Decision-making Techniques Research Articles

Academic Success Among Master of Social Work Students with Clinical Depression: An Interpretive Phenomenological Analysis

ABSTRACT Purpose Little is known about depression’s impact on Master of Social Work (MSW) students’ academic success and career trajectory. This study explored the experiences of students living with clinical depression and enrolled in a MSW program at a singular large Midwestern University. Materials and Methods Participants were recruited using convenience and purposive sample techniques. Data were transcribed and analyzed using an interpretive phenomenological analysis (IPA) framework, and the iterative processes associated with qualitative data analysis. Results Participants addressed their depression by (a) recognizing, identifying, and addressing their childhood triggers, (b) finding healthy coping techniques to address their depression while also addressing previous negative behaviors, (c) incorporating intentional decision-making techniques to help them once they were enrolled in the MSW program, and (d) incorporating behavior change techniques learned in their classes to help them deal with their depression. Discussion Students noted that classes presented opportunities to help them balance and address their triggers while also providing opportunities to holistically engage with academic demands. While there is no singular blueprint to ensure academic success, having clearly defined expectations within the academic setting (e.g. program expectations, studying for exams, course expectations, and personal responsibilities), helped to reduce stress, anxiety, and other unhealthy skills that may be correlated with their depressive symptoms and episodes. Conclusion Recommendations for faculty and students were made.

GIS-based multi-criteria decision making for identifying rainwater harvesting sites

The Middle East region, with its arid and semi-arid climate, is one of the regions most affected by climate change and water scarcity. To address the severe issue of water scarcity in the western region of Iraq, this study identifies optimal potential rainwater harvesting (RWH) locations. Geographic Information System (GIS) and multi-criteria decision-making (MCDM) techniques were employed to generate themed layers for RWH. The nine primary criteria considered were rainfall, elevation, slope, stream order, soil texture, land use, groundwater depth, distance from the lake, and runoff depth. A weighted overlay assessment was used to identify probable RWH locations. The analytical hierarchical process was used to weight criteria depending on the study region, hydrological and socioeconomic parameters, and literature. The consistency ratio (CR = 3.16%) was calculated to validate the optimum weights of the comparison components, from which it was found that the weights assigned to each criterion were appropriate for comparative purposes. The results indicated that the optimum location (very high suitability) for RWH is mostly in middle regions of the study area, covering 286 km2 (13%), while for the other categories, high suitability is at 23% (498 km2), medium suitability at 29% (636 km2), low suitability at 21% (462 km2), and very low suitability at 14% (305 km2). Sensitivity analysis was used to identify the relative importance of the parameters and determine how each of the nine criteria influences the optimal RWH sites. These findings can assist decision makers and planners in devising strategies to mitigate the effects of climate change and increase any reclaimed area for agriculture.

DecideXpert: Collaborative system using AHP-TOPSIS and fuzzy techniques for multicriteria group decision-making

decideXpert: Collaborative system using AHP-TOPSIS and fuzzy techniques for multicriteria group decision-making

Towards Sustainable Healthcare: A Fuzzy Multi-Criteria Framework for Green Hospital Investments

It is very necessary to develop optimal investment strategies for green hospitals. Optimal investment strategies are necessary for these hospitals to achieve maximum efficiency. Moreover, the right investment strategies ensure that green hospitals use resources such as energy and water efficiently. This contributes to the reduction of operational costs in the long term. For these reasons, the development of optimal investment strategies is of vital importance for the successful construction of green hospitals. On the other hand, a comprehensive need is needed to determine the most accurate investment strategies. This study also aims to develop the most appropriate investment strategies for green hospitals. To achieve this goal, a new model is established in which fuzzy logic and multi-criteria decision-making techniques are integrated. Effective waste management is the most important factor to be considered in this process. In addition, reducing the carbon footprint is another important issue in this context.

Barriers and Opportunities in Implementing Carbon Neutrality Goals in China’s Heavy Industries

The transition to carbon neutrality in China’s heavy industries is essential for mitigating global climate change, given the sector’s significant contribution to greenhouse gas emissions. This study systematically examines the multifaceted barriers and opportunities influencing decarbonization efforts in these industries. Employing expert-driven methodologies, including the Delphi method and the best-worst method (BWM), the research identifies and prioritizes critical challenges, such as the high upfront costs for renewable technologies, the technological dependency on coal, and the fragmented regulatory frameworks. It highlights transformative opportunities, emphasizing advancements in carbon capture, utilization, and storage (CCUS), renewable energy integration, and strengthened carbon pricing mechanisms. The findings reveal the interaction between economic, technological, and policy dimensions, underscoring the need for coordinated interventions to overcome entrenched barriers. The study contributes to theoretical advancements by integrating expert insights with robust multi-criteria decision-making techniques and offers actionable pathways for policymakers and industry stakeholders to accelerate industrial decarbonization. These insights align with international climate objectives, providing a scalable framework for global sustainable transitions in energy-intensive sectors.

Comparative Analysis of Different Drying Methods on Strawberry Aroma Compounds via Multi-Criteria Decision-Making Techniques

Food and food safety, as one of the basic issues of human life, has made it necessary to store foods for a long time with the increasing population. One of the oldest and most common methods of extending the shelf life of food products is the drying process. The drying process contributes to the higher quality of foods in terms of physical, chemical, and microbial properties by ensuring that beneficial contents such as vitamins, minerals, and aroma compounds are better preserved. The aroma values of foods, which consist of taste and smell components, gain importance. In foods, the taste is determined by permanent components, while smell is determined by volatile components. The loss of volatile aroma compounds in the strawberry drying process negatively affects product quality. Small changes in aroma compounds can lead to significant differences in product taste. Therefore, strawberry aroma is a critical factor for consumer appeal and commercial success. In this study, the effects of drying methods on the aroma compounds of strawberry fruit were compared with Multi Criteria Decision Making (MCDM) techniques. In this study, PSI-based MCDM techniques were used to make the most appropriate choice among strawberry drying methods. The values of 23 distinct aroma compounds obtained with different drying methods applied to strawberry fruit were analyzed with 7 different MCDM techniques. The calculations gave similar results and these results were combined with the Borda rule. Accordingly, the drying methods with the highest scores were determined as freeze drying.

Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategy

The performance of nanofluids is largely determined by their thermophysical properties. Optimizing these properties can significantly enhance nanofluid performance. This study introduces a hybrid strategy based on computational intelligence to determine the optimal conditions for ternary hybrid nanofluids. The goal is to minimize dynamic viscosity and maximize thermal conductivity by varying the volume fraction, temperature, and nanomaterial mixing ratio. The proposed strategy integrates machine learning, multi-objective optimization, and multi-criteria decision-making. Three machine learning techniques—GMDH-type neural network, gene expression programming, and combinatorial algorithm—are applied to model dynamic viscosity and thermal conductivity as functions of the input variables. Then, the high-performing models provide the foundation for optimization using the well-established multi-objective particle swarm optimization algorithm. Finally, the decision-making technique TOPSIS is employed to identify the most desirable points from the Pareto front, based on various design scenarios. To validate the proposed strategy, a ternary hybrid nanofluid composed of graphene oxide (GO), iron oxide (Fe₃O₄), and titanium dioxide (TiO₂) was employed as a case study. The results demonstrated that the combinatorial approach excelled in accurately modeling (R = 0.99964–0.99993). The optimization process revealed that optimal VFs span a broad range across all mixing ratios, while optimal temperatures were consistently near the maximum value (65 °C). The decision-making outcomes indicated that the mixing ratio was consistent across all design scenarios, with the volume fraction serving as the key differentiating factor.

Integrating intuitionistic fuzzy and MCDM methods for sustainable energy management in smart factories.

Improving energy efficiency is crucial for smart factories that want to meet sustainability goals and operational excellence. This study introduces a novel decision-making framework to optimize energy efficiency in smart manufacturing environments, integrating Intuitionistic Fuzzy Sets (IFS) with Multi-Criteria Decision-Making (MCDM) techniques. The proposed approach addresses key challenges, including reducing carbon footprints, managing operating costs, and adhering to stringent environmental standards. Eight essential criteria are identified, such as the use of renewable energy, the efficiency of production, and the health and safety of workers, to evaluate energy performance. Using the entropy method for criterion weighting and the CRADIS technique for alternative ranking, we prioritize a range of energy-efficient solutions. The novelty of our approach lies in its comprehensive assessment of complex real-world energy management scenarios within smart factories, offering a robust and adaptable decision-support tool. Our empirical results, validated through sensitivity analysis, show that alternative 5 delivers the most significant improvement in energy efficiency. This study provides valuable information for industry practitioners seeking to transition to more sustainable production methods and supports the broader sustainability agenda.

Sustainable solar energy deployment: a multi-criteria decision-making approach for site suitability and greenhouse gas emission reduction.

Conventional power generation methods have led to adverse environmental impacts. Thus, the need for a strategic transition to alternative energy sources arises. This study presents a comprehensive approach to sustainable solar energy deployment using multi-criteria decision-making (MCDM) techniques. The research aims to identify suitable sites for utility-scale solar photovoltaic (PV) installations, estimate potential energy output, and assess solar PV deployment's environmental and economic impacts. The methodology integrates analytic hierarchy process (AHP), fuzzy logic, and geographic information systems (GIS) to evaluate land suitability across four scenarios. The analysis considers technical, economic, environmental, and social factors, including solar radiation, proximity to infrastructure, land use, topography, and stakeholder opinions. Results reveal that Scenario 1 analytic hierarchy process multi-criteria-decision-making (AHP-MCDM) identified 2824.1 km2 of suitable land, while the combined approach in Scenario 3 yielded 666.9 km2. The study estimates potential annual energy generation ranging from 19.69 to 109.15 GWh/km2/year, depending on the scenario and solar PV technology used. Environmental impact assessments indicate potential annual CO2 emission reductions of up to 51,365.84 tons, with associated cost savings of US $3.28 million. The research provides valuable insights for policymakers and investors, highlighting 16 optimal sites for utility-scale solar farm development across Jamaica, with the most promising in the Westmoreland, Manchester, and St. Mary parishes. These findings contribute to Jamaica's renewable energy goals and offer a replicable, sustainable solar energy planning framework in similar geographical contexts.

Synergy of machine learning and the Einstein Choquet integral with LOPCOW and fuzzy measures for sustainable solid waste management

<p>Solid waste management (SWM) protects public health, the environment, and limited resources in densely populated and urbanized countries such as Singapore. This work presents an advanced framework for optimizing SWM using advanced mathematical models and decision-making techniques, including the circular $ q $-rung orthopair fuzzy set (C$ q $-ROFS) for data, combined with the Choquet integral (CI) and logarithmic percentage change-driven objective weighting (LOPCOW) methods, enhanced by the aggregation operators (AOs) circular $ q $-rung orthopair fuzzy Einstein Choquet integral weighted averaging (C$ q $-ROFECIWA) and circular $ q $-rung orthopair fuzzy Einstein Choquet integral weighted geometric (C$ q $-ROFECIWG) aggregation operators. By conducting a systematic evaluation, these methods classified different alternatives to SWM, evaluating them according to criteria such as their environmental impact, cost-effectiveness, waste reduction efficiency, feasibility of implementation, health safety, and public acceptance. The operators C$ q $-ROFECIWA and C$ q $-ROFECIWG perform better than previous approaches in the effective management of multifaceted and dynamic SWM scenarios. The comparison study demonstrates that the integration of these operators with LOPCOW and the Choquet integral offers decision-making conclusions that are more reliable and sustainable. The study conducted in Singapore successfully finds the most feasible SWM alternatives and emphasizes the possibility of implementing more environmentally sustainable practices in the urban environment. This research offers practical insights for policymakers and emphasizes the need to improve and enhance these approaches to improve SWM in various urban environments.</p>

Integrating Neutrosophic Theory for Improved Decision-Making in Wireless Body Area Networks: Enhancing Accuracy and Efficiency in Health Monitoring

Wireless Body Area Networks (WBANs) play a pivotal role in modern healthcare by enabling continuous monitoring of physiological data through sensors placed on or around the human body. Despite their significant benefits, WBANs face challenges such as data uncertainty, complex decision-making processes, and dynamic network conditions. These challenges can lead to inaccuracies and inefficiencies in health monitoring and diagnostics. The paper's main aim is to incorporate neutrosophic theory into Wireless Body Area Networks to provide enhancements in decision-making. In modern healthcare, the use of WBANs for monitoring physiological data by sensors, which are attached to or around the human body, can be continuous. Despite huge advantages, the main challenges that WBANs face are the uncertainties in data, complex decision-making processes, and dynamic network conditions, making health monitoring and diagnostics inaccurate and inefficient. In this paper, authors propose a robust framework to map sensor data into the neutrosophic domain and apply neutrosophic logic for enhanced accuracy and reliability of decision-making. In this paper, a Neutrosophic Decision-Making Algorithm is proposed, and its performance is compared with other decision-making techniques in terms of accuracy, response time, energy efficiency, and reliability. Experimental results show major improvements of around 95.3% in accuracy and a reduction of up to 25% in response time and energy consumption. Results underline the potential of neutrosophic theory for revolutionizing decision-making processes within WBANs to ensure more reliable and efficient health monitoring. This approach enables not only operational life but also improves patient outcome, avoiding a wrong diagnosis, during long-term health monitoring applications using WBAN devices.

Decision-making technique with neutrosophic [formula omitted]-rough set approach for sustainable industry evaluation using sine trigonometric operators

To overcome the shortcomings of existing fuzzy decision-making models, the approximation of the neutrosophic Z-number becomes an invaluable tool in complicated circumstances including incomplete and contradictory data. To overcome the challenges of methods like multi-criteria decision-making (MCDM), trapezoidal neutrosophic numbers (TrNNs), and neutrosophic Z-numbers (NZNs), this approach integrates rough set theory, neutrosophic set theory, and Z-numbers to introduce Neutrosophic Z-Rough data. Issues like measurement reliability in TrNNs and continuity difficulties in NZNs can be addressed with the use of sophisticated aggregation techniques and Neutrosophic Z-Rough numbers (NZRNs). Using the periodicity and symmetry of the sine-trigonometric function (STF), this work investigates sine-trigonometric approaches to Neutrosophic Z-Rough numbers. New Rough Neutrosophic Z-number sine-trigonometric aggregation operators are obtained by merging the special features of the STF with the NZRNs. The method’s practical applicability within the NZRN framework is demonstrated by applying these operational notions to the MCDM approach, which supports sustainable decision-making in commercial contexts. In addition to improving the NZRN domain’s decision-making efficiency and rationality, this study guarantees a consistent and trustworthy assessment method. New approaches based on NZRNs are necessary since conventional MCDM frameworks are unable to handle the uncertainties brought forth by Industry 4.0. Results from a battery of tests—including sensitivity analyses, reliability evaluations, and comparison studies show that the suggested method is fresh, trustworthy, and excellent for gauging corporate sustainability.

Constructing a knee osteoarthritis transplant via the neutrality aggregation operators under spherical fuzzy Z-numbers framework.

Understanding the biomechanics of osteoarthritis is necessary for designing a biomedical knee implant to reduce pain, increase mobility, and enhance the patient's quality of life. The most appropriate implant design may be chosen by using Multi-Attribute Group Decision-Making (MAGDM) techniques, which include a number of variables including material characteristics, biomechanical performance, cost-effectiveness, and patient-specific requirements. Compared to conventional fuzzy set structures, Spherical Fuzzy -Number Sets ( S) provide an enhanced method for resolving uncertainty in MAGDM and are more suited for handling complicated decision-making situations. In order to get crucial weight information, this work uses spherical fuzzy entropy measures ( ) to suggest a unique distance metric specifically designed for spherical fuzzy -numbers. Furthermore, to solve issues in MAGDM, especially when the preferences and criterion weights of decision-makers are uncertain, we offer Aggregation Operators based on Neutrality Aggregation ( ) T-norm and T-Conorm. We first investigate the notion, their accuracy and scoring functions, and the fundamental ideas that underlie their work. Next, we create a method for generating decisions for MAGDM situations by using information. Our suggested operators provide a flexible and resilient structure for collecting information by integrating operations into the framework. This makes them particularly useful for developing knee implants for osteoarthritis. This work advances the field of MAGDM for biomedical engineering both theoretically and practically. We provide comparison findings to confirm our technique and show its efficacy and field usefulness.

Analysis of Landfill Site Suitability for Solid Waste Disposal in the Ranchi District of Jharkhand, India Using Analytical Hierarchy Process and Geo-Spatial Techniques

ABSTRACT The selection of an appropriate landfill site is a complex issue due to the involvement of numerous criteria and constraints. A non-engineered and unsuitable site can harm the environment, society and economy of an area. This paper focuses on the application of Geographic Information System (GIS) and Analytical Hierarchy Process (AHP), a multi-criteria decisionmaking technique, to determine suitable landfill sites. A total of nine parameters have been used namely; groundwater depth, slope, geology, existing landfill site, drainage density, road network, land use and land cover (LULC), railway lines and airport. The weights were calculated using AHP. A landfill site suitability map was generated with 5 classified areas namely; most unsuitable (0.07%), unsuitable (5.42%), less suitable (23.94%), moderately suitable (58.24%) and most suitable (12.38%). The areas identified in the study can be used by policymakers in both urban as well as rural developmental planning and waste management. Using the patches of land from the landfill suitability map, a decentralised community-based landfill model can also be adopted to minimize and possibly eliminate the need for large landfill sites.

Individual difference factors that predict evaluations and acceptance of authorized concealment for the reduction of nocebo side effects.

Authorized concealment is a shared decision-making technique in which a clinician asks a patient if they would prefer not to be informed about all possible treatment side effects, so as to minimize nocebo effects. The present research assessed individual difference variables that predict evaluations and receptivity of authorized concealment. A demographically diverse national community US sample of 1012 adults (ages ranging from 18 to 94; M=43.2) learned about nocebo effects and then evaluated and estimated their likelihood of consenting to four possible methods of authorized concealment. A range of self-reported psychological, health, and demographic variables were tested as predictor variables. The strongest and most reliable outcomes were that greater trust in doctors and a greater desire to avoid side effect knowledge predicted positive evaluations of authorized concealment and estimated personal use of authorized concealment. There was some evidence that women, those of older age, reporting better health, and using medicine more regularly, had a more positive response to authorized concealment, but these effects were dependent upon the specific method of authorized concealment described. Individual difference variables can predict openness to authorized concealment. This knowledge may benefit clinicians who consider employing this shared decision-making technique with patients.

An Analysis on Sustainable Automotive Car Body Material Using Weighted Sum method

Introduction: In order to reduce its influence on the environment, the automobile industry must overcome a significant obstacle and switch to sustainability car body materials. This overview examines the demand for cutting-edge materials that enhance vehicle performance, increase recycling, and minimize carbon emissions. The sector can meet expanding transportation demands while promoting a greener future by using sustainable options. The automotive industry plays a major role in the global carbon footprint, with car body products being a major contributor. As sustainability becomes a major concern, the need for eco-friendly alternatives to traditional materials is clear. This introduction explores the concept of sustainable automotive car body materials, highlighting their importance in reducing emissions, conserving resources and promoting a greener future. By exploring innovative solutions, the industry can embrace sustainability while maintaining high-performance standards. Research significance: Research on sustainable automotive car body materials is significant in addressing the pressing challenges facing the automotive industry. By focusing on the development and use of environmentally friendly alternatives, this research aims to reduce the environmental impact of the industry. By reducing carbon emissions and promoting resource conservation, sustainable car body materials can contribute to a greener and more sustainable future. Furthermore, this research has the potential to improve energy efficiency and vehicle performance, leading to safer and more efficient vehicles. Additionally, by meeting growing consumer demand for sustainable options, this research will help automakers gain a competitive edge in the market and align their products with the values and expectations of environmentally conscious consumers. Method: The weighted sum method is a decision-making technique that assigns weights to different criteria and calculates an overall performance score of the alternatives. By assigning relative importance to each criterion, this method allows decision makers to objectively evaluate options and make informed choices. The weighted sum method combines the weighted scores of each criterion to obtain a comprehensive performance measure that enables a systematic approach to decision making. The method provides a structured framework for analyzing complex decision-making problems, finding applications in fields as diverse as project selection, supplier evaluation, and product design. Alternate parameters: Steel Alloy (AHSS), Aluminium Alloy (A7075 T6), Carbon Fiber/ epoxy laminate (CL), Titanium Alloy (Ti6Al4 V). Evaluation parameters: Tensile Strength (Mpa), Stiffness (Gpa), Damping capacity, Cost (Rs), CO2 Emission (Ton/Ton), Results: The Steel Alloy (AHSS) is in 1st rank. The Aluminium Alloy (A7075 T6) is in 4th rank. Carbon Fiber/ epoxy laminate (CL) is in 2nd rank. Titanium Alloy (Ti6Al4V) is in 3rd rank. The result is done by using the WSM method. Conclusion: sustainable automotive car body material in the Steel Alloy (AHSS) is in 1st rank. The Aluminium Alloy (A7075 T6) is in 4th rank. Carbon Fiber/ epoxy laminate (CL) is in 2nd rank. Titanium Alloy (Ti6Al4V) is in 3rd rank. The result is done by using the WSM method.

Hybrid MCDM-FMEA Model for Process Optimization: A Case Study in Furniture Manufacturing

The furniture-manufacturing industry is pressured to improve quality, productivity, and profitability, particularly within increasingly volatile market conditions. This study is focused on the development of methods for optimizing production processes in a furniture-manufacturing company through the application of an integrated risk management framework. By integrating Failure Mode and Effects Analysis (FMEA) with advanced multi-criteria decision-making (MCDM) techniques, specifically fuzzy AHP, fuzzy TOPSIS, and fuzzy WINGS, a hybrid model is developed to identify, prioritize, and address critical failure points while accounting for complex interdependencies. Significant failure modes, such as order inaccuracies and delivery delays, are revealed as key findings and are found to notably affect productivity and customer satisfaction. The proposed model’s ability to capture cascading effects and a nuanced prioritization enables a more precise risk assessment, thereby supporting resilience and process efficiency in the furniture-manufacturing sector. This approach is shown to not only optimize production but also provide a foundation for applying such hybrid models in other industries to manage sector-specific interdependencies effectively.

Analytical Techniques for Supporting Hospital Case Mix Planning Encompassing Forced Adjustments, Comparisons, and Scoring.

Background/Objectives: This article presents analytical techniques and a decision support tool to aid in hospital capacity assessment and case mix planning (CMP). To date, no similar techniques have been provided in the literature. Methods: Initially, an optimization model is proposed to analyze the impact of making a specific change to an existing case mix, identifying how patient types should be adjusted proportionately to varying levels of hospital resource availability. Subsequently, multi-objective decision-making techniques are introduced to compare and critique competing case mix solutions. Results: The proposed techniques are embedded seamlessly within an Excel Visual Basic for Applications (VBA) personal decision support tool (PDST), for performing informative quantitative assessments of hospital capacity. The PDST reports informative metrics of difference and reports the impact of case mix modifications on the other types of patients present. Conclusions: The techniques developed in this article provide a bridge between theory and practice that is currently missing and provides further situational awareness around hospital capacity.

Assessing the Circular Economy in Regions of Chile by Using Multiple-Criteria Decision-Making (MCDM)

The circular economy has gained significant global attention, particularly in developed countries, as a novel economic model to reduce waste and optimize resource utilization. This study assesses the capacity of Chilean regions to adopt the circular economy model. Six Chilean regions were selected for this purpose. The research process is carried out and employs a descriptive–analytical approach, conducting a comparative analysis of the six regions based on nine waste-related indicators. The Rank Alternative by Fractional Separation of Ideal Solutions (RAFSI) method, a multi-criteria decision-making (MCDM) technique, was used with trapezoidal fuzzy numbers for data analysis. A sensitivity analysis was employed to ensure the robustness of the results and determine the range of variation and flexibility of the model. The findings indicate that the Antofagasta region ranked first based on the research indicators, followed by Coquimbo, Arica y Parinacota, Valparaíso, Atacama, and Tarapacá. These results highlight significant disparities in the performance of Chile’s northern regions, emphasizing the key factors influencing circular economy adoption. This underscores the crucial need for region-specific strategies to enhance sustainability and waste management, as one size does not fit all in this context.

Utilizing Multi-Criteria Decision-Making Techniques in the Selection of Electric Vehicles: An Analysis for the Turkish Electric Vehicle Market

Global warming and climate change are among the biggest problems of our time. The rapid depletion of fossil fuels and the harmful effects of internal combustion engines on the environment are increasing the interest in electric vehicles. These vehicles emit less carbon emissions than gasoline and diesel vehicles, reducing the emission of harmful gases such as greenhouse gases into the atmosphere. The use of electric vehicles provides significant benefits for human health and environmental health. The sale of electric vehicles in Turkey is very important in terms of sustainability and economy. This study aims to help rank the alternatives by using multi-criteria decision-making (MCDM) methods in the selection of the 11 most preferred electric vehicles in Turkey. Various criteria such as DC fast charging time, power (kW), range, price, battery capacity, electricity consumption (kWh) and number of services were considered in the study. Following the criteria provided by the MEREC weighting method, four different decision-making methods such as TOPSIS, PROMETHEE, MOORA and WASPAS were applied and the outcomes were combined with the COPELAND approach to obtain a final ranking. The findings show that the X5 vehicle received the highest score according to the COPELAND method and that this vehicle ranked high in other methods as well, and its overall performance was remarkable. The X11 and X4 vehicles stand out as the second and third best alternatives, respectively. As a result of the study, it was concluded that the most important criterion among the 7 criteria is “charging time” and the least important criterion is “electricity consumption”.The results obtained from the COPELAND method are presented in a clear and understandable way. With this feature, decision makers can easily understand the comparisons between alternatives. These results help consumers considering purchasing an electric vehicle to determine which vehicles are more suitable, while also providing useful information for professionals in the industry to make strategic decisions.