Q-rung Orthopair Research Articles

Retraction Note: A New Extended VIKOR Approach Using q-Rung Orthopair Fuzzy Sets for Sustainable Enterprise Risk Management Assessment in Manufacturing Small and Medium-Sized Enterprises

The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation's findings the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article.

Enhancing industrial robot selection through a hybrid novel approach: integrating CRITIC-VIKOR method with probabilistic uncertain linguistic q-rung orthopair fuzzy

The increasing complexity and novel features of industrial robots have made their selection for specific applications a challenging task. Decision-makers are faced with the daunting task of navigating through various attributes and specifications, often under conditions of ambiguity and uncertainty. To assist in this complex decision-making process, this paper introduces a novel decision-making framework based on probabilistic uncertain linguistic q-rung orthopair fuzzy sets (PULq-ROFS). This framework effectively combines the strengths of probabilistic uncertain linguistic term set (PULTS) and q-rung orthopair fuzzy set (q-ROFS) to provide a more robust approach for handling ambiguity and uncertainty in the robot selection process. The proposed methodology integrates a multi-attribute group decision-making (MAGDM) approach. This approach utilizes the VIseKriterijumska Optimizacija I KOmpromisno Resenje (VIKOR) method in conjunction with the criterion importance via inter-criteria correlation (CRITIC) method. The CRITIC method determines attribute weights by analyzing both the differences and contrast intensity of criteria, thereby accounting for the relative strength and conflict among them. VIKOR is then employed to aggregate individual regret and group utility, resulting in a compromise solution that guides decision-makers toward the optimal robot selection. Proposed method provide the clarity and confidence to decision makers for choice of attributes and crediting this enhancement to the framework’s transparency and its ability to incorporate a wide range of stakeholder perspectives. This framework facilitates a more inclusive decision-making process that acknowledges differing viewpoints and preferences. This proposed approach not only directs users toward optimal selections but also encourages collaboration among decision-makers, promoting a sense of shared ownership and responsibility in the selection process. This paper select the best robot by utilizing the benefits of both CRITIC and VIKOR method. The effectiveness of this integrated approach is validated through parameter and comparative analysis. The results demonstrate the potential applicability of the proposed methodology in real-world industrial robot selection scenarios, providing decision-makers with a powerful tool to navigate the complexities of modern robotic systems.

On signless Laplacian energy of inverse dominating complex interval-valued q-rung orthopair fuzzy graph with application

On signless Laplacian energy of inverse dominating complex interval-valued q-rung orthopair fuzzy graph with application

A Novel Approach for Multi-Criteria Decision-Making Problem with Linguistic q-Rung Orthopair Fuzzy Attribute Weight Information

Linguistic q-Rung orthopair fuzzy set is a new extension of the linguistic Pythagorean fuzzy set, which effectively represents the fuzzy and uncertain decision-making information based on qualitative modeling. However, its operational rules are unable to process pure linguistic exponential calculations, in which the exponents are represented using linguistic q-Rung orthopair fuzzy values and the bases are represented as linguistic terms or interval linguistic numbers. This greatly restricts its application in decision making under complex environments. As the complement of the existing linguistic q-Rung orthopair fuzzy operational rules, this paper defines linguistic q-Rung orthopair fuzzy calculation rules, including division, subtraction, and exponent operations. Based on theorem-based proofs, the relevant properties of the calculation rules have been analyzed, such as commutative law, distributive law, symmetry, and so on. Moreover, in order to facilitate the application of linguistic q-Rung orthopair fuzzy theory, this paper introduces the concept of dual linguistic q-Rung orthopair fuzzy value. Building on this foundation, a series of weighted aggregation operators for the calculations involving linguistic q-Rung orthopair fuzzy values and dual linguistic q-Rung orthopair fuzzy values have been designed. In conclusion, a novel pure linguistic multi criteria decision-making methodology is introduced in this work. The validity and utility of the proposed method are demonstrated via a real-world application in the decision process of energy resource exploitation.

Risk assessment of poverty-returning based on hybrid q-rung orthopair fuzzy EDAS and prospect theory

PurposeAccurately identifying the risk of poverty-returning is a complex and critical challenge in current poverty alleviation efforts. However, there is currently no study on evaluation methods for the risk of poverty-returning. This study aims to establish a robust and systematic approach for an evaluation framework for the risk of poverty-returning.Design/methodology/approachBased on relevant assessment criteria, a maximum deviation method was established to identify the weights of the indicators. A complex evaluation methodology using prospect theory (PT), a q-rung orthopair fuzzy set (QrOFS) and evaluation relying on distance from average solution [EDAS] (QrOFS-PT-EDAS) was developed to evaluate the poverty-returning risks. Some policy recommendations to reduce the risk of poverty-returning have also been put forward.FindingsHis study identifies the risk factors of poverty relapse from nine aspects, including natural disasters, accidents and policy-driven poverty relapse. In addressing the evaluation challenge arising from uncertain decision-making, the QrOFS aligns more with people’s thinking habits and expression methods in complex environments. The proposed hybrid evaluation framework accurately measures the poverty-returning risk, which is beneficial for the formulation of policy recommendations.Originality/valueA scientific and comprehensive assessment system index for poverty-returning is constructed. A hybrid QrOFS-PT-EDAS framework is presented to make the evaluation results more scientific and objective. Several strategic recommendations for reducing the poverty-returning risk are presented. This study offers a novel framework for assessing poverty-returning issues that can be extended to many other areas.

Q- Rung Orthopair Fuzzy Sets and Topological Spaces

The concept of q-rung orthopair fuzzy set, where q is a positive integer, introduced by Yager, is studied in the present paper and fundamental properties of it are examined. The concept of the 1-rung orthopair fuzzy set coincides with Atanassov’s intuitionistic fuzzy set, a 2-rung orthopair fuzzy set is known as a Pythagorean fuzzy set, while a 3-rung orthopair fuzzy set is referred to as a Fermatean fuzzy set. Also the ordinary notion of topological space is extended in this work to a q-rung orthopair fuzzy environment, as well as the fundamental properties and concepts of convergence, continuity, compactness and of Hausdorff topological space. All these contents are illustrated by suitable examples.

Group decision-making based on multiplicative consistency and consensus of linguistic interval-valued q-rung orthopair fuzzy preference relations

Group decision-making based on multiplicative consistency and consensus of linguistic interval-valued q-rung orthopair fuzzy preference relations

Integrated MADM approach based on extended MABAC method with Aczel–Alsina generalized weighted Bonferroni mean operator

Currently, q-rung orthopair (q-ROF) set theory is one of the most effective set theories in dealing uncertainty associated with imprecise information. In complex decision-making problems, input variables can be described by q-ROF numbers to cope ambiguity. While, generalized weighted Bonferroni mean (GWBM) operator can reflect correlation among input arguments. Aczel–Alsina operations underline fair and accurate evaluation of decision-makers. Harnessing these benefits, a pioneering extension of the GWBM operator based on Aczel–Alsina operations is introduced. Simultaneously, a novel generalized distance measure is crafted, drawing inspiration from Dice and Jaccard similarities. Beside these, using stepwise weight assessment ratio analysis (SWARA) and multi-attribute border approximation area comparison (MABAC) methods, this study pioneers an integrated method, q-ROF-SWARA-MABAC for assessing and prioritizing factors and alternatives on q-ROF environment. Later, with the suggested model, a case study on high-speed rail corridor (HSRC) for India is solved, revealing Varanasi-Howrah HSRC as the most preferable choice.. Moving forward, detailed sensitive analysis of suggested model is performed to explore the pertinence and supremacy. Eventually, the outcomes manifest that novel framework is flexible, reliable, accurate and could be viable option to consider for future use.

Optimizing Warehouse Selection Through Topological Index Based Energy of q-Rung Orthopair Fuzzy Graphs

In recent years, the study of q-rung orthopair fuzzy graphs (or q-ROFGs) has gained significant attention due to their ability to model complex and uncertain relationships. One crucial aspect in analyzing such graphs is the computation of their energy, which provides insights into their structural properties and dynamics. q-rung orthopair fuzzy sets were introduced, as a method for adjusting the range of indication of decision information by modifying a parameter q. This article presents a comprehensive approach for computing the energy of q-ROFGs. The new concepts of Zagreb energy of q-ROFGs are proposed and some bounds are determined. To demonstrate the applicability of the developed approach and show its viability, a numerical model related to the selection of the most suitable warehouse location is provided. Lastly, a comparative analysis is done, and the proposed approach is found to be compatible with existing models.

Assessing the factors of blockchain technology-enabled hospitals using an integrated interval-valued q-rung orthopair fuzzy decision-making model

Assessing the factors of blockchain technology-enabled hospitals using an integrated interval-valued q-rung orthopair fuzzy decision-making model

Correlation Measures for q-rung Orthopair m-polar Fuzzy Sets with Application to Pattern Recognition

Correlation provides a relationship between two quantities with the help of certain statistical data. It shows the strength of the relationship amongst different alternatives. q-rung orthopair fuzzy setting offers extended space for the selection of values of the two components. There is no iota in the fact that the multipolarity factor reduces the chances of error. We study correlation in the framework of q-rung orthopair m-polar fuzzy set to judge the efficiency of this system in real-life problems and decision-making. We present some major characteristics of the correlation measure in the said setting. An application to pattern recognition is also incorporated. To elaborate on the ideas of correlation measures and q-rung orthopair m-polar fuzzy set, we instigate a comparison analysis to probe the supremacy and dominance of propound abstraction.

Fairly Aggregation Operators Based on Complex p, q-Rung Orthopair Fuzzy Sets and Their Application in Decision-Making Problems

The decision-making technique is used to evaluate the best optimal among the collection of finite alternatives. Further, the technique of complex p, q-rung orthopair fuzzy (CPQROF) set is very reliable and dominant due to parameters “p” and “q”. In contrast, the technique of simple q-rung orthopair fuzzy sets is the special case of the CPQROF set. The major theme of this article is to expose the novel theory of fairly operational laws based on CPQROF numbers (CPQROFNs). Further, we evaluate the weighted fairly aggregation operators based on CPQROF information, called CPQROF weighted fairly averaging (CPQROFWFA) operator and CPQROF ordered weighted fairly averaging (CPQROFOWFA) operator. Some properties are also discussed for the above operators. Additionally, we validate the system of multi-attribute decision-making (MADM) difficulties based on initiated operators. Finally, we associate our suggested ranking consequences with approximately prevailing techniques to demonstrate the proposed theory's sovereignty and validity.

Evaluating the financial credibility of third-party logistic providers through a novel frank operators-driven group decision-making model with dual hesitant linguistic q-rung orthopair fuzzy information

Evaluating the financial credibility of third-party logistic providers through a novel frank operators-driven group decision-making model with dual hesitant linguistic q-rung orthopair fuzzy information

Interval-valued q-rung orthopair fuzzy complex proportional assessment-based approach and its application for evaluating the factors of blockchain technology in various domains

Interval-valued q-rung orthopair fuzzy complex proportional assessment-based approach and its application for evaluating the factors of blockchain technology in various domains

Power equipment supplier evaluation under a q-rung orthopair fuzzy set based decision making model

Power equipment supplier evaluation under a q-rung orthopair fuzzy set based decision making model

Application of Multi-criteria Decision-making Methods for Forensic Analysis of Mechanical Parts in Vehicle Accidents using q-Rung Orthopair Fuzzy Numbers

This study explores the application of a Multi-Criteria Decision-Making (MCDM) approach based on q-rung orthopair fuzzy numbers to identify the origin of mechanical parts found at vehicle accident scenes. The primary objective is to determine the most likely vehicle to which these parts belong by evaluating key criteria such as compatibility, damage level, serial number matching, and color compatibility. Q-rung orthopair fuzzy numbers offer an advanced method to handle the inherent uncertainty and vagueness associated with forensic evaluations, particularly in scenarios where data is incomplete or imprecise. The proposed methodology involves defining the criteria, assigning membership and non-membership degrees using q-rung orthopair fuzzy sets, and applying an aggregation process to effectively rank alternatives. This approach facilitates flexible decision-making by accommodating different levels of confidence and uncertainty, making it particularly suitable for forensic applications where evidence is often ambiguous. The findings demonstrate that integrating q-rung orthopair fuzzy numbers into the MCDM framework significantly enhances the accuracy and reliability of identifying vehicle components involved in accidents. The proposed methodology provides a systematic tool to support forensic investigations, aiding in the determination of liability and contributing to more robust outcomes in mechanical evidence analysis.

Evaluating municipal solid waste management with a confidence level-based decision-making approach in q-rung orthopair picture fuzzy environment

Evaluating municipal solid waste management with a confidence level-based decision-making approach in q-rung orthopair picture fuzzy environment

Decision Analysis Algorithm Using Frank Aggregation in the SWARA Framework with p,qRung Orthopair Fuzzy Information

The present study introduces an innovative approach to multi-criteria decision making (MCDM) aimed at handling decision analysis involving p,qrung orthopair fuzzy (p,qROF) data, where the criteria weights are completely unknown. To achieve this objective, we formulate generalized operational rules referred to as Frank operational rules, tailored for p,qROF numbers (p,qROFNs) utilizing the Frank t-norm and t-conorm. With these newly devised operations as a foundation, we create a variety of p,qROF aggregation operators (AOs) to effectively aggregate p,qROF information. Furthermore, we examine specific instances of these operators and rigorously establish their desirable properties, including idempotency, monotonicity, boundedness, and symmetry. Subsequently, we adapt the SWARA technique to the realm of p,qROF fuzzy data and this adapted technique becomes instrumental in determining criteria weights within the proposed MCDM framework centered around proposed AOs. We furnish a descriptive example to exemplify the practicality of the developed approach. Lastly, the effectiveness and soundness of our approach are underscored through both parameter analysis and a comparative evaluation.

A novel LqROPF-Entropy-WASPAS group model based on Frank aggregation operators and improved score function in linguistic q-rung orthopair fuzzy framework

Decision analysis plays a crucial role in our everyday actions. Efficient decision-making models rely heavily on accurately representing human cognitive knowledge. The linguistic q-rung orthopair fuzzy sets (LqROPFSs) offer a versatile means of representing qualitative cognitive information by adapting the parameter q to different scenarios. This study presents a novel scoring function to rank linguistic q-rung orthopair fuzzy numbers (LqROPFNs) with greater precision compared to the current score function. Next, we present novel arithmetic/geometric aggregation operators (AOs) that utilize new Frank operational rules to combine a finite collection of LqROPFNs. The work also examines the several desirable characteristics and special cases of the provided AOs. Furthermore, a novel decision-making model called the LqROPF-Entropy-WASPAS model has been introduced to address the challenges of multiple attribute group decision-making (MAGDM) problems in a linguistic q-rung orthopair fuzzy environment. The model incorporates proposed AOs and a scoring function. The suggested methodology is exemplified by considering a practical decision to select an online teaching platform. The validity of the results is confirmed through an extensive sensitivity analysis and comparative investigation employing various existing MAGDM approaches within the linguistic q-rung orthopair fuzzy framework. The proposed approach offers enhanced flexibility to decision experts, empowering them to analyze decision outcomes across diverse scenarios. This flexibility is achieved by allowing the manipulation of values associated with various parameters, enabling decision-makers to tailor the analysis according to their specific attitudes and requirements. This adaptability ensures a more advanced and personalized analysis of decision outcomes, accommodating decision experts’ distinct viewpoints and preferences in varying situations.

Smart supply chain collaboration maturity evaluation model based on a q-Rung orthopair fuzzy decision making methodology

In today’s IT-intensive business context, evaluating supply chain (SC) collaboration maturity (CM) is crucial for guiding enterprises toward integrated processes that collaborate across enterprise boundaries. This study develops an SC CM evaluation model by integrating a conceptual maturity model with a q-Rung Orthopair Fuzzy Set (q-ROFs)-based decision-making methodology, including a new scoring method. The major aim of this study is to facilitate a qualitative multi-criteria evaluation of SC CM. The conceptual model develops criteria sets based on smart technologies (such as sensors, robotics, cloud, ERP) and business processes. The proposed methodology is applied to 10 generic case companies, demonstrating its ability to provide multi-criteria scorecards and comparative rankings within each maturity level. The results show that the suggested approach can be used to guide the future efforts of individual enterprises, serving for benchmarking purposes across multiple companies by providing an overall objective evaluation for managers and policymakers. The proposed methodology is original by: (a) conceptual model development, (b) developing a multi-criteria quantitative decision-making approach with a new scoring method and integrating it with unique process-based and IT-based criteria sets of the conceptual maturity model, and (c) facilitating managerial scorecards and evaluations.