Q-rung Orthopair Fuzzy Research Articles

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.

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.

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

Safety assessment of intelligent vehicles considering drivers’ risk perception information under interval 2-tuple q-rung Orthopair Fuzzy Sets

Safety assessment of intelligent vehicles considering drivers’ risk perception information under interval 2-tuple q-rung Orthopair Fuzzy Sets

Harnessing High-Altitude Advantages: Sustainable Data Center Site Selection in Ski Resort Regions for Optimized Energy Efficiency

Data centers are a part of the digital economy, but they are great contributors to energy use and carbon emissions. The site selection for data centers can, therefore, be an important tool in the optimization of energy efficiency and sustainability, especially in those areas where climatic and infrastructural factors can keep environmental impacts as low as possible. This study will evaluate seven possible sites for a data center in Turkey, focusing on high-altitude ski resorts with natural cooling advantages. In the paper, the research uses q-Rung Orthopair Fuzzy Numbers (q-ROFs) in combination with methods like Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), taking environmental, technical, and socio-economic criteria into consideration, such as energy efficiency, disaster risk, infrastructure quality, and accessibility. To strengthen result reliability, a sensitivity analysis by varying the q parameter and a comprehensive study by performing different multi-criteria decision-making (MCDM) methods were employed. Analysis revealed that Erciyes and Uludağ consistently ranked among the top alternatives across all methods and q values, indicating their robustness. The results confirm the effectiveness of the proposed methodology in supporting sustainable and high-altitude-aware site selection decisions. This research presents a holistic and Sustainable Site Selection framework for data centers; therefore, it provides actionable insights into stakeholders who balance operational efficiency with environmental responsibility. Future studies should subsequently delve into dynamic modeling and global applications to enhance the versatility of the framework.

A q-Rung Orthopair Fuzzy Three-Way Decision Method Under Sequence Coverage and Its Application to Air Quality Evaluation

A q-Rung Orthopair Fuzzy Three-Way Decision Method Under Sequence Coverage and Its Application to Air Quality Evaluation

Supplier selection using the q-rung orthopair fuzzy based MAIRCA method

The selection of suppliers is one of the most important strategic decisions that companies have to make in the context of supply chain management. While traditional methods cannot fully reflect the complexity of this process, fuzzy logic and multi-criteria decision-making (MCDM) deal more effectively with uncertainties and subjective evaluations. In this study, the q-rung orthopair fuzzy (q-ROF) based multi-attribute ideal-real comparative analysis (MAIRCA) method is proposed for supplier selection in an animal products company. To the best of our knowledge, this study represents the first application of the q-ROF MAIRCA method in the context of supplier selection. Also, this study demonstrates the ability of the q-ROF MAIRCA method to effectively manage uncertainty and subjectivity in decision-making processes. By incorporating the diverse opinions and preferences of decision-makers and systematically evaluating six suppliers based on criteria such as quality, product variety, price, and delivery time, the proposed method ensures robust and consistent results. The findings highlight the suitability of the q-ROF MAIRCA method for addressing complex supplier selection problems and its potential applicability in other industries with varying criteria.

Data-driven decision-making for enhancing aerobic and anaerobic performance in runners

The ability to undertake aerobic and anaerobic activities is critical for runners since these activities have an impact on endurance, speed, and overall performance. The capacity for aerobic exercise allows for prolonged effort throughout long runs, whereas the power of anaerobic exercise allows for rapid bursts of speed and efficient recovery. This article presents a data-driven decision-making technique that incorporates a cubic set with a q-rung Orthopair fuzzy set called cubic q-rung Orthopair fuzzy set (Cq-ROFS) to investigate the aerobic and anaerobic performance of runners. An introduction to Cq-ROFS is provided first, followed by its definition. Secondly, we suggest the Cq-ROF weighted average operator as a means of efficiently aggregating Cq-ROF information. Third, in order to determine the weights of attributes, a Cq-ROF-criterion impact loss method is built. The Cq-ROF-weighted aggregated sum product assessment method is then developed for the purpose of measuring runners’ aerobic and anaerobic performance. In the end, we use comparative analysis to show how our proposed strategy is superior to others.

Q-Rung Orthopair Fuzzy Sets-Enhanced FMEA for COVID-19 Risk Assessment

Q-Rung Orthopair Fuzzy Sets-Enhanced FMEA for COVID-19 Risk Assessment

Three-way Decision Approach Based on Utility and Dynamic Localization Transformational Procedures within a Circular q-Rung Orthopair Fuzzy Set for Ranking and Grading Large Language Models

Three-way Decision Approach Based on Utility and Dynamic Localization Transformational Procedures within a Circular q-Rung Orthopair Fuzzy Set for Ranking and Grading Large Language Models

Some New Techniques of Computing Correlation Coefficient between q-Rung Orthopair Fuzzy Sets and their Applications in Multi-Criteria Decision-Making

The term q-rung orthopair fuzzy set is an essential variant of fuzzy set with the capacity of tackling fuzziness and imprecision in the decision-making process. A fundamental concept in the decision-making process is the idea of correlation coefficient because of its wide applications. The process of decision-making is complex due to imprecisions, and as such the idea of correlation coefficient has been investigated under q-rung orthopair fuzzy setting. Some authors have constructed some techniques of correlation coefficient under q-rung orthopair fuzzy sets with practical applications. However, these existing techniques are defectives with several drawbacks in terms of precision and alignment with the conditions of correlation coefficient. In this work, two new techniques for estimating correlation coefficient under q-rung orthopair fuzzy sets are presented and theoretically discussed. Moreover, we apply the new techniques of correlation coefficient under q-rung orthopair fuzzy sets in disease diagnosis and employment process by using simulated q-rung orthopair fuzzy data based on multi-criteria decision-making approach and recognition principle. Some comparative analyses are provided to ascertain the benefits of the new techniques of correlation coefficient under q-rung orthopair fuzzy sets over the obtainable techniques with regard to reliability and performance rating.