Q-rung Orthopair Research Articles

A novel muirhead mean operators based decision algorithm with applications in the orientation of basketball players using circular q-rung orthopair fuzzy information

Modern basketball players depend on decision-making techniques to enhance their position orientation and performance results. The way a player positions, together with their effectiveness and gameplay strategy, changes based on what they have learned and the amount of experience they have gained. A new intelligent decision-making model has been created to evaluate basketball player orientation while enhancing their performance through assessments. The proposed evaluation method uses an advanced decision algorithm based on circular q-rung orthopair fuzzy sets (Cq-ROFS), representing an evolved extension of fuzzy set theory to handle uncertain and complex data from player evaluation. This study introduced four innovative aggregation operators, which include circular q-rung orthopair fuzzy muirhead mean (Cq-ROFMM), circular q-rung orthopair fuzzy dual muirhead mean (Cq-ROFDMM), circular q-rung orthopair fuzzy weighted muirhead mean (Cq-ROFWMM) and circular q-rung orthopair fuzzy dual weighted muirhead mean (Cq-ROFDWMM). The decision-making accuracy improves when these operators maintain criterion relationships because they produce unbiased and dependable evaluations of player abilities and expertise. The development includes constructing key theoretical properties while creating a reliable multi-criteria decision-making (MCDM) algorithm that enables efficient computations. A case study of basketball player orientation implements the proposed methodology to prove its practical success and demonstrates its ability to handle uncertainties. The proposed method outperforms existing techniques according to sensitivity and comparative analysis tests because it demonstrates robustness, flexibility, and enhanced decision-making precision. The proposed model demonstrates emerging value as an intelligent sports analytics tool and a broader MCDM applications solution.

Knee injury prevention via personalized exercise using EDAS method and Sugeno Weber operator under complex q rung orthopair fuzzy data

Knee injuries are common in several people, frequently controlling for significant injuries and health care costs. This article explains the role of personalized exercise prescriptions in preventing knee injuries. For this purpose, we used the multicriteria decision-making (MCDM) technique to select the best alternative, including criteria such as level of muscle strength improvement, cardiovascular endurance, recovery time, and improvement in flexibility and range of motion. The complex q-rung orthopair fuzzy set (C-qROFS) is a prevailing tool for managing ambiguity by combining satisfactory, dissatisfactory, and complex phase data. It is the extent of fuzzy theories that characterize directional and magnitude-based uncertainties, allowing more significant decision-making. In existing research work, C-qROFS was defined with different aggregation operators. However, no work is available on combined Segeno Weber aggregation operator (AOs) and EDAS in the framework of C-qROFS. We propose some notion AOs such as C-qROF Sugeno Weber weighted averaging (C-qROFSWWA) and C-qROF Sugeno Weber weighted geometric (C-qROFSWWG) as essential properties. We have also proposed the EDAS technique for C-qROFS. The EDAS technique for C-qROFS certifies efficient decision-making by exploiting C-qROF information to assess alternatives based on proximity to an ideal solution. A real-life example is proposed for selecting the best-personalized exercise using our suggested aggregation operator. We take four alternatives after finding that rank strength training focused on quadriceps and hamstrings is the best alternative for preventing knee injuries. To check the superiority and validity of the suggested technique, a deep comparative study with the existing aggregation operator must be conducted.

A consensus-based group decision-making method for multidisciplinary team meeting under q-rung orthopair fuzzy environment

A consensus-based group decision-making method for multidisciplinary team meeting under q-rung orthopair fuzzy environment

A three-way decision model oriented to the twin fuzzy concepts of q-rung orthopair

A three-way decision model oriented to the twin fuzzy concepts of q-rung orthopair

Evaluation of unmanned combat aerial vehicles using q-rung orthopair fuzzy entropy based multi-attribute border approximation area comparison method

Unmanned combat aerial vehicles (UCAVs) have become an indispensable part of modern military operations. As they can be used for both defensive and offensive purposes, they play a crucial role in shaping military strategies and improving the operational capabilities of security forces. Many countries are investing in UCAV technology and placing these vehicles at the centre of their defence strategies. Choosing the right UCAV enables a country to strengthen its national security and its position in international relations by enhancing its defence capabilities. This study considers the evaluation of UCAVs as a multi-criteria decision making (MCDM) problem. In the study, the q-ROF (q-rung orthopair fuzzy) entropy-based MABAC (Multi-Attribute Border Approximation Area Comparison) method is proposed as a new integrated MCDM technique to solve the problem. The theoretical framework of the proposed method is explained in detail and applied to an UCAV selection problem. In practise, fourteen different UCAV alternatives were evaluated based on nine criteria (length, wingspan, height, empty weight, maximum takeoff weight, payload capacity, maximum cruising speed, maximum altitude, duration in the air). As a result of the application, the best-performing alternative was identified as UCAV-9 (A9). In addition, the results of the proposed method were compared with the results of the classical q-ROF MABAC, q-ROF MAIRCA (Multi Attributive Ideal-Real Comparative Analysis), q-ROF TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), q-ROF CRITIC-EDAS (Criteria Importance through Inter-criteria Correlation-Evaluation based on Distance from Average Solution), and q-ROF BWM-MARCOS (Best–Worst-Method-Measurement of Alternatives and Ranking according to COmpromise Solution) methods. UCAV-9 (A9) emerged as the strongest alternative based on the comparison analysis. In addition, two different sensitivity analyses were also carried out. The sensitivity analysis on the criteria weights revealed that the alternatives were highly influenced by these weights. Based on these results, it can be concluded that this study offers a practical framework for countries to select the appropriate UCAV and makes a significant contribution to the literature in this field.

Dynamic Aggregation Operators for Selection of Optimal Communication System in the Rescue Department Under Complex q-rung Orthopair Fuzzy Environment

Selecting the advanced communication system for the rescue department is pivotal for enhancing coordination, operations, and effectiveness in today’s dynamic environment. Addressing the complexities arising from uncertainty and periodicity, the Complex q-rung Orthopair Fuzzy Set theory emerges as adept, encapsulating comprehensive problem specifications. This study introduces two innovative aggregation operators within the Cq-ROFS framework: the Complex q-rung Orthopair Fuzzy Dynamic Weighted Averaging (Cq-ROFDWA) and the Complex q-rung Orthopair Fuzzy Dynamic Weighted Geometric (Cq-ROFDWG) operators. Some important characteristics of the newly defined operators are established. Moreover, these operators contribute to a systematic framework for handling Multiple Attribute Decision Making (MADM) problems involving complex q-rung Orthopair fuzzy information. The article exemplifies their application in resolving a MADM problem, determining the optimal option of an advanced communication system. Finally, to validate the derived methodologies, a thorough comparison study is carried out, demonstrating the superiority of the presented operators against various existing operators.

Integrated Fermatean Fuzzy SWARA and Q-ROF-EDAS Methodology for Supplier Evaluation in the Shipyard Industry

In recent years, increasing complexity in supply chains and the presence of high cost and risk factors in project-based industries such as shipbuilding have made supplier selection a critical decision-making problem. In this context, this study evaluates the criteria that shipyards should consider in supplier selection using an integrated multi-criteria decision-making (MCDM) approach based on Fermatean Fuzzy SWARA (FF-SWARA) and q-Rung Orthopair Fuzzy Set-based EDAS (q-ROF EDAS) methods. In the first stage of the study, the importance weights of twelve supplier selection criteria—gathered under two main categories based on expert opinions and literature review—were determined using the FF-SWARA method. In the second stage, supplier alternatives were ranked using the q-ROF EDAS method. A sensitivity analysis was also conducted in the study, and rankings generated under 100 different scenarios were evaluated. The results obtained demonstrate the practical applicability of the proposed method and its capability to address uncertainty in the decision-making process, contributing to more consistent and informed decisions in the shipyard and shipbuilding sectors.

Advanced Decision-Making With Complex q-Rung Orthopair Fuzzy Muirhead Mean Models

Reliable and adaptable approaches are essential in MADM to address the complexities of medical diagnosis. To enhance decision-making (DM) processes, we propose a novel method combining the Muirhead mean (MM) with q-rung orthopair fuzzy sets (q-ROFS). The q-ROFS framework offers an advanced solution for handling ambiguity and vagueness in medical diagnoses by enabling a three-dimensional representation of expert opinions through membership degrees (MDs). The proposed method leverages MM's strength in capturing interrelationships among features, ensuring a more accurate and balanced integration of expert inputs. This integration is particularly valuable in medical diagnosis, where the interplay and relative importance of symptoms and diagnostic criteria are complex and critical. By expanding the theoretical applications of fuzzy sets (FS) in MADM, this innovative approach not only improves patient outcomes but also enhances the reliability of diagnostic procedures. It provides a practical tool for elevating DM quality in medical settings.

Sustainable Urban Development: q-Rung Orthopair Fuzzy MCDM withGeneralized Power Prioritized Yager Aggregation

In light of accelerating urbanization and the intensifying challenges of climate change, ensuring sustainability and resilience in urban environments has become a strategic imperative. This study introduces an innovative methodology that combines Multi-Criteria Decision Making (MCDM) with q-rung orthopair fuzzy Yager aggregation operators (q-ROFYAOs) to address the multifaceted complexities of sustainable urban development. The paper proposes novel Yager operations grounded in Yager t-norms within the framework of q-rung orthopair fuzzy sets (q-ROFSs). Utilizing these foundations, two advanced aggregation operators are formulated: the q-Rung Orthopair Fuzzy Generalized Power Prioritized Yager Weighted Average Operator (q-ROFGPOPRYWA) and the q-Rung Orthopair Fuzzy Generalized Power Prioritized Yager Weighted Geometric Operator (q-ROFGPOPRYWG). These operators satisfy essential mathematical properties, including idempotency, monotonicity, and boundedness, ensuring consistency and reliability in decision-making contexts. To demonstrate the practical relevance of the proposed approach, the operators are embedded within an MCDM framework and applied to a real-world case study on sustainable urban planning. The analysis encompasses sensitivity testing, comparative evaluations, and performance assessments, offering comprehensive insights into the robustness of the method. Finally, the paper provides a critical evaluation of the advantages and limitations of the proposed operators, underscoring their effectiveness in promoting urban resilience and minimizing environmental impact within complex decision-making environments.

Enhanced decision-making for urban climate change transportation policies using q-rung orthopair fuzzy rough fairly information aggregation

Enhanced decision-making for urban climate change transportation policies using q-rung orthopair fuzzy rough fairly information aggregation

A Neutrosophic and Q-Rung Orthopair Fuzzy Sets Approach for Desertification Susceptibility Mapping: A Case Study in Matrouh, Egypt

A Neutrosophic and Q-Rung Orthopair Fuzzy Sets Approach for Desertification Susceptibility Mapping: A Case Study in Matrouh, Egypt

Consistency-based decision-making method with linguistic Q-rung orthopair fuzzy preference relation for power battery selection of new energy vehicles

Consistency-based decision-making method with linguistic Q-rung orthopair fuzzy preference relation for power battery selection of new energy vehicles

Offshore Wind Power Site Selection in Türkiye Using q-Rung Orthopair Fuzzy Sets and the COPRAS Method

Wind energy has significant potential for electricity/energy generation. At the same time, Türkiye is among the important markets in wind energy production worldwide. With the increasing energy demand and sustainability goals, it can be said that energy production is gradually shifting towards offshore wind farms (OWFs). In this study, three alternative locations in the Aegean Sea region of Türkiye were evaluated to determine the most suitable site for OWF installation based on two main and twelve sub-criteria. The methodology applied in the study generally consists of four stages. In the first stage, a normalized weighted decision matrix was created using the steps of the q-rung Orthopair fuzzy set. In the second stage, the COPRAS method, one of the multi-criteria decision-making (MCDM) methods, was applied to rank the alternatives and the best alternative was determined to be Bozcaada offshore. In the third stage, a comparison analysis was performed using q-ROF TOPSIS and q-ROF WASPAS methods. As a result of the comparison analysis, it was determined that all three methods gave the same results. In the last stage, the sensitivity of all three MCDM methods was checked by changing the q levels. As a result of the sensitivity analysis, it was determined that the q-ROF COPRAS method was insensitive to changes in q levels, while the q-ROF TOPSIS and q-ROF WASPAS methods were sensitive because the alternative rankings changed as a result of these changes.

Supplier selection based on group decision making using q-rung orthopair fuzzy Aczel Alsina Hamy mean operators

In this article, we expose the theory of q-rung orthopair fuzzy (q-ROF) sets (q-ROFSs), which is the robust improvement of concepts of fuzzy sets (FSs) and intuitionistic FSs. The q-ROFS is an advanced framework that permits decision-makers to evaluate complex and unpredictable information during the decision-making process. The Hamy mean (HM) models are more powerful and effective aggregation models used to reduce the impact of different attributes and express correlation among different objects. We discussed the basic operations of Aczel Alsina operations under consideration of q-ROF environments. Some new strategies proposed by exploring the theory of Aczel Alsina aggregation expressions based on HM models, such as q-ROF Aczel Alsina Hamy mean (q-ROFAAHM) and q-ROF Aczel Alsina weighted Hamy mean (q-ROFAAWHM) operators. We also present a list of new approaches under consideration of the Dual Hamy mean (DHM) model, such as q-ROF Aczel Alsina Dual Hamy mean (q-ROFAADHM) and q-ROF Aczel Alsina weighted Dual Hamy mean (q-ROFAAWDHM) operators. Some flexible and reliable properties of our derived approaches are also discussed. A multi-attribute group decision-making (MAGDM) technique is a relatively advanced decision-making approach which is utilized to evaluate reliable optimal options by the decision maker. An appropriate algorithm for a MAGDM problem is also presented to reveal the robustness of our derived approaches. To show the flexibility and consistency of our discussed approaches, we study a practical example to choose the best option. To show the applicability and feasibility of currently discussed methodologies, we contrast the results of previously proposed aggregation operators (AOs) with the results of new methodologies.

Enhancing Web Security Through Complex Cubic q-Rung Orthopair Fuzzy Information

Over the past two decades, the evolution of web services has profoundly transformed communication and information technologies. As computer programs that facilitate data exchange and interoperability between applications via the Internet, web services play a pivotal role in enabling seamless interactions and information access. However, despite their advantages, these platforms also introduce significant security risks. This study leverages the novel framework of Complex Cubic q-Rung Orthopair Fuzzy Sets (CCuqROFS) to model and address these security threats effectively. Unlike conventional models, CCuqROFS provides a robust structure that incorporates membership (M) and non-membership (NM) degrees, offering a more precise representation of uncertainty. Through illustrative examples, we introduce key concepts such as Complex Cubic q-Rung Orthopair Fuzzy Relations (CCuqROFR), the Cartesian product of CCuqROFSs, and various forms of CCuqROFRs. For the first time in fuzzy set theory, this work systematically examines the relationships between different security threats and countermeasures in web services. The proposed methodologies demonstrate how robust security measures can mitigate the impact of these threats. Finally, a comparative analysis underscores the advantages of our strategies, providing valuable insights for enhancing web service security.

Algorithm for investigating risk analysis and factors affecting suicidal attempts under uncertainty

Many people die from suicide, and it is a significant challenge in most societies, which calls for improved assessment procedures. This work presents a risk assessment model and outlines the risk factors for suicidal attempts under conditions of risk uncertainty. This algorithm assesses risk factors entirely using an interval-valued q-rung orthopair fuzzy (ivq-ROF) set information based Sugeno–Weber aggregation operators and EDAS method. Second, it applies Positive Distance from the Average (PDA) and Negative Distance from the Average (NDA) to balance an assessment, normalize various criteria, and rank them into higher order. We proposed ivq-ROF Sugeno–Weber weighted averaging (ivq-ROFSWWA), ivq-ROFS weighted geometric (ivq-ROFSWG) operators and EDAS method for improving the process of aggregation of fuzzy information. In the final type of stage, add up the PDA and NDA scores to determine the critical risk factors. This approach also increases the accuracy of predicting suicide risk, which is a vital asset for mental health researchers and practitioners to build effective intervention and prevention initiatives. Also, the nature of the algorithm renders decisions on compound data interfaces beneficial to numerous public health situations. Its application may include understanding factors that should inform policies that touch on mental health services and enhance the utilization of scarce resources in meeting the growing demand for such services. In conclusion, this study aspires to avoid future suicides due to a solid analytical framework for the research problem.

Research on Civil Aviation Airport Site Selection Considering Group Consensus Level Under Large-Scale Uncertain Information

In response to the decision-making challenges posed by large-scale information and complex data sources in civil aviation airport site selection, this paper proposes a novel method that integrates group consensus within a framework of substantial uncertainty. The method comprises five key processes: (1) Evaluation process: Based on the constructed multicriteria evaluation system for airport site selection, the q-Rung Orthopair Fuzzy (q-ROF) information is employed to represent evaluations from large-scale decision makers, which effectively characterizes the uncertainty of information and broadens the evaluative scope. (2) Clustering process: A clustering procedure is designed for large-scale q-ROF evaluation data and weight information of criteria, identifying and removing outliers. (3) Consensus reaching process: Considering the characteristics of q-ROF evaluations and multiplicative preference relations, two adaptive consensus reaching algorithms are developed to enhance group consensus levels, thereby improving the rationality of decision-making results. (4) Weight determination process: Criteria and subcriteria weights are calculated using multiplicative preference weighting approach and a deviation maximization model, respectively, derived from aggregated group evaluations. (5) Ranking process: The q-ROF Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is applied, in conjunction with the induced q-ROF information integration paradigm, to comprehensively rank the alternative sites. Finally, the feasibility and effectiveness of the proposed method are demonstrated through a case study of civil aviation branch airport planning in a specific city.

Novel q-Rung Orthopair Fuzzy distance based similarity measure and score function in real life decision making

Novel q-Rung Orthopair Fuzzy distance based similarity measure and score function in real life decision making

Optimizing performance evaluation of green supply chain management practices with linguistic q-rung orthopair fuzzy information

Optimizing performance evaluation of green supply chain management practices with linguistic q-rung orthopair fuzzy information

Dynamic q-rung orthopair hesitant fuzzy decision-making model based on Banzhaf value of fuzzy measure

Dynamic q-rung orthopair hesitant fuzzy decision-making model based on Banzhaf value of fuzzy measure