculating the weights and obtaining the ranking of the requirements. In this way, the refrigerant selection criteria are prioritized, and the most crucial factor in refrigerant selection has emerged as energy efficiency. In light of the results, it has become clear that it is now essential for everyone in the world to use environ- mentally friendly, highly effective refrigerants.

A Taguchi-based hybrid multi-criteria decision-making approach for optimization of performance characteristics of diesel engine fuelled with blends of biodiesel-diesel and cerium oxide nano-additive

AbstractGiven the pressing demand and ongoing necessity for fossil fuels, there is an imperative to actively seek alternative resources to replace petroleum-based fuels. The presents study considers a problem of experimentally investigating the effect of varying levels of important input parameters of a diesel engine fuelled with a novel blend of biodiesel-diesel and cerium oxide nano-additive on the sustainable performance characteristics of a diesel engine. Four input parameters, i.e., blend percentage (B in %), nanoparticle concentration (NPC in ppm), engine load (LD in %) and ignition pressure (IP in bar) each at four levels are considered. Experiments are conducted as per the Taguchi’s L16 standard orthogonal array and for each experiment, performance parameters (such as Brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC)), emission measures (Carbon monoxide (CO), oxides of nitrogen (NOx), unburnt hydrocarbons (UBHC) and Vibration level (VL)) of the diesel engine are collected. A hybrid multi-criteria decision-making (MCDM) approach, i.e., integrated MEREC-MARCOS method along with signal-to-noise (S/N) ratio and analysis of mean (ANOM) is employed to determine optimal setting of the input parameters that yield optimal multiple performance characteristics. The results reveal that B at 40%, NPC at 80 ppm, LD at 50% and IP at 200 bar is the optimal setting of the input parameters that produce optimum values of the output responses considered simultaneously. Further, results of the analysis of variance (ANOVA) show that Nanoparticle concentration percentage contribution is the maximum (79.63%) followed by engine load (8.40%), ignition pressure (6.28%), and blend percentage (2.11%). The optimization results are: BTE is 32.87%, BSEC is 0.285, CO is 0.018%, NOx is 559.6 ppm, UBHC is 28.1 ppm and VL= 19.57m2/sec which were validated with a confirmation test. Henceforth, such hybrid fuels provide sustainable energy solutions and environmental conservation simultaneously addressing the current and future demands.

Mapping coastal groundwater potential zones using remote sensing based AHP model in Al Qunfudhah region along Red Sea, Saudi Arabia

Due to the increases in agriculture and industry sector as well as high population, lack of water is becoming a major problem in the Middle East especially in arid regions. Saudi Arabia needs more groundwater research and explorations because of its higher water use and no source of freshwater. Assessing groundwater zonation in semi-arid locations is essential due to the significant degree of variation in groundwater depth, aquifer features, topographical characteristics, and insufficient precipitation. Mapping prospective groundwater zones in Al Qunfudhah region of southwestern Saudi Arabia has utilized the capability of the multi-criteria decision approaches (MCDA), and the Geographic information system (GIS). We have used the analytical hierarchy process (AHP) as one of the MCDA that is applied to achieve the objective of the current study by integrating twelve controlling factors. These factors are represented by the thematic layers; slope, precipitation, soil type, land use/cover (LULC), drainage density (DD), normalized difference vegetation index (NDVI), curvature, topographic position index (TPI), Terrain Ruggedness Index (TRI), drainage density (DD), and Lineament Density (LD). These thematic layers are combined with GIS to delineate the zones of groundwater potentialities. All factors were classified and weighted according to their importance and its effect on groundwater zones. Their normalized weights were evaluated using a pairwise comparison matrix. The present study shows that the groundwater potential zones (GWPZs) map is represented by five groups ranging between a very high zone with an area of 23781.06 Km2 that represents 4.04 % of the studied area, and a very poor GWPZ with an area of 182944.4 Km2 that represents 31.09 % of the studied area. The AHP model suggests that lineament density, slope, and drainage density are more important for determining the groundwater potentiality than other physiographic factors. The study's findings will be helpful in developing practical strategies for the region's groundwater supply. This analysis shows how the methodology may be used to study a broad coastal groundwater basin. The current study will give the decision makers to select suitable sites with a high groundwater potential.

A risk-based multi-criteria decision-making framework for offshore green hydrogen system developments: Pathways for utilizing existing and new infrastructure

Unlocking the potential of offshore renewables for green hydrogen (GH2) production can be a game-changer, empowering economies with their visionary clean energy policies, amplifying energy security, and promoting economic growth. However, their novelty entails uncertainty and risk, necessitating a robust framework for facility deployment and infrastructure planning. To optimize offshore GH2 infrastructure placement, this work proposes a novel and robust GIS-based multi-criteria decision-making (MCDM) framework. Encompassing thirty-two techno-socio-economic-safety factors and ocean environmental impact analysis this methodology facilitates informed decision-making for sustainable and safe GH2 development. Utilizing the synergies between offshore wind and solar resources, this study investigates the potential of hybrid ocean technologies to enhance space utilization and optimize efficiency. To illustrate the practical application of the proposed framework, a case study examining a GH2 system in Australia's marine region and its potential nexus with nearby offshore industries has been conducted. The performed life cycle assessment (LCA) explored various configurations of GH2 production, storage, and transportation technologies. A Bayesian objective weight integrating technique has been introduced and contrasted statistically with the hybrid CRITIC, Entropy, MEREC and MARCOS-based MCDM approaches. Various locations are ranked based on the net present value of life cycle cost, GH2 production capacity, risk, availability, and environment sustainability factors, illustrating their compatibility. A sensitivity analysis is conducted to confirm that a Bayesian approach improves the decision-making outcomes through identifying optimal criteria weights and alternative ranks more effectively. Empowering strategic GH2 decisions globally, the proposed approach optimizes system performances, cost, sustainability, and safety, excelling in harsh environments.

Integrating Multi-Criteria Decision Models in Smart Urban Planning: A Case Study of Architectural and Urban Design Competitions

The focus of this study is to integrate the DEX (Decision EXpert) decision-modeling method in architectural and urban design (A & UD) competitions. This study aims to assess the effectiveness of integrating the DEX (Decision EXpert) decision-modeling method into the evaluation process of A & UD competitions to enhance decision-making transparency, objectivity, and efficiency. By using symbolic values in decision models, the approach offers a more user-friendly alternative to the conventional jury decision-making process. The practical application of the DEX method is demonstrated in the Rhinoceros 3D environment to show its effectiveness in evaluating A & UD competition project solutions related to the development of the smart city. The results indicate that the DEX method, with its hierarchical and symbolic values, significantly improves the simplicity of the evaluation process in A & UD competitions, aligning it with the objectives of the smart cities. This method provides an efficient, accessible, and viable alternative to other multi-criteria decision-making approaches. This study importantly contributes to the field of architectural decision making by merging qualitative multi-criteria decision models into the CAD environment, thus supporting more informed, objective, and transparent decision-making processes in the planning and development of smart cities.

Double-layer multi-criteria group decision-making approach using neutralized possibility degree-based decision matrix with fuzzy information

Multi-criteria group decision-making (MCGDM) problems are fundamentally characterized by uncertainty and ambiguity. The quantification of such data is vital for determining the most optimal course of action. In this paper, a double-layer possibility degree-based interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs) Vlsekriterijumska Optimizacija i Kompromisno Resenje (VIKOR) approach is presented. First, the possibility degrees of IVq-ROFSs are calculated and processed to establish a new neutralized decision matrix. Second, the components of the new decision matrix are taken into account for obtaining a new pair of positive and negative ideal solutions. The utility and regret measures in the VIKOR approach are ultimately determined by combining criteria weights and cumulative possibility degrees to rank the alternatives. The results of the study show that the proposed approach contributes to acquiring accurate alternative rankings in situations where traditional approaches have had difficulty generating ranks. Also, by eliminating the early normalization stage, which has a direct impact on the final ranking, the proposed approach concludes a unique ranking for each decision-making problem. Several comparative and sensitivity analysis are also conducted to exhibit robustness and applicability of the proposed approach in real world.

Extracting of prospective groundwater potential zones using remote sensing data, GIS, and multi-criteria decision-making approach in the Sub-Himalayan Dooars region of West Bengal, India

Groundwater is one of the fundamental sources of fresh water. Currently, the unequal distribution of groundwater poses a significant environmental crisis, leading to scarcity in certain regions and affecting the livelihood of living organisms worldwide. The present study introduces the Sub-Himalayan foothills Dooars, a highly populated and agriculturally based area. To delineate the groundwater potentiality eight different effective parameters have been employed such as slope, geomorphology, soil, LULC, lineament, rainfall, hydrogeology, and drainage density. For the process and development of these eight thematic layers, geographic information systems and satellite images (Landsat 8) have been used to give the normalized weight and rank value in each thematic layer and their sub-criteria. MCDA-AHP-based model is used for overlay analysis to delineate groundwater potentiality (GWPZ). After overlay analysis in ArcGIS 10.2.2 final groundwater potentiality map is further grouped into four potentiality zones, i.e., very high, high, moderate, and low potential zones. A very high potential zone encompasses 22.45 sq. km. in the southern portion of the selected study region. The high potential zone encompasses 1613.32 sq. km in the southeastern part, the moderate zone is covered by 1063.84 sq.km, and the 71.03 sq.km (2.56% area of the region) area is covered by the low potential zone. The available well data from CGWB have been utilized to assess the potential of groundwater in the district, ensuring originality and authentic validation. It is found that there is a correlation between the observed potentiality zone and existing well data. With the growing population in the Sub-Himalayan Dooars region, there has been a corresponding rise in the demand for water. Hence, this research would assist the authorities and policymakers in effectively managing the groundwater resources in the district.

A Multi-Criteria Decision-Making Framework for Zero Emission Vehicle Fleet Renewal Considering Lifecycle and Scenario Uncertainty

In the last decade, with the increased concerns about the global environment, attempts have been made to promote the replacement of fossil fuels with sustainable sources. For transport, which accounts for around a quarter of total greenhouse gas emissions, meeting climate neutrality goals will require replacing existing fleets with electric or hydrogen-propelled vehicles. However, the lack of adequate decision support approach makes the introduction of new propulsion technologies in the transportation sector a complex strategic decision problem where distorted non-optimal decisions may easily result in long-term negative effects on the performance of logistic operators. This research addresses the problem of transport fleet renewal by proposing a multi-criteria decision-making approach and takes into account the multiple propulsion technologies currently available and the objectives of the EU Green Deal, as well as the inherent scenario uncertainty. The proposed approach, based on the TOPSIS model, involves a novel decision framework referred to as a generalized life cycle evaluation of the environmental and cost objectives, which is necessary when comparing green and traditional propulsion systems in a long-term perspective to avoid distorted decisions. Since the objective of the study is to provide a practical methodology to support strategic decisions, the framework proposed has been validated against a practical case referred to the strategic fleet renewal decision process. The results obtained demonstrate how the decision maker’s perception of the technological evolution of the propulsion technologies influences the decision process, thus leading to different optimal choices.

Spherical fuzzy sets based integrated DEMATEL, ANP, VIKOR approach and its application for renewable energy selection in Turkey

Renewable energy continues occupying the global agenda for the low-carbon transition. Identifying suitable sources for renewable energy involves different criteria and alternatives, which can be defined as a Multi-Criteria Decision-Making (MCDM) problem. MCDM methods can be enriched with Group Decision Making (GDM) to merge individual judgments into group opinions, as well as with fuzzy sets to better simulate human opinions. To handle uncertainties of DMs’ preferences, a recently developed fuzzy set extension, the Spherical Fuzzy Sets (SFSs), is combined in this study with different MCDM tools to establish a decision support tool for the energy source selection problem. This article proposes a novel MCDM approach that integrates SF-DEMATEL (Spherical Fuzzy Decision-Making Trial and Evaluation Laboratory), SF-ANP (Spherical Fuzzy Analytic Network Process), and SF-VIKOR (Spherical Fuzzy Vlse Kriterijumska Optimizacija Kompromisno Resenje) algorithms in a GDM environment and presents its application in a case study for selecting renewable energy sources in Turkey. Comparing wind, geothermal, solar, hydropower, and biogas, the proposed method highlighted both wind and solar energy as a compromise solution as the most suitable options for Turkey. It has been found that compared to similar fuzzy sets, SFS can better reflect the hesitation degree to address the lack of knowledge and errors in the membership function definitions. The robustness and plausibility of these findings are demonstrated with sensitivity and comparative analyses, suggesting this MCDM method can also be applied to similar problems in other settings.