Q-rung Orthopair Research Articles

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

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.

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

Q-Rung orthopair fuzzy multi-criteria decision-making method for Internet of Things platforms selection

q-Rung orthopair fuzzy multi-criteria decision-making method for Internet of Things platforms selection

Weighted correlation coefficients for q-rung orthopair fuzzy sets with application in multi-criteria decision-making

Weighted correlation coefficients for q-rung orthopair fuzzy sets with application in multi-criteria 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

Evaluation of medium-lift forest fire helicopter using q-rung orthopair fuzzy set based alternative ranking technique based on adaptive standardized intervals approach

Evaluation of medium-lift forest fire helicopter using q-rung orthopair fuzzy set based alternative ranking technique based on adaptive standardized intervals approach

Q-rung orthopair fuzzy equivalences and related similarity measures with applications to pattern recognition and MADM

Q-rung orthopair fuzzy equivalences and related similarity measures with applications to pattern recognition and MADM

An extended TOPSIS technique based on correlation coefficient for interval-valued q-rung orthopair fuzzy hypersoft set in multi-attribute group decision-making

The accurate determination of results in decision analysis is usually predicated on the association between two factors. Although generating data for analytical purposes presents an apparent hurdle, the data obtained may present hurdles in its interpretation. Correlation coefficients can be used to analyze the interaction between two factors and their variations. These coefficients deliver an objective description of the association between parameters, assisting in predicting and assessing alterations between particular parameters. The purpose of this research is to explore the applicability of correlation coefficients (CC) and weighted correlation coefficients (WCC) in interval-valued q-rung orthopair fuzzy hypersoft sets (IVq-ROFHSS) structures with their essential characteristics. These measures are developed to address the inevitable confusion, inconsistency, and volatility in real-life decision-making challenges. The implementation of these components attempts to boost the productivity of the technique for order preference by similarity to the ideal solution (TOPSIS) method. The computational models with correlation constraints are presented to determine the reliability and regularity of the proposed method. This research proves that the proposed technique is effective for multi-attribute group decision-making (MAGDM), particularly for analyzing and prioritizing convoluted data sets. Moreover, a numerical illustration is presented to clarify how the advocated decision-making methodology can be implemented in reality in evaluating bio-medical disposal techniques for hospitals. This study determines incineration as the most beneficial method for BMW disposal, demonstrating its more efficient use of alternative disposal techniques. A comparative analysis further substantiates the feasibility and effectiveness of the proposed approach over other decision-making techniques.

Fuzzy-based group decision-making approach utilizing a 2-tuple linguistic q-rung orthopair set for the selection of optimal watershed system model

Watershed system models are tools used to study and simulate the behavior of watersheds, which are areas of land where all the water drains to a common outlet, typically a river, lake, or ocean. These models are essential for understanding and managing water resources, predicting flood events, and assessing the impact of land use and climate changes on the environment. Moreover, these advanced models should also help in making better decisions about the watersheds. This paper presents an integrated novel approach for decision-making in the selection of a watershed system model called decision-making trial and evaluation laboratory (DEMATEL)-multi-objective optimization based on the ratio analysis (MOORA) framework. The proposed method employs the 2-tuple linguistic q-rung orthopair fuzzy set (2TLq-ROFS) and enables the integration of new knowledge into models through dynamic data-sharing mechanisms. The weights of attributes are determined by the application of the 2TLq-ROF-DEMATEL technique, while the 2TLq-ROF-MOORA approach is employed to establish the ranking of the alternatives. The weighted power Muirhead mean (WPMM) aggregation operator is subsequently expanded to incorporate the 2TLq-ROF information for aggregating data. Finally, in order to assess the feasibility of the given methodology, an illustration is employed to choose the most suitable watershed system model. Moreover, we establish the soundness and feasibility of our methodology through a comprehensive comparison with several existing methodologies. Finally, findings of the study are reviewed, and potential directions for future research are discussed.

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.

Modal multi-attribute group decision making with unknown weights and new scores under interval-valued q-rung orthopair fuzzy sets and its applications

Modal multi-attribute group decision making with unknown weights and new scores under interval-valued q-rung orthopair fuzzy sets and its applications

Stratified enhanced interval-valued q-rung orthopair picture bipolar fuzzy decision-making approach for municipal solid waste disposal technique selection

Stratified enhanced interval-valued q-rung orthopair picture bipolar fuzzy decision-making approach for municipal solid waste disposal technique selection

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