Q-rung Orthopair Fuzzy Research Articles

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

In the relevant literature, there is no study dealing with the financial credibility of third-party logistic providers with the help of decision-making frames. Further, there are no criteria to evaluate the third-party logistics providers' creditworthiness in practice, and decision-makers in the banks consider their judgments and experiences to assess the demand of the logistics firms. This study proposes a multi-criteria group decision-making framework through a dual hesitant linguistic q-rung orthopair fuzzy (DHLq-ROF) set to manage uncertainties more effectively and make a theoretical contribution to the academic literature. For ranking, the score function and accuracy function are defined. Additionally, some novel operational laws based on Frank t-norms and t-conorms are defined for DHLq-ROF numbers. A wide range of generalized aggregation operators, such as DHLq-ROF Frank weighted averaging, DHLq-ROF Frank weighted geometric, DHLq-ROF Frank generalized weighted averaging, and DHLq-ROF Frank generalized weighted geometric operators, are also investigated. Beyond that, several prominent characteristics of the proposed operators are studied. It is applied to a financial credibility problem for a multinational organization to demonstrate the introduced model's applicability. Considering the results obtained regarding the importance of the criteria, the most crucial criterion is market indebtedness, followed by fleet vehicle structure and current rate criteria, respectively. The results indicate that UPS, Kuhne & Nagel and DHL Deutsche Post are the best third-party logistic providers. The sensitivity analysis shows that the framework possesses favourable flexibility and effectiveness. Thanks to the framework's ability to produce practical solutions to challenging decision-making problems, it can be reliably preferred in engineering and other fields.

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.

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.

Selecting the most successfull recycling strategy over daily consumption products: application of q-Rung Orthopair Fuzzy Topsis method

Recycling is the process of collecting and reusing that helps the countries to achieve their sustainable development goals. This study, for the first time in the literature, considers the recycling of many daily consumption products as a decision-making problem with the q-rung orthopair fuzzy (q-ROF) approach. In Turkey, recycling initiatives are primarily led by the government and municipalities, involving either reprocessing in public facilities or collaboration with private enterprises. The research evaluates the effectiveness of recycling strategies, considering paper, plastic, textiles, batteries, frying oils, electronics, glass, and wood as alternative products. Criteria such as convertibility rate, resource usage for recycling, converted product lifespan, recycling process complexity, economic gain, product consumption rate, and trading opportunities are employed in the decision-making process. The q-rung orthopair fuzzy Technique for Order Preference by Similarity to Ideal Solution (q-ROFTOPSIS) method is applied to assess these criteria. Decision makers, comprising a recycling expert, a recycling business engineer, and an academician specializing in recycling studies, contribute to the evaluation. The study reveals electronic products as the most successful in recycling, while frying oils exhibit the least success.

Neutrality geometric aggregation operators based on cubic q-rung orthopair fuzzy sets and their applications in multiple attribute group decision-making problems

Cubic q-rung orthopair fuzzy set (Cq-ROFS) is a hybrid set that can hold much more information to handle data uncertainties. It provides more space for decision makers (DMs) to describe their opinion in the multi-attribute group decision-making (MAGDM). In order to involve neutral character to discuss the neutral values assigned by decision-makers, in this paper, some cubic q-rung orthopair fuzzy aggregation operators based on neutrality operation are proposed, such as the cubic q-rung orthopair fuzzy weighted neutrality geometric (Cq-ROFWNG) operator, the cubic q-rung orthopair fuzzy ordered weighted neutrality geometric (Cq-ROFOWNG) operator, the cubic q-rung orthopair fuzzy hybrid neutrality geometric (Cq-ROFHNG) operator. Meanwhile, some properties of these operators are discussed. Further, we develop a novel MAGDM approach based on the proposed operators in a cubic q-rung orthopair fuzzy environment. Finally, numerical examples are provided to demonstrate the effectiveness and superiority of the proposed method through a detailed comparison with existing methods.

Risk evaluation of livestream e-commerce platforms based on expert trust networks and CODAS

As an emerging e-commerce model, livestream e-commerce (LEC) has presented novel opportunities for the advancement of the retail industry. While retailers reap the benefits by this platform, they also encounter challenges associated with platform risks. This paper aims to propose a novel hybrid model to address the risks inherent in livestream e-commerce platforms (LECPs) evaluation problems. Firstly, the paper makes use of q-Rung orthopair fuzzy (q-ROF) trust network (TN) to handle the intricate and uncertain evaluation environment of LECPs and utilizes combinative distance-based assessment (CODAS) to rank the alternative platforms. Secondly, considering that experts have different levels of familiarity with different indicator, a q-ROF-TN matrix between experts is established for each indicator to ascertain their respective weights, which adds robustness to the evaluation process. Thirdly, the dual feedback mechanism is designed to enhance group consensus that attempts to ensure that the evaluation process is collaborative and considers diverse expert opinions, leading to more reliable outcomes. Finally, a set of evaluation indicators is constructed covering various aspects like capital flow management capability and public domain traffic support, and the level of risk of the four LECPs is evaluated using the proposed method. Furthermore, the reliability and effectiveness of the proposed evaluation model are validated through sensitivity analysis and comparative study.

Enhanced EDAS methodology for multiple-criteria group decision analysis utilizing linguistic q-rung orthopair fuzzy hamacher aggregation operators

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Associated Probabilities’ Aggregations in Fuzzy Collaborative Filtering Recommender System Prediction

AbstractIn this article, the expert evaluations transformed in the multi-attribute decision-making (MADM) model are presented in the discrimination q-rung picture linguistic fuzzy numbers (q-RPLFNs). In the construction of a second-order additive fuzzy measure (TOAFM) the attributes’ interaction indexes and Shapley values are taken into account. The Shapley entropy maximum principle for identification of associated probabilities class (APC) of a TOAFM is constructed. Based on the APC of the TOAFM, a new aggregation operators’ class is constructed which represents some hybrid extensions of ordered weighted averaging (OWA), geometric (OWG), the Choquet integral averaging (CA) and geometric (CG) operators under discrimination q-rung orthopair fuzzy (q-ROF) and q-rung picture linguistic fuzzy (q-RPLF) information. These operators, constructed for the q-RPLF and q-ROF environments, take into account the overall pair interactions among attributes. Main properties on the correctness of extensions are proved: for the lower and upper capacities of order 2, all constructed operators consequently coincide with q-ROF and q-RPLF Choquet averaging and geometric operators, respectively. Constructed operators in the evaluation of prediction of fuzzy Collaborative Filtering Recommender Systems (CFRS) are used. New symmetric discrimination measures as some extensions of discrimination measures for the fuzzy CFRS are proposed. Users’ profile data by the constructed operators in the new similarity measure under q-rung picture linguistic environment are aggregated. The developed new approach is schematically described in such a way that it can be “embedded” in any existing CFRS model. An example is given to illustrate the results, for which the software designed to aggregate profile data for similarity comparison provides the use of new and well-known classical aggregation operators.

A novel failure mode and effect analysis model based on extended interval-valued q-rung orthopair fuzzy approach for risk analysis

The failure mode and effect analysis (FMEA) is an essential technique for risk assessment that can not only determine the potential failure modes, causes of failure and effects, but also expunge the potential failure modes in a system. Imprecise evaluation outcomes may be achieved due to the inherent imperfections caused by the traditional FMEA approach. Accordingly, a hybrid FMEA model based upon the interval-valued q-rung orthopair fuzzy (IVq-ROF) set and extended weighted aggregated sum product assessment (WASPAS) method is propounded to provide decision support for risk assessment. The main dedications of this investigation are as below. First, an innovative IVq-ROF score function is presented to compare the IVq-ROF numbers and the related properties of the proposed function are also discussed. Second, the IVq-ROF Aczel–Alsina operation laws are investigated and then some novel IVq-ROF Hamy mean operators are propounded on based on the advanced operations. Third, the IVq-ROF logarithmic percentage change-driven objective weighting (LOPCOW) method is advanced based on the presented score function to determine the objective importance of the risk criteria and the fuzzy full consistency method (FUCOM) is utilized to ascertain the subjective importance of the risk criteria. Fourth, a novel FMEA model based on extended WASPAS method is presented to ascertain the prioritization of failure modes. Lastly, an empirical about risk assessment in the shipbuilding process is implemented to verify the applicability and feasibility of the designed FMEA model. The comparison analysis and sensibility study are executed to demonstrate the significant merits and stability of the developed model. The best advantage of the presented model is that it not only obtains a consistent outcome and takes into account the interrelations among the risk criteria, but also addresses more problems where the weights of the criteria and experts are not determined in advance.

Interval-valued (p,q,r)-spherical fuzzy sets and their applications in MCGDM and MCDM based on TOPSIS method and aggregation operators

The (p,q,r)-Spherical fuzzy set is a useful generalization of other fuzzy structures such as fuzzy, intuitionistic fuzzy, Pythagorean fuzzy, picture fuzzy, q-rung orthopair fuzzy, spherical fuzzy, and T-spherical fuzzy sets. In this paper, we define concept of interval-valued (p,q,r)-spherical fuzzy set (IVpqr-SFS) and Set-theoretical and arithmetic operations of them. Two aggregation operators named interval-valued (p,q,r)-spherical fuzzy weighted arithmetic (IVpqr-SFWAA) and interval-valued (p,q,r)-spherical fuzzy weighted geometric (IVpqr-SFWGA) aggregation operators are introduced for IVpqr-SF numbers (IVpqr-SFN) and their properties were examined. The proposed operators are explained with examples. Furthermore, the score and accuracy functions of an IVpqr-SFN are introduced. We also define distance measures between two same types IVpqr-SFSs based on Hamming, Euclidean, and Hausdorff distance measures. Moreover, a multi-criteria group decision-making (MCGDM) method is developed based on the TOPSIS method by using the proposed distance measures and score function, and a numerical example is given to show the process of the proposed MCGDM method. In addition, a multi-criteria decision-making (MCDM) method is developed using the proposed IVpqr-SFWAA, IVpqr-SFWGA operators, and an illustrative example is given to show the process of the proposed MCDM method. Finally, a comparative analysis is presented between the proposed methods and the existing methods.

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

The healthcare industry faces numerous challenges in managing its supply chain efficiently, where critical decisions must be made promptly to ensure the availability of essential medical resources. This research introduces a novel artificial intelligence (AI) approach, utilizing the “Sugeno–Weber (SW) t-conorms and t-norms” (SWt-CNs&t-Ns) for decision-making in a Dual Hesitant q-Rung Orthopair Fuzzy (DHq-ROF) context. The SWt-CNs&t-Ns are chosen for their adaptability in data unification, serving as prominent operations for union and intersection processes. Developing a set of fundamental operations is imperative to effectively utilize SWt-CNs&t-Ns and hybrid aggregation operators in DHq-ROF settings. Following the introduction of these processes, several aggregating operators have been provided. These operators include DHq-ROF SW weighted averaging, ordered weighted averaging, hybrid averaging, and their geometric counterparts utilizing DHq-ROF data. The SW triangular norm-based approach aggregates group preferences, facilitating a systematic decision-making process. Triangular norms ensure a realistic representation of interrelationships among decision criteria, leading to optimal healthcare supply chain management solutions. Furthermore, the SW triangular norm-based approach aggregates group preferences, enabling a systematic and comprehensive decision-making process. Choosing the best healthcare supply chain management solutions is easier when you use triangular norms because they give a more accurate picture of how the decision criteria affect each other. The effectiveness of the proposed AI framework is demonstrated through a series of experiments and case studies, showcasing its ability to enhance decision accuracy, reduce uncertainty, and improve overall supply chain performance. The research findings underscore the potential of AI-driven solutions to revolutionize healthcare supply chain management, ultimately leading to better resource allocation, cost efficiency, and improved patient care.

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

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

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.

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.

The application of cosine similarity measures with Laplacian energy to q-rung orthopair fuzzy graphs in decision-making problems

Q-rung orthopair fuzzy sets (q-ROFS), which are better than the intuitionistic and Pythagorean fuzzy sets, are a significant tool for expressing ambiguous information. Their key feature is that their ability to represent a larger space of uncertain information is based on the fact that the product of the qth power of the membership degree and the qth power of the degrees of non-membership is equal to or less than 1. Under these circumstances, we train group decision-making problems in the study using q-rung orthopair fuzzy inclination associations. Through the calculation of the standard deviation of one separable q-rung orthopair inclination association to the others and the unclear evidence of q-rung orthopair fuzzy inclination connections, we propose a novel approach to estimate the qualified reputation weights of authority. The “internal” and “impartial” evidence of authority is taken into consideration by this new mindset. Subsequently, we included the weights of authorities into the q-rung orthopair fuzzy inclination relations and used a relative similarity approach to determine the relevance of replacements and the best substitutes. The planned techniques' usefulness and realism are demonstrated by the contrast analysis with additional methods through mathematical demonstrations, both of which show the fuzzy set’s membership degree and non-membership degree, respectively.

Evaluation and Prioritization of Sustainable Enterprise Resource Planning in SMEs Using q-Rung Orthopair Fuzzy Rough Set-Based Decision Support Model

Evaluation and Prioritization of Sustainable Enterprise Resource Planning in SMEs Using q-Rung Orthopair Fuzzy Rough Set-Based Decision Support Model

An integrated multiple-criteria decision-making and data envelopment analysis framework for efficiency assessment in sustainable healthcare systems

Efficiency is critical in allocating sustainable healthcare resources to ensure that hospitals can effectively care for patients while maintaining high-quality care delivery. Hence, it is necessary to monitor efficiency carefully. This study aims to assess hospital unit effectiveness through a novel comprehensive approach integrating Multiple-Criteria Decision Making (MCDM) with Data Envelopment Analysis (DEA). The proposed MCDM-DEA framework involves allocating varying weights to distinct data categories. It harnesses the capabilities of the q-rung orthopair fuzzy (q-ROF) methodology to address the inherent uncertainties in healthcare performance assessment. The experimental results provide a comprehensively structured ranking system for specific hospital departments. This ranking system allows decision-makers to identify the strengths and weaknesses of each department, enabling them to make informed decisions regarding resource allocation and improvement strategies. Furthermore, the integration of MCDM-DEA provides a robust and objective assessment tool for monitoring and evaluating the performance of hospital departments over time. These rankings offer invaluable insights to decision-makers, equipping them with the strategic information needed to enhance the overall performance of hospital units.

Linguistic q-rung orthopair fuzzy Z-number and its application in multi-criteria decision-making

This paper proposes a new linguistic model called Linguistic q-Rung Orthopair fuzzy Z-number (LqROFZN), which combines the advantages of linguistic variables, Z-number and q-Rung Orthopair Fuzzy numbers. It can be used as a powerful tool for uncertain decision-making, which can effectively improve the accuracy and reliability of the decision-making results, and has a notable application prospect for the fields of information decision-making, risk assessment, diagnosis and so on. In this paper, firstly, the definition of LqROFZN and its operational rules are given, a new distance measure and the concept of entropy are given under LqROFZN, and the entropy of LqROFZN can assess the credibility situation of LqROFZN. Next, two aggregation operators under LqROFZN are given, namely the Linguistic q-Rung Orthopair fuzzy Z-number weighted aggregation (LqROFZWA) operator and the Linguistic q-Rung Orthopair fuzzy Z-number weighted Geometric aggregation (LqROFZWGA) operator. Finally, the MCDM method under LqROFZN is given and the credibility of the evaluation results is assessed using the entropy of LqROFZN. In a set of actual airline aircraft selection cases, the feasibility and advantages of the proposed method are verified through comparative analysis with other methods.

Improved q-Rung Orthopair Fuzzy WASPAS Method Based on Softmax Function and Frank operations for Investment Decision of Community Group-Buying Platform

The traditional WASPAS (Weighted Aggregated Sum Product ASsessment) method has attracted widely attention. Unfortunately, the decision-makers’ decision attitude or risk preference is ignored in the existing WASPAS methods. To overcome this shortcoming, this paper embedded expert’s decision attitude with risk preference into WASPAS method for solving multi-attribute group decision-making problems with q-rung orthopair fuzzy (q-ROF) information. We develop the q-ROF Frank softmax weighted averaging (q-ROFFSWA) and q-ROF Frank softmax weighted geometric (q-ROFFSWG) operators based on Frank operations and softmax function. The relevant properties and particular cases are explored, and the monotonicity of these operators’ score functions is analyzed. Then, the q-ROF multi-attribute group decision-making framework based on the improved WASPAS method is constructed. The weighted sum model and weighted product model in traditional WASPAS are replaced by the two proposed aggregation operators. The q-ROF distance measure is utilized to de fuzzy the performance values of alternatives. And a compromise function between optimistic and pessimistic decision attitudes with risk preferences is proposed. Lastly, the presented method is implemented in a real case of investment decision of community group-buying (CGB) platform, and sensitivity analysis and comparative study with existing methods are conducted to verify the practicality, robustness and effectiveness.