Multi-criteria Decision-making Method Research Articles

Decision Making in Bank Personnel Selection Using the Analytical Hierarchy Process

Banks, like in many other areas, must make the right decisions in personnel selection. Incorrect personnel choices can lead to customer loss and increase internal organizational problems. Therefore, it is crucial for banks to make the right personnel selections and use objective criteria when making these decisions. Selecting the most suitable employee who can contribute to increasing the value of the organization from numerous applications requires a multi-stage process. Recent studies have observed that multi-criteria decision-making methods are frequently used in evaluating recruitment processes. In this regard, the Analytical Hierarchy Process (AHP) method is a tool that can assist banks in making accurate personnel decisions. The aim of the study is to identify the criteria used in personnel selection in banks and prioritize these criteria. In the research, key factors in the recruitment process include personal characteristics, organizational expectations, and experience as the main criteria. Using the AHP method, priority rankings of these factors have been made, and the most important criteria have been identified. The results highlight that determining the right criteria is crucial for enhancing the quality of the workforce and designing efficient recruitment processes in the banking sector. Furthermore, the AHP model provides a concrete tool for recruitment processes in the sector, helping decision-makers to make more informed and objective decisions.

Enhancing energy resilience in enterprises: a multi-criteria approach

Unreliable energy supply disrupts productivity and escalates costs in manufacturing enterprises, highlighting the need for robust energy resilience strategies. Existing multi-criteria decision-making (MCDM) methods, such as the analytic hierarchy process (AHP), best–worst method (BWM), and unaugmented TOPSIS, often lack advanced risk prioritization and objective criteria weighting, limiting their effectiveness in addressing complex, multidimensional energy challenges. This study proposes an integrated framework that combines Failure Mode and Effects Analysis (FMEA) for risk prioritization, the CRITIC-method for objective criteria weighting based on the 4A’s energy resilience dimensions—availability, accessibility, affordability, and acceptability—and fuzzy-TOPSIS for strategy ranking. The framework bridges theoretical rigor with operational applicability, addressing the unique challenges faced by manufacturing enterprises. Validated through a real-world case study and sensitivity analysis, it identifies actionable strategies, such as flexible scheduling, demonstrating consistent rankings across comparative analyses with fuzzy VIKOR, WASPAS, and MARCOS. This sector-specific approach advances both academic understanding and industrial practice by providing a scalable, multidimensional tool for enhancing energy resilience.

Urban soil ecological risk assessment based on "climate change- resilience".

Under the dual pressures of global climate change and rapid urbanization, urban soil ecosystems face increasingly complex risks and challenges. Existing urban soil ecological risk assessment methods are primarily limited to single-dimensional analysis or linear assessment models, making it difficult to reveal the complex interaction mechanisms among soil, climate, and human activities. This study pioneers the integration of social-ecological system theory with multi-criteria decision-making methods to innovatively construct a dynamic coupling assessment framework. This framework employs the AHP-DEMATEL method to identify causal relationships and feedback mechanisms between indicators, while utilizing the VIKOR method for comprehensive quantitative risk assessment. Through an empirical analysis of 11 prefecture-level cities in Zhejiang Province, this study reveals that: (1) climate change-related indicators demonstrate dominant weights, with the impact of extreme weather (C3) reaching a comprehensive weight of 0.1082, significantly higher than other indicators; (2) soil biodiversity (C8) exhibits the highest centrality (10.112), forming a key feedback node in the assessment system, highlighting the crucial role of biological factors in maintaining soil system stability; (3) green infrastructure coverage (C16) shows the highest cause degree (2.261), generating complex cascade effects through its influence on soil temperature, moisture, and biodiversity; (4) urban risk levels display significant spatial heterogeneity, with Lishui performing optimally (group benefit value S = 0.069) and Zhoushan performing poorest (S = 1.000), quantifying the impact of economic development patterns and geographical location on soil ecological risk. The dynamic coupling assessment framework and quantitative indicator system established in this study provide a new paradigm for understanding the complexity of urban soil ecosystems.

A Comparative Analysis of Multi-Criteria Decision-Making Methods and Normalization Techniques in Holistic Sustainability Assessment for Engineering Applications

A Comparative Analysis of Multi-Criteria Decision-Making Methods and Normalization Techniques in Holistic Sustainability Assessment for Engineering Applications

A Green Supplier Selection Through an MCDM Based Framework Under Fuzzy Environment

Uncertainty exists in reality decision-making problems. Therefore, fuzzy theories, fuzzy intervals, intuitionistic fuzzy, and Z-numbers have been proposed and successfully applied to multi-criteria decision-making (MCDM). However, since the information presented in the Z-numbers method is the subjective opinion of the decision-maker, the problem of overestimating or underestimating the reliability of the information may occur in both individual and group decision-making. The Extended Z-numbers (ZE-numbers) method was proposed in 2021 to solve this problem; hence, the decision-making process no longer relies on subjective opinions only but seeks external experts related to the problem to further vote on the evaluation value given by the internal decision-maker, in order to modify the information’s reliability, and thus to obtain a more realistic result. This paper combines the ZE-numbers method with the improved Elimination et Choix Traduisant la Realite II (ELECTRE II) proposed in 2022 and proposes a new MCDM method based on ZE-numbers, named ZE-ELECTRE II. The green supplier selection problem was used as an illustrative example. Meanwhile, the close analysis in this paper examines two primary dimensions of variability: (1) simulation of external expert voting situations to analyze the variations in information reliability and decision-making results and to cross-compare them with other MCDM methods; (2) investigation of the impact of internal preferences, as reflected through systematic adjustments to the weights of the evaluation criteria. The results show that uncertainty of information, reliability, and the perspectives of different decision-makers and expert groups can be considered through ZE-numbers. The proposed ZE-ELECTRE II is applicable to group decision-making, validates the robustness of the process, and is suitable for dynamic decision-making under varying decision-maker preferences. Furthermore, using the ZE-numbers along with the MCDM method can obtain more flexibility and more reliable results.

Power and Energy Transformation: Multi-Criteria Decision-Making Utilizing Complex q-Rung Picture Fuzzy Generalized Power Prioritized Yager Operators

Existing studies in decision-making often face limitations in adequately handling complex and uncertain environments. This study addresses these shortcomings by introducing novel operations and aggregation techniques tailored for complex q-rung picture fuzzy numbers (Cq-RPFNs). Building upon the drawbacks of conventional approaches, we extend Yeager's operations to Cq-RPFNs, enabling comprehensive operations such as addition, multiplication, scalar product, and scalar power. Additionally, two aggregation operators, the Complex q-rung Picture Fuzzy Generalized Power Prioritized Yager Weighted Average, and the Complex q-rung Picture Fuzzy Generalized Power Prioritized Yager Weighted Geometric are developed to overcome the limitations of existing aggregation methods. Rigorous analysis of these operators for properties such as idempotency and monotonicity ensures their robustness in decision-making processes. Through a comprehensive multi-criteria decision-making method utilizing these operators, decision-makers are empowered to navigate complex decision landscapes effectively. Comparative evaluations against existing alternatives highlight the superiority and effectiveness of the proposed approaches. A case study focusing on India's Power and Energy Sector in 2024 demonstrates practical application, supported by numerical examples that validate the methodologies' efficacy. This study addresses the drawbacks of existing approaches and provides valuable insights and tools for decision-makers operating in complex and uncertain domains.

Evaluating solar power plant sites using integrated GIS and MCDM methods: a case study in Kermanshah Province

This study utilizes an integrated Geographic Information System (GIS)-based Multi-Criteria Decision-Making (MCDM) approach to perform Solar Power Plant Site Selection (SPPSS) in Kermanshah Province, Iran. It introduces a novel group weighting method, the Dempster-based Best-Worst Method (DBWM), which combines weights vectors derived from experts’ opinions. The study also conducts a comprehensive sensitivity analysis comparing four GIS-based models for SPPSS. Findings indicate that the Inverse Distance Weighted (IDW) method is the most precise for interpolation, which was subsequently applied in the analysis. Results demonstrate that the GIS-based DBWM-Technique for Order Preference by Similarity to Ideal Solution (GIS-based DBWM-TOPSIS) model is the most stable, identifying slope as the primary criterion for SPPSS. Based on this model, 3% of the area is classified as very low suitability, 9% as low, 24% as moderate, 33% as high, and 31% as very high suitability. The study highlights the substantial impact of selecting appropriate spatial analysis techniques and uses normalization to standardize input criteria with varied units and ranges, enhancing comparability within the MCDM process. Eslamabad-e Gharb, Kangavar, and Gilan-e Gharb counties emerged as the most suitable locations for solar power plant (SPP) development.

Circular Intuitionistic Fuzzy EDAS Approach: A New Paradigm for Decision-Making in the Automotive Industry Sector

The automotive industry has become a robust manufacturing sector in the global manufacturing field, boosting and developing at high speed based on technology updates, environmental protection rules and regulation changes, and customers' new needs and demands. Such dynamics give rise to complex problems like optimizing cost strategy and innovation, operating under high sustainability standards, and navigating the supply chain. These difficulties have led to the need for more structured, logical and effective approaches to decision-making that can handle cross purposes and the world of slow changes and unpredictability. To mitigate these problems, in this context, this study presents the Circular Intuitionistic Fuzzy EDAS (CIF-EDAS) approach to handle decision-making challenges in the automotive industry. Integrating the circular intuitionistic fuzzy sets (CIFSs) with the EDAS approach provides a better assessment of uncertainty and hesitation, enhancing the reliability and strength of multi-criteria decision-making (MCDM) methods. The applicability of the proposed approach is illustrated by a case study in the automotive industry, in which the assessment of different scenarios based on conflicting criteria is effective. Moreover, a comparison analysis has been conducted, demonstrating the proposed approach's superiority and highlighting its efficacy in facilitating robust decision-making. The CIF-EDAS approach is clarified to exemplify its applicability in optimizing decision-making of complicated real-life industrial problems and demonstrating its capability to evolve for the automotive industry's newly emergent restrictive and dynamic demand.

A Novel Decision-Support Framework for Supporting Renewable Energy Technology Siting in the Early Design Stage of Microgrids: Considering Geographical Conditions and Focusing on Resilience and SDGs

This research is focused on microgrid design supporting tools and presents an innovative framework for renewable energy (RE) sources’ site selection, integrating multicriteria decision-making (MCDM) methods with resilience considerations and alignment to the Sustainable Development Goals (SDGs). It addresses present climatic challenges, identifies key causes of possible power failures, and develops strategies to mitigate their effects, while providing tools for energy managers and decision-makers to select suitable RE sources/technologies, based on geographical and sustainability criteria. The framework categorizes criteria into quantitative and qualitative types, adopting a cost (C)- and benefit (B)-based approach. The Analytic Hierarchy Process (AHP) calculates criteria weights to ensure accuracy and compatibility in decision-making, integrating SDG objectives into the evaluation process. This study focuses on five major RE options, photovoltaic (PV), wind, wave, tidal, and geothermal, analyzing more than 50 criteria for each energy type. This evaluation incorporates the expertise of over 50 experts and case studies, making it one of the most extensive research efforts in RE site selection. By systematically addressing resilience challenges and linking them with SDG priorities, this study provides a robust framework for evaluating and optimizing RE options. Its methodologies offer significant contributions to advancing sustainable energy development and enhancing energy systems’ resilience to climate and infrastructural challenges.

A Comprehensive and Systematic Review of Multi-Criteria Decision-Making (MCDM) Methods to Solve Decision-Making Problems: Two Decades from 2004 to 2024

A Comprehensive and Systematic Review of Multi-Criteria Decision-Making (MCDM) Methods to Solve Decision-Making Problems: Two Decades from 2004 to 2024

Urban Scenic Spot Activity Center Investment: Strategic Construction Company Selection Using the Grey System-II Thinking Compromise Ranking of Alternatives from Distance to Ideal Solution Multi-Criteria Decision-Making Method

Urban Scenic Spot Activity Center Investment: Strategic Construction Company Selection Using the Grey System-II Thinking Compromise Ranking of Alternatives from Distance to Ideal Solution Multi-Criteria Decision-Making Method

Comprehensive benefits evaluation of low impact development using scenario analysis and fuzzy decision approach

The comprehensive benefit evaluation of LID based on multi-criteria decision-making methods faces technical issues such as the uncertainties and vagueness in hybrid information sources, which can affect the overall evaluation results and ranking of alternatives. This study introduces a multi-indicator fuzzy comprehensive benefit evaluation approach for the selection of LID measures, aiming to provide a robust and holistic framework for evaluating their benefits at the community level. The proposed methodology integrates quantitative environmental and economic indicators with qualitative social benefit indicators, combining the use of the Storm Water Management Model (SWMM) and ArcGIS for scenario-based analysis, and the use of hesitant fuzzy language sets and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for decision-making. The framework’s novelty lies in the integration of the hesitant fuzzy weighted average algorithm to handle subjective uncertainties in expert judgment and the incorporation of multi-return period scenarios to enhance the robustness of the evaluation. The comprehensive benefits of 26 LID configurations were conducted in Chenglong Road Subdistrict under five rainfall return period scenarios of 5, 10, 20, 50, and 100 years. The results show that LID measures, particularly combinations of sunken green spaces and permeable paving, offer significant reductions in runoff and peak flow, along with improved flood mitigation across multiple return periods. Additionally, this study identifies practical LID implementation priorities for local decision-makers. The relative closeness is influenced by the indicators and non-calibrated parameters. However, it overall does not affect the main trends and key insights derived. The robustness of the proposed approach is reinforced by four key aspects: the impact of the Thiessen polygon method in ArcGIS, the influence of composite runoff coefficient and iterative optimization in SWMM, the effect of hesitant fuzzy linguistic sets and TOPSIS on weight calculation, and the contribution of simulations under different return periods to stability analysis.

A New Innovative Approach with Revised Pythagorean Fuzzy SWARA in Assessing of Soil Erodibility Factor

Soil erosion is a significant issue that threatens to soil in land degradation processes. The soil erodibility factor is a crucial tool for assessing the susceptibility of soils to erosion. The main aim of this study was to compare the results obtained using the Pythagorean Fuzzy-SWARA method which evaluates the impact weights of the criteria considered for the soil erodibility factor of the soils in the micro-basins located in the district of Çarşamba district of Samsun province, with the results obtained using the formula developed by Wischmeier and Smith. To achieve this case, 78 surface soil samples were collected from micro basins and analyzed for organic matter, clay, sand, silt, very fine sand, degree of structure, and hydraulic conductivity parameters. The erodibility factor was then calculated using these data, and spatial distribution maps were created for both methods. In this study, a revised of the Pythagorean Fuzzy-SWARA approach is proposed to calculate the weight values of the criteria. The values were 0.418 for organic matter, 0.227 for clay, 0.120 for degree of structure, 0.100 for hydraulic conductivity, 0.058 for sand, 0.053 for silt, and 0.039 for very fine sand. Soil erodibility values were determined using a linear combination approach, which normalized all parameter values by a standard scoring function. In estimating soil erodibility, our revised Pythagorean Fuzzy-SWARA approach was found to have a significant relationship with the soil erodibility factor method (R2 = 0.691 at the 1% level) compared to the soil erodibility factor method in estimating soil erodibility. Consequently, the method developed here suggests that fuzzy multi-criteria decision-making methods can be an alternative approach for determining the soil erodibility 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.

Comparative analysis on washability indices of anthracite coal by using MCDM methods

This paper presents the results of the Multi-Criteria Decision Making (MCDM) methods for determining the optimal separation density in the gravity separation of anthracite coal, and also suggests an appropriate MCDM model for this purpose. Five separation densities were selected as the output and seven coal washability indices were used as the input based on the washability results of coal. The SAW, TOPSIS, WASPAS, WISP, and CoCoSo methods were used in this study. The evaluation indices of the five methods were averaged as a new index. Finally, alternative A4 (i.e., the optimal separation density is 1650 kg/m<sup>3</sup>) was selected as the final separation density.

Financial performance evaluation of firms in BIST 100 index with ITARA and COBRA methods

Evaluating firms’ financial performance is important for survival in the competitive environment arising from technological advancements and gaining a competitive advantage. This study aims to assess the financial performances of firms traded on the Istanbul Stock Exchange 100 Index (Borsa Istanbul (BIST) 100 Index) between 2018 and 2022 using an integrated multi-criteria decision-making (MCDM) method. This is the first study to use a new combined approach proposed based on the indifference threshold-based attribute ratio analysis (ITARA) and cost estimation, benchmarking, and risk assessment (COBRA) methods, which have not been applied to corporate performance assessment. The ITARA method is used to find the weights of the criteria, and the ranking of the firms in terms of financial performance is obtained using the COBRA method. The results show that the firms with the highest financial performance are ISMEN, ALGYO, PGSUS, KOZAA, and IPEKE. The companies with the lowest financial performance are AKFGY, BAGFS, MGROS, KOZAL, and GOZDE. The results of this ranking provide important information for firms to recognize their position and for investors who want to invest in firms in the BIST 100 Index. Additionally, sensitivity analyses are performed to assess the impact of changes in the criteria on the ranking of the firms and to validate the results of the proposed method. A comparative analysis is made with different MCDM methods like the technique for order preference by similarity to ideal solution (TOPSIS), and combined compromise solution (CoCoSo) methods and Spearman’s rank correlation test results are presented. This approach leads to the conclusion that the proposed approach can be a useful and effective tool for assessing the financial performance of firms.

Identification and Prioritization of Criteria Affecting the Location of Safe Assembly Points Using the Best-Worst Method

Introduction: Identifying secure gathering spots during emergencies is a crucial step in emergency management across multiple industries, particularly in oil, gas, and petrochemical fields. This study was designed to identify and rank the factors affecting the placement of safe assembly points for implementation in the country’s process industries. Materials and Methods: This descriptive-exploratory study was conducted at the National Petrochemical Company. During the initial phase, criteria affecting the selection of safe assembly points were determined by examining scientific literature, standards, guidelines, along with national and international directives. A Delphi study involving 15 experts in emergency management experts was subsequently conducted to finalize the criteria list . During the second phase, these criteria were prioritized and ranked through a multi-criteria decision-making method utilizing and the Best-Worst Method. Results: The Delphi study was completed after two rounds, revealing 10 internal and 7 external criteria, amounting to 17 criteria that affect the determination of safe assembly points during emergencies in processing industries. Within the internal criteria, accessibility was the highest rated (weight: 0.1948), vulnerability to incidents was second (weight: 0.1543), and travel time was third (weight: 0.1109). Among the external criteria, the distance from nearby hazardous centres was the highest (weight: 0.2181), the type of potential scenarios was second (weight: 0.2081), and nearby emergency facilities placed third (weight: 0.1462). Conclusion: In process industries, the placement of safe assembly points during emergencies must take into account factors like the distance from surrounding hazardous areas, the type of possible incidents, ease of access, vulnerability to incidents, nearby emergency services, and wind patterns to guarantee optimal personnel protection.

Electric Vehicle Charging Station Positioning Problem: Multi-Criteria Decision Making Analysis with Entropy, CoCoSo and EDAS Methods

The increasing adoption of electric vehicles (EVs) underscores the critical need for an efficient and sustainable charging infrastructure. This study addresses the problem of optimal electric vehicle charging station (EVCS) location selection using a multi-criteria decision-making (MCDM) approach. Specifically, the Entropy, CoCoSo (Combined Compromise Solution), and EDAS (Evaluation Based on Distance from Average Solution) methods were applied to evaluate 25 potential locations in Altıeylül. The Entropy method was first employed to objectively determine the weight of each criterion based on their variability, ensuring that more significant factors had a greater impact on the final decision. Environmental, technical, and social criteria were incorporated to ensure that the selected sites would maximize accessibility, reduce air pollution, and enhance user convenience. The results revealed that Location_17 emerged as the top choice for EVCS placement based on both CoCoSo and EDAS rankings. While both methods provided consistent results for high-performing locations, significant discrepancies were observed for certain low-performing sites, highlighting the value of combining multiple MCDM methods. This study provides an informed framework for selecting optimal EVCS locations, offering a balanced evaluation of criteria, and contributes to the growing body of research on sustainable infrastructure planning for EVs.

Comparing MCDM Methods for Assessing the Lecturer Performance Index at Dipa Makassar University

Assessing or evaluating the Lecturer's Performance, presented through the Lecturer Performance Index, is a crucial element in the university system that impacts the quality of The University's Three Main Purposes, which include teaching, research, and community service. To ensure objective and accurate evaluations, methods that can accommodate various relevant criteria are needed. One increasingly popular approach is the Multi-Criteria Decision Making (MCDM) method, which allows for evaluating and comparing alternatives based on multiple criteria. This research compares several MCDM methods, namely Weighted Product (WP), Simple Additive Weighting (SAW), and Multi-Objective Optimization by Ratio Analysis (MOORA), used to assess the lecturer's performance at Dipa University Makassar. The WP method can handle criteria with different units, SAW is simpler and easier to apply, while MOORA offers a more comprehensive analysis. This study also identifies challenges in assessing the lecturer's performance, such as the influence of students' subjective evaluations that may lead to bias, as well as the addition of several of the lecturer's performance evaluation criteria such as teaching innovation, student mentoring, international and national journal publications, internal publications, and book publications. Additionally, self-development criteria based on academic fields are considered. The findings of this research are expected to provide insights into effective MCDM methods for the lecturer's performance evaluation and offer recommendations for educational institutions to choose the appropriate and transparent evaluation method. By using MCDM, the objectivity and accuracy of the lecturer's performance evaluations can be improved, biases can be reduced, and contributions can be made toward developing more fair and systematic evaluation standards.

Spatial decision-making model for priority development of Indonesia coast guard stations

Indonesia Coast Guard (IDNCG) is a paramilitary agency in charge of security and safety patrols in Indonesian maritime waters. The research objective is to develop priorities for the development of 35 IDNCG stations are expected to be constructed within 3 years and are considered priority project to face the increasing threats at sea. However, there is presently no system to provide support for the scientific solutions to prioritize station development despite the need for immediate decisions. This research was used to design model for determining station development priorities using the integration method of Geographic Information Systems (GIS), Analytical Hierarchy Process (AHP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). GIS was used as an effective tool for identifying and taking measurements in certain areas. Meanwhile, multi-criteria decision-making (MCDM) methods such as AHP and TOPSIS provided structural and pairwise quantification, as well as comparisons between elements and criteria for ranking station construction priorities. The most suitable alternative stations to be prioritized were determined by integrating the three methods which were classified as cost-effective for decision-making. Model was based on four criteria, including distance to Archipelagic Sea Lanes (ASL), Distance to the port, vulnerability coverage, and vessel density coverage. Stations were ranked based on a three-year development plan. The location ranking is then expressed in the form of a map to be used by policy makers in determining priorities for developing IDNCG Stations which will have an impact on increasing security and safety in Indonesian waters.