A Comprehensive Review of the Novel Weighting Methods for Multi-Criteria Decision-Making

In the realm of multi-criteria decision-making (MCDM) problems, the selection of a weighting method holds a critical role. Researchers from diverse fields have consistently employed MCDM techniques, utilizing both traditional and novel methods to enhance the discipline. Acknowledging the significance of staying abreast of such methodological developments, this study endeavors to contribute to the field through a comprehensive review of several novel weighting-based methods: CILOS, IDOCRIW, FUCOM, LBWA, SAPEVO-M, and MEREC. Each method is scrutinized in terms of its characteristics and steps while also drawing upon publications extracted from the Web of Science (WoS) and Scopus databases. Through bibliometric and content analyses, this study delves into the trend, research components (sources, authors, countries, and affiliations), application areas, fuzzy implementations, hybrid studies (use of other weighting and/or ranking methods), and application tools for these methods. The findings of this review offer an insightful portrayal of the applications of each novel weighting method, thereby contributing valuable knowledge for researchers and practitioners within the field of MCDM.WOS:0009972313000012-s2.0-85160203389Emerging Sources Citation IndexarticleUluslararası işbirliği ile yapılan - EVETHaziran2023YÖK - 2022-2

Interval-valued Fermatean fuzzy heronian mean operator-based decision-making method for urban climate change policy for transportation activities

Climate change affects the world. Due to excessive GHG emissions, urban transportation contributes to this threat. Policymakers and authorities want to reduce transportation-related GHG emissions. An imaginary urban area with high transportation-related greenhouse gas emissions, dense, interconnected transportation modes, and a high population density is considered. Istanbul, Turkey meets the criteria of this imaginary place, so the case analysis considers this city. Istanbul's decision-makers are looking for effective strategies to prioritize urban climate change policy alternatives for transportation activities. Four alternative strategies and 13 criteria are presented in this context. Innovative multi-criteria decision-making (MCDM) method with the interval-valued Fermatean fuzzy sets (IVFFSs) strategies is proposed for advantage-prioritization so decision-makers can select the most effective strategies for policies. Utilizing the IVFFSs, the proposed method effectively tackles the qualitative/quantitative data and uncertain information that occurs in realistic applications. In this study, firstly IVFF-heronian mean operators with their desirable characteristics are presented to aggregate the IVFF information. The proposed operators can overcome the drawbacks of existing IVFF information-based operators by considering the relationships between IVFF numbers. Based on IVFF heronian mean operators, a hybrid decision-making framework is proposed by integrating criteria importance through inter-criteria correlation (CRITIC), rank sum (RS), and the double normalization-based multi-aggregation (DNMA) methods with IVFF information. In this method, the CRITIC and RS methods are implemented to derive the objective and subjective weights of the considered evaluation criteria and DNMA is applied to prioritize urban climate change policy alternatives for transportation activities. Sensitivity and comparative analyses with existing studies confirm the proposed framework. The evaluation results show that the integration of transportation sectors, strategies, and innovations across different urban areas in all regions option has the highest overall utility degree (0.731) among a set of four urban climate change policy alternatives for transportation activities.</p

Ranking Barriers Impeding Sustainability Adoption in Clean Energy Supply Chains: A Hybrid Framework With Fermatean Fuzzy Data

In this article, we aim to prioritize barriers hindering sustainability inclusion within clean energy supply chains. Supply chain management is a crucial aspect of the clean energy sector, whereby the global supply chains can be enforced with policies to adopt sustainability/green practices. The literature infers that the adoption of sustainability is not direct, and multiple barriers impede the process, driving researchers to rank these barriers. Previous studies on prioritizing barriers cannot effectively model uncertainty; experts' reliability is directly assigned; interrelationships/hesitation of criteria/experts are usually not considered; and there is a lack of personalized ordering based on individuals' preferences. Motivated by these gaps, the authors put forward an integrated framework with a Fermatean fuzzy set, variance-based criteria importance through intercriteria correlation for determining experts' and criteria weights, and ranking procedure with complex proportional assessment-Copeland for personalized ordering of barriers. The usefulness of the developed approach is testified through a case example. Results infer that wastage/pollution reduction and profit from green production are the two top criteria considered for rating sustainability barriers, while limited governmental policies, monitoring/control issues, and expertise mismatch are the top three barriers impeding sustainability adoption. Finally, sensitivity and comparative analyses are performed to understand the framework's efficacy

A SPHERICAL FUZZY BASED DECISION MAKING FRAMEWORK WITH EINSTEIN AGGREGATION FOR COMPARING PREPAREDNESS OF SMEs IN QUALITY 4.0

Researchers work hard to embrace technological changes and redefine the quality management as Quality 4.0 (Q 4.0). In this context, the purpose of the current work is twofold. First, it aims to compare the preparedness of the small and medium enterprises (SMEs) for sustaining in Q4. Second, it intends to propose a novel hybrid spherical fuzzy based multi-criteria group decision-making (MAGDM) framework with Einstein aggregation (EA). A real-life case study on six SMEs is carried out with the help of three experts. For aggregating the individual responses (using spherical fuzzy numbers or SFNs), EA is used. Then two very recent models such as Simple Ranking Process (SRP) and Symmetry Point of Criterion (SPC) are extended using SFN to rank the SMEs. Finally, the validation tests and sensitivity analysis are carried out. It is noted that the application of analytical tools, knowledge management and use of technology under the support and mentorship of visionary leadership are the key criteria for building up the capability to embrace Q 4.0. Interestingly, it is noted that medium scale firms are better prepared than small-scale enterprises. This work is apparently a first of its kind that focuses on SMEs for assessing their quality management practices in Industry 4.0 era

A novel spherical fuzzy AHP method to managing waste from face masks and gloves : an Istanbul-based case study

Waste management has emerged as a critical issue in the wake of the COVID-19 pandemic and the earthquake that struck southeast Turkey on February 6th, 2023, particularly regarding the disposal of face masks and gloves. Extensively utilized for disease prevention and maintaining personal hygiene, these items are categorized as medical waste, presenting significant disposal challenges in Turkey. This study aims to overcome these challenges by prioritizing key factors in waste management during the COVID-19 era through the application of the Spherical Fuzzy Analytic Hierarchy Process (SF-AHP) in Istanbul. By conducting a comprehensive literature review and consulting with experts, relevant criteria for managing this medical waste have been identified and prioritized. Furthermore, a sensitivity analysis of the decision support model is performed to evaluate its robustness. The data highlight the crucial importance of recycling, landfilling, and incineration capacities, regulatory frameworks, and incineration costs as primary determinants and criteria shaping the waste management landscape. The sensitivity analysis highlights the resilience of our proposed methodology, demonstrating consistent and robust prioritization outcomes even with varying criteria weights, thereby validating the reliability of the methodology in informing policy decisions. The originality of this study lies in its innovative application of spherical fuzzy sets—offering high accuracy and compatibility with human reasoning—to the management of face masks and gloves waste, an area not previously explored using Spherical Fuzzy Multi-Criteria Decision Making (SF-MCDM) in current literature. This novel approach introduces a rigorous and pioneering methodology for investigating this specific aspect of waste management and enriches the academic conversation by providing a practical SF-MCDM framework

Using multi-attribute decision-making technique for the selection of agribots via newly defined fuzzy sets

Reference parameter mapping (passing arguments by reference) is a technique where the reference (like to find physical meaning, memory address) of a parameter is passed to a function or procedure, rather than a copy of the parameter's value. This approach enables changes made to the parameter within the function to affect the original data. In decision-making systems, reference parameter mapping (passing arguments by reference) offers several key advantages that enhance flexibility, consistency, and efficiency. This is especially useful in scenarios where decisions are based on shared data, complex interactions, and iterative updates. In this paper, a new class of fuzzy set was introduced that is known as the $\left({q}_{1}^{}, {q}_{2}^{}\right)$-rung Diophantine fuzzy set, where ${q}_{1}$ and ${q}_{2}$ are reference parameter mappings. Most of the classical and new generalized fuzzy sets are exceptional classes of $\left({q}_{1}^{}, {q}_{2}^{}\right)$-rung Diophantine fuzzy set ($\left({q}_{1}^{}, {q}_{2}^{}\right)$-$RDFS$) like intuitionistic fuzzy set ($IFS$), Pythagorean fuzzy Sets ($P\mathrm{y}FS$s) and $\mathfrak{q}$-rung Orthopair fuzzy sets ($\mathfrak{q}$-$ROFS$s), linear Diophantine fuzzy sets ($LDFS$), and so on. It is commonly seen in multi-criteria decision-making ($MCDM$) scenarios that the presence of imprecise information and ambiguity in the decision maker's judgment affects the resolution technique. Fuzzy models that are now in use are unable to effectively manage these uncertainties to provide an appropriate balance during the decision-making process. Using control (reference) parameter mappings, $\left({q}_{1}^{}, {q}_{2}^{}\right)$-$RDFS$s are potent fuzzy model that can handle these challenging problems. Two more novel ideas are presented in this work: $\left({q}_{1}^{}, {q}_{2}^{}\right)$-rung Diophantine fuzzy averaging and geometric aggregation operators with newly defined score and accuracy functions. An agricultural field robot $MCDM$ framework was proposed, incorporating $\left({q}_{1}^{}, {q}_{2}^{}\right)$-rung Diophantine fuzzy averaging and geometric aggregation operators. This strategy's efficacy and adaptability in addressing real-world issues were demonstrated by its application to get more benefits. This study has a lot of potential to handle difficult socioeconomic issues and offer vital information to academic, government, and analysts searching for fresh approaches in a variety of fields

Analysis of power aggregation operators through circular intuitionistic fuzzy information and their applications in machine learning analysis

Machine learning language is very valuable for depicting different problems, especially computer language, data mining, data sciences, and machine language. The circular intuitionistic fuzzy set (C-IFS) is a flexible approach to fuzzy sets and intuitionistic fuzzy sets. Keep in mind the flexibility of C-IFS, decision-maker used C-IFS to cope with incomplete and redundant human opinions accurately. Furthermore, power operators are used for depicting or aggregating the collection of data into a singleton set. In this manuscript, we explore the power operators for circular intuitionistic fuzzy (C-IF) information, such as C-IF power weighted averaging (C-IFPWA) operator, C-IF power weighted ordered averaging (C-IFPWOA) operator, C-IF power weighted geometric (C-IFPWG) operator, and C-IF power weighted ordered geometric (C-IFPWOG) operator. Some properties of the above information are also stated. Additionally, we evaluate the procedure of the multi-attribute decision-making (MADM) technique for resolving the utilization of the most suitable part of machine learning in complicated scenarios. Finally, we illustrate some numerical examples for addressing the comparison between proposed techniques and existing methods to show the effectiveness and reliability of the presented operators

Innovation Performance Analysis of G20 Countries: A Novel Integrated LOPCOW-MAIRCA MCDM Approach Including the COVID-19 Period

Purpose: This study aims to examine the innovation performance of G20 countries in 2018-2022 with multi criteria decision making methods. When the 5-year performance was analyzed, it was also revealed whether the COVID-19 outbreak has an impact on the innovation performance of the countries. Methodology: An integrated LOPCOW (Logarithmic Percentage Change-driven Objective Weighting) - MAIRCA (Multi Attribute Ideal-Real Comparative Analysis) method was applied in the study. First, the indicators representing innovation performance (institutions, human capital, and research, infrastructure, market sophistication, business sophistication, knowledge and technology outputs, creative outputs) was objectively weighted by the LOPCOW method. Then, the innovation performance of G20 countries was calculated with the MAIRCA method. Finally, a comparative analysis was also presented to support the findings. Findings: As a result of the innovation performance analysis using multi criteria decision making methods, human capital, and research were found to be the most important indicators, and the United States was found to be the country with the best innovation performance. In the sensitivity and comparative analysis, it was concluded that the integrated LOPCOW-MAIRCA method provides robust outputs. Originality: This study makes original contributions by analyzing the impact of the COVID-19 pandemic on the innovation performance of countries considering the 2018-2022 period and the integrated multi criteria decision making methods it uses that have not yet been applied in the literature

An intuitionistic fuzzy entropy-based gained and lost dominance score decision-making method to select and assess sustainable supplier selection

Sustainable supplier selection (SSS) is recognized as a prime aim in supply chain because of its impression on profitability, adorability, and agility of the organization. This work introduces a multi-phase intuitionistic fuzzy preference-based model with which decision experts are authorized to choose the suitable supplier using the sustainability "triple bottom line (TBL)" attributes. To solve this issue, an intuitionistic fuzzy gained and lost dominance score (IF-GLDS) approach is proposed using the developed IF-entropy. To make better use of experts' knowledge and fully represent the uncertain information, the evaluations of SSS are characterized in the form of intuitionistic fuzzy set (IFS). To better distinguish fuzziness of IFSs, new entropy for assessing criteria weights is proposed with the help of an improved score function. By considering the developed entropy and improved score function, a weight-determining process for considered criterion is presented. A case study concerning the iron and steel industry in India for assessing and ranking the SSS is taken to demonstrate the practicability of the developed model. The efficacy of the developed model is certified with the comparison by diverse extant models