Q-rung Orthopair Research Articles

Enhancing healthcare supply chain management through artificial intelligence-driven group decision-making with Sugeno–Weber triangular norms in a dual hesitant q-rung orthopair fuzzy context

Enhancing healthcare supply chain management through artificial intelligence-driven group decision-making with Sugeno–Weber triangular norms in a dual hesitant q-rung orthopair fuzzy context

A novel multi-criteria group decision making algorithm for enhancing supply chain efficiency under high uncertainty during crisis based on q-rung orthopair fuzzy information

In recent years, q-rung orthopair fuzzy sets (q-ROFSs) have been becoming gradually more advantageous in handling information with high uncertainty. Several multi-criteria group decision-making (MCGDM) algorithms under q-ROFS information have been proposed in literature and used to solve various real-life problems. However, several shortcomings of some existing MCGDM algorithms under certain circumstances also emerged which limit their applicability. To overcome these challenges and deal with information under high uncertainty more accurately and reasonably, we propose a novel MCGDM algorithm under q-ROF information, i.e., (q-ROF-MCGDM), that retains the advantages of currently available methods, but extend its applicability by introducing a new q-rung orthopair fuzzy weighted averaging aggregation operator (q-ROFWAAO) along with a new entropy measure. The proposed q-ROF-MCGDM algorithm involves the steps: firstly, the input requirement in terms of alternatives, criteria and expert evaluations are required. Secondly, aggregating expert opinions using weights and normalizing decision matrices, and finally, calculating entropy weights and aggregating overall evaluations for final prioritization. Further, new operational laws have been developed and several necessary properties of the proposed q-ROFWAAO are also proved. Moreover, a sensitivity and comparative analysis has been carried out for validity and effectiveness of the proposed q-ROF-MCGDM algorithm. The proposed q-ROF-MCGDM algorithm has been implemented to enhance the efficiency of the United Arab Emirates (UAE) food industry under high uncertainty during the recent crisis. Finally, an evaluation of identifying and prioritizing the most severe food SC disruptions and appropriate mitigation strategies under crisis is provided to demonstrate applicability of the proposed q-ROF-MCGDM algorithm, and the obtained real case results confirm its usefulness. Findings of the study offer valuable insights to both food industry researchers and managers in developing effective recovery strategies, mitigating risks, and improving overall efficiency to ensure the survival of food businesses during the crisis.

Multiple attribute decision making based on score function of q-connection numbers, q-CNPWG aggregation operator of q-connection numbers, and set pair analysis theory in the environments of q-rung orthopair fuzzy numbers

Multiple attribute decision making based on score function of q-connection numbers, q-CNPWG aggregation operator of q-connection numbers, and set pair analysis theory in the environments of q-rung orthopair fuzzy numbers

An Integrated Q-Rung Orthopair Fuzzy (Q-ROF) for the Selection of Supply-Chain Management

The integration of sustainable indicators into supply-chain management (SCM), including cost, innovation capability, quality, service capability, long-term cooperation, environmental management system, pollution reduction, green image, social responsibility, and employment practices, has become essential for conducting strategic analyses of the entire supply-chain process competitive advantage. This study proposes a fuzzy integration multi-criteria decision-making (MCDM) method to solve SCM issues. To navigate this complexity, a multi-criterion decision-making (MCDM) framework is employed, integrating MCDM methods with fuzzy logic to effectively address subjective environmental criteria. This innovative approach not only enhances supply-chain management (SCM) but also emphasizes the necessity for ongoing innovation in tackling contemporary supply-chain challenges. It serves as a cornerstone for sustainable supplier selection practices and optimizing SCM processes. In this study, a hybrid fuzzy MCDM method is proposed for supplier selection. The method addresses supplier selection by utilizing evaluations from expert decision-makers based on predetermined criteria. This comprehensive approach ensures that all relevant factors are considered, promoting sustainable and efficient supply-chain management.

Healthcare Waste Management through Multi-Stage Decision-Making for Sustainability Enhancement

The possible threats that healthcare waste management (HWM) poses to the environment and public health are making it more and more crucial for medical facility administrators to be worried about it. This is in line with the global trend towards firms giving sustainability more of a priority. Many organizations, including the World Health Organization (WHO) and other organizations, as well as national and state laws, have mandated the proper disposal of infectious and hazardous healthcare waste. To effectively address the complex problem of selecting the best treatment option for HWM, a multi-criteria decision-making (MCDM) procedure must be used. The alternative ranking order method accounting for two-step normalization (AROMAN) methodology is provided in the context of q-rung orthopair fuzzy environment. This method comprises two steps of normalization and is based on the criteria importance through intercriteria correlation (CRITIC) paradigm. Whereas the AROMAN methodology uses vector and linear normalization techniques to improve the accuracy of the data for further computations, the CRITIC method assesses the intercriteria correlations and scores the significance of each criterion. The ranking from the proposed method is Al5>Al4>Al3>Al1>Al2. The study’s conclusions indicate that recycling (Al5) is the best option since it lessens trash production, aids in resource recovery, and protects the environment. Using this method helps decision makers deal with subjectivity and ambiguity more skillfully, promotes consistency and transparency in decision making, and streamlines the process of choosing the best waste management system. Sustainable waste management practices have been implemented in the biomedical industry with some success. The proposed technique is a helpful tool for legislators and practitioners seeking to improve waste management systems.

An integrated group decision-making framework for assessing S3PRLPs based on MULTIMOORA-WASPAS with q-rung orthopair fuzzy information

Sustainable third-party reverse logistics has gradually risen to prominence as a component of contemporary commercial development as a result of the acceleration of global economic integration and the prominent growth of information technology in the logistics industry. In the procedure of sustainable third-party reverse logistics providers (S3PRLPs) selection, indeterminacy and conflict information bring great challenges to decision experts. In view of the significant superiority of q-rung orthopair fuzzy (q-ROF) set in expressing uncertain and vague assessment information, this essay designs a comprehensive assessment framework through merging the best and worst method (BWM), Multiplicative Multi-objective Optimization by Ratio Analysis with Full Multiplicative Form (MULTIMOORA) and weighted aggregated sum product assessment (WASPAS) method to address the S3PRLPs selection issue with entirely unknown weight information under q-ROF setting. Firstly, we present a novel score function for comparing q-ROF numbers after analyzing the inadequacies of previous works. Secondly, the q-ROF Frank interactive weighted average (q-ROFFIWA) and q-ROF Frank interactive weighted geometric (q-ROFFIWG) operators are advanced based on the constructed operations to take into consideration the interactive impact of information fusion procedure. Thirdly, the q-ROF-MULTIMOORA-WASPAS decision framework is built based on novel score function and the developed operators, in which the synthetic weights of the criterion are determined by the modified BWM and entropy weight method to reflect both the subjectivity of the decision expert and the objectivity of the decision information. Ultimately, an empirical example was used to evaluate S3PRLPs to demonstrate the applicability and feasibility of the developed methodology, and a comparative analysis was conducted with other existing methods to highlight its advantages in dealing with complex decision problems. The discussion from the research indicates that the developed methodology can be used to evaluate S3PRLPs and further improve the quality of logistics services for organizations.

Enhancing breast cancer treatment selection through 2TLIVq-ROFS-based multi-attribute group decision making.

Breast cancer is an extremely common and potentially fatal illness that impacts millions of women worldwide. Multiple criteria and inclinations must be taken into account when selecting the optimal treatment option for each patient. The selection of breast cancer treatments can be modeled as a multi-attribute group decision-making (MAGDM) problem, in which a group of experts evaluate and rank alternative treatments based on multiple attributes. MAGDM methods can aid in enhancing the quality and efficacy of breast cancer treatment selection decisions. For this purpose, we introduce the concept of a 2-tuple linguistic interval-valued q-rung orthopair fuzzy set (2TLIVq-ROFS), a new development in fuzzy set theory that incorporates the characteristics of interval-valued q-rung orthopair fuzzy set (IVq-ROFS) and 2-tuple linguistic terms. It can express the quantitative and qualitative aspects of uncertain information, as well as the decision-makers' level of satisfaction and dissatisfaction. Then, the 2TLIVq-ROF weighted average (2TLIVq-ROFWA) operator and the 2TLIVq-ROF weighted geometric (2TLIVq-ROFWJ) operator are introduced as two new aggregation operators. In addition, the multi-attribute border approximation area comparison (MABAC) method is extended to solve the MAGDM problem with 2TLIVq-ROF information. To demonstrate the efficacy and applicability of the suggested model, a case study of selecting the optimal breast cancer treatment is presented. The results of the computations show that the suggested MAGDM model is able to handle imprecision and subjectivity in complicated decision-making scenarios and opens new research scenarios for scholars.

Network mapping of climate change priorities in USA: golden cut bipolar q-ROFSs

The purpose of this study is to identify affordable and clean energy-based climate change priorities in USA for the sustainable development. Five factors that can influence clean energy-based sustainable development are weighted with Multi step wise weight assessment ratio analysis approach. Furthermore, selected priorities for the climate change goal of sustainable development are evaluated. The novelty of this study is presenting affordable and clean energy-based climate change priorities for the sustainable development by considering an original fuzzy decision-making model based on M-SWARA and ELECTRE with bipolar q-rung orthopair fuzzy sets and golden cut. Because they include both membership, non-membership and hesitancy, it can be possible to perform more effective analysis. This issue helps to reach more reliable results. The main findings solve the problem that government support has the greatest weight with respect to the clean energy-based sustainable development.

Effective multi-attribute group decision-making approach to study astronomy in the probabilistic linguistic q-rung orthopair fuzzy VIKOR framework

Effective multi-attribute group decision-making approach to study astronomy in the probabilistic linguistic q-rung orthopair fuzzy VIKOR framework

Supplier selection in green supply chain management using correlation-based TOPSIS in a q-rung orthopair fuzzy soft environment

Supplier selection in green supply chain management using correlation-based TOPSIS in a q-rung orthopair fuzzy soft environment

MCDM approach integrating q-rung orthopair fuzzy sets and social network analysis for ranking UPI digital payments in India: a case study

MCDM approach integrating q-rung orthopair fuzzy sets and social network analysis for ranking UPI digital payments in India: a case study

Multiattribute group decision-making based on weighted correlation coefficient of linguistic q-rung orthopair fuzzy sets and TOPSIS method

Multiattribute group decision-making based on weighted correlation coefficient of linguistic q-rung orthopair fuzzy sets and TOPSIS method

An integrated group decision-making method under q-rung orthopair fuzzy 2-tuple linguistic context with partial weight information.

Considering the advantages of q-rung orthopair fuzzy 2-tuple linguistic set (q-RFLS), which includes both linguistic and numeric data to describe evaluations, this article aims to design a new decision-making methodology by integrating Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and qualitative flexible (QUALIFLEX) methods based on the revised aggregation operators to solve multiple criteria group decision making (MCGDM). To accomplish this, we first revise the extant operational laws of q-RFLSs to make up for their shortcomings. Based on novel operational laws, we develop q-rung orthopair fuzzy 2-tuple linguistic (q-RFL) weighted averaging and geometric operators and provide the corresponding results. Next, we develop a maximization deviation model to determine the criterion weights in the decision-making procedure, which accounts for partial weight unknown information. Then, the VIKOR and QUALIFLEX methodologies are combined, which can assess the concordance index of each ranking combination using group utility and individual maximum regret value of alternative and acquire the ranking result based on each permutation's general concordance index values. Consequently, a case study is conducted to select the best bike-sharing recycling supplier utilizing the suggested VIKOR-QUALIFLEX MCGDM method, demonstrating the method's applicability and availability. Finally, through sensitivity and comparative analysis, the validity and superiority of the proposed method are demonstrated.

An integrated group decision-making method under q-rung orthopair fuzzy 2-tuple linguistic context with partial weight information

Considering the advantages of q-rung orthopair fuzzy 2-tuple linguistic set (q-RFLS), which includes both linguistic and numeric data to describe evaluations, this article aims to design a new decision-making methodology by integrating Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and qualitative flexible (QUALIFLEX) methods based on the revised aggregation operators to solve multiple criteria group decision making (MCGDM). To accomplish this, we first revise the extant operational laws of q-RFLSs to make up for their shortcomings. Based on novel operational laws, we develop q-rung orthopair fuzzy 2-tuple linguistic (q-RFL) weighted averaging and geometric operators and provide the corresponding results. Next, we develop a maximization deviation model to determine the criterion weights in the decision-making procedure, which accounts for partial weight unknown information. Then, the VIKOR and QUALIFLEX methodologies are combined, which can assess the concordance index of each ranking combination using group utility and individual maximum regret value of alternative and acquire the ranking result based on each permutation’s general concordance index values. Consequently, a case study is conducted to select the best bike-sharing recycling supplier utilizing the suggested VIKOR-QUALIFLEX MCGDM method, demonstrating the method’s applicability and availability. Finally, through sensitivity and comparative analysis, the validity and superiority of the proposed method are demonstrated.

Ensemble feature selection using q-rung orthopair hesitant fuzzy Hamacher, Einstein and Dombi Aggregation operators

Ensemble feature selection using q-rung orthopair hesitant fuzzy Hamacher, Einstein and Dombi Aggregation operators

An enhanced fuzzy IDOCRIW-COCOSO multi-attribute decision making algorithm for decisive electric vehicle battery recycling method

An enhanced fuzzy IDOCRIW-COCOSO multi-attribute decision making algorithm for decisive electric vehicle battery recycling method

Optimizing decision-making in electric power system selection: A generalized approach based on Hamacher aggregation operators for q-rung orthopair fuzzy soft sets

Optimizing decision-making in electric power system selection: A generalized approach based on Hamacher aggregation operators for q-rung orthopair fuzzy soft sets

Generalized extended Bonferroni means for isomorphic membership grades

Generalized extended Bonferroni means for isomorphic membership grades

Q-Rung orthopair fuzzy 2-tuple linguistic WASPAS algorithm for patients’ prioritization based on prioritized Maclaurin symmetric mean aggregation operators

Due to the fuzziness of the medical field, q-rung orthopair fuzzy 2-tuple linguistic (q-RF2L) set is the privileged way to aid medical professionals in conveying their assessments in the patient prioritization problem. The theme of the present study is to put forward a novel approach centered around the merging of prioritized averaging (PA) and the Maclaurin symmetric mean (MSM) operator within q-RF2L context. According to the prioritization of the professionals and the correlation among the defined criteria, we apply both PA and MSM to assess priority degrees and relationships, respectively. Keeping the pluses of the PA and MSM operators in mind, we introduce two aggregation operators (AOs), namely q-RF2L prioritized Maclaurin symmetric mean and q-RF2L prioritized dual Maclaurin symmetric mean operators. Meanwhile, some essential features and remarks of the proposed AOs are discussed at length. Based on the formulated AOs, we extend the weighted aggregated sum product assessment methodology to cope with q-RF2L decision-making problems. Ultimately, to illustrate the practicality and effectiveness of the stated methodology, a real-world example of patients’ prioritization problem is addressed, and an in-depth analysis with prevailing methods is performed.

Novel Distance Measures of q-Rung Orthopair Fuzzy Sets and Their Applications

The q-rung orthopair fuzzy sets (q-ROFSs), a novel concept for processing vague information, offer a more potent and all-encompassing method compared to traditional fuzzy sets, intuitionistic fuzzy sets, and Pythagorean fuzzy sets. The inclusion of the parameter q allows for the q-rung orthopair fuzzy sets to capture a broader range of uncertainty of information. In this paper, we present two novel distance measures for q-ROFSs inspired by the Jensen–Shannon divergence, called DJS_2D and DJS_3D, and we analyze some properties they satisfy, such as non-degeneracy, symmetry, boundedness, and triangular inequality. Then, the normalized distance measures, called DJS_2D˜ and DJS_3D˜, are proposed and we verify their rationality through numerical experiments. Finally, we apply the proposed distance measures to practical scenarios, including pattern recognition and multicriteria decision-making, and the results demonstrate the effectiveness of the proposed distance measures.