Group Decision-making Approach Research Articles

A novel Complex q-rung orthopair fuzzy Yager aggregation operators and their applications in environmental engineering.

Improving human health and comfort in buildings requires efficient temperature regulation. Temperature control system has a significant contribution in minimizing the impact of climate change. Temperature control system is used in industry to control temperature. The polar form of complex Pythagorean fuzzy set is a limited notion because when decision makers take the value for membership degree as then we can observe that the basic condition for complex Pythagorean fuzzy set fails to hold that is . Moreover, we can observe that the Cartesian form of a complex Pythagorean fuzzy set is also a limited notion because it can never discus advance data. Hence keeping in mind these limitations of the existing notions, in this article, we have explored the Cartesian form of a complex q-rung orthopair fuzzy set. Moreover, we have developed the Yager operational laws based on a Cartesian form of complex q-rung orthopair fuzzy set. We have introduced aggregation theory named complex q-rung orthopair fuzzy Yager weighted average and complex q-rung orthopair fuzzy Yager weighted geometric aggregation operators in Cartesian form. Based on these aggregation operators, we have initiated a multi-attribute group decision-making (MAGDM) approach to define the reliability and authenticity of the developed theory. Furthermore, we have utilized this device algorithm in the selection of a temperature control system. The comparative study of the delivered approach shows the advancement and superiority of the delivered approach.

A novel MADM model integrating hybrid information for evaluating the development prospects of urban new energy vehicles.

As an effective approach to mitigating urban environmental issues, New Energy Vehicles (NEVs) have become a focal point of research regarding their current development status and future prospects in China. Addressing the significant disparities in the development of the NEVs industry across different cities, this study focuses on ten typical Chinese cities and develops a novel multi-attribute decision-making (MADM) framework to evaluate the prospects of NEVs promotion in these cities. The study first establishes a comprehensive indicator system that covers key dimensions such as economy, policy support, infrastructure, technological innovation, and environment, encompassing five different types of evaluation information. This system incorporates five different types of evaluation information: exact numbers, interval numbers, triangular fuzzy numbers, hesitant fuzzy numbers, and probabilistic linguistic term sets (PLTS), enhancing the framework's ability to handle diverse data types. Subsequently, the improved entropy (IEntropy) weight method is employed to determine the objective weights of the evaluation indicators. These objective weights are then integrated with the Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, facilitating a structured group decision-making approach that synthesizes hybrid evaluation information. Based on modular thinking, hybrid evaluation information is synthesized to evaluate and rank the NEVs development prospects of each city. Sensitivity analysis and comparative analysis further demonstrate the robustness and reliability of the proposed MADM framework. The ranking results indicate that Shanghai and Guangzhou lead in NEVs promotion, while cities like Harbin and Zhengzhou lag behind. Based on these findings, the study proposes targeted policy recommendations to promote the sustainable development of the NEVs industry in major Chinese cities.

Decision-Making Technique for College Physical Education Teaching Quality Evaluation Based on TODIM and TOPSIS With Interval Neutrosophic Numbers

The evaluation of university physical education teaching quality is a comprehensive assessment of the effectiveness of sports courses, aiming to enhance teaching quality and students' physical literacy. The evaluation includes the achievement of teaching objectives, the scientific and practical nature of course content, the innovation and diversity of teaching methods, the professional competence and teaching ability of instructors, and students' participation and progress.The college physical education teaching quality evaluation is multiple-attribute group decision-making (MAGDM) approach. Recently, the TODIM and TOPSIS techniques have been employed to address MAGDM challenges. To capture uncertain data during the evaluation process, interval neutrosophic sets (INSs) are administrated as a valuable tool. This study introduces the interval neutrosophic number TODIM-TOPSIS (INN-TODIM-TOPSIS) approach to address MAGDM using INSs. Eventually, numerical study for college physical education teaching quality evaluation is administrated to validate the INN-TODIM-TOPSIS approach.

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.

Cost Overrun Risk Assessment for Healthcare Projects: A Modified Fuzzy Group Decision-Making Approach

Cost Overrun Risk Assessment for Healthcare Projects: A Modified Fuzzy Group Decision-Making Approach

A hybrid linear Diophantine fuzzy group decision approach for enterprise digital transformation maturity evaluation

PurposeThe main goal of this paper is to present a synthetic multiple criteria group decision-making (MCGDM) methodology for assessing the enterprise digital maturity with linear Diophantine fuzzy (LDF) setting.Design/methodology/approachThis paper utilizes the presented LDF generalized Dombi operator to aggregate assessment information of experts. The developed combined weight model through merging the rank sum (RS) model and symmetry point of criterion (SPC) method is used to ascertain the comprehensive importance of criterion. The evaluation based on distance from average solution (EDAS) approach based upon regret theory (RT) is presented to achieve the sorting of candidate enterprises.FindingsFirstly, the proposed method has strong stability. Secondly, the proposed method takes into consideration the psychological behavior of experts during the decision-making process which further enhances the rationality of the decision results. Finally, the proposed method integrates expert and criterion weight determination models which provides a practical evaluation framework for assessing the digital maturity of enterprises. The research outcomes confirm that the proposed approach fails to resolve the decision problems with unknown weight information flexibly, but also reflect the psychological behavior of expert in decision process. The presented weight approach also provides a rational algorithm to ascertain the weight more accurate.Originality/valueA composite LDF group decision-making approach is presented by aggregating the proposed generalized Dombi operator, combined weight model and the EDAS model, which make the outcome more reasonable. Sensitivity analysis and comparison study are conducted to reflect the superiority of the proposed approach.

Prediction of eco sustainability component using fuzzy Z numbers based ratio analysis and interval type 3 fuzzy logic system

Amid rapid global urbanization, understanding eco-sustainability challenges in megacities like Delhi is crucial both locally and globally. This study introduces a novel multi-criteria group decision-making approach, the Z-Fuzzy Step-Wise Weight Assessment Ratio Analysis (SWARA), to address these complex challenges. This method prioritizes key eco-sustainability components like air quality index (AQI), water quality index (WQI), Temperature, and Precipitation, based on their impact, aiming to enhance resource allocation and environmental quality. Criteria weights are determined using fuzzy analytical hierarchical processing with Z-numbers, which manage decision-makers' uncertainty through a reliability function, illustrating their confidence levels and supporting the adaptability and effectiveness of eco-sustainability measures. A case study is conducted on seven sub-cities in Delhi examining their unique environmental impacts and urban sustainability challenges. Moreover, the most effective component of eco-sustainability say AQI is also dependent on some factors like PM10, PM2.5, SO2, O3, NO2, and CO. Considering these factors as inputs, an interval type-3 fuzzy logic system (IT3FLS) has been constructed to predict AQI, supporting timely interventions and improvements in eco-sustainability management. The IT3FLS model demonstrated exceptional precision across all Delhi sub-cities, with low root means square error (<0.05), and high R2 values (>0.94), affirming its effectiveness. To validate the Z-Fuzzy SWARA rankings, we compared them with existing Z-Fuzzy methods, consistently ranking AQI first. The IT3FLS's AQI predictions were also compared with those from fuzzy logic and advanced machine learning algorithms, demonstrating superior performance. Finally, the study underscores the managerial implications, emphasizing their role in guiding decision-making and strategy implementation.

Fermatean trapezoidal fuzziness average aggregation scheme for selection of infant clothing by group decision-making

The textile industry has gradually increased its use of various chemicals and the quantity of residues produced. Newborn baby apparel should be made of safe materials since it often comes into touch with them. Infant cloth selection is determined via group decision-making in Fermatean trapezoidal fuzzy number (FTFN). This study provides a new score and accuracy function and weighted, ordered, and hybrid averaging aggregation operators on FTFN. The properties of the proposed averaging aggregation operators on FTFN are presented. An algorithm for the multi-attribute group decision-making (MAGDM) approach will be proposed by applying the proposed averaging aggregation operators. The case studies concentrate on choosing newborn clothing with reduced chemical content within the textile sector. The presentation of the sensitivity analysis of the criterion weights is conducted to guarantee the durability and stability of the framework that has been implemented. Furthermore, a comprehensive analysis compares the novel framework with previous models, highlighting its superior performance.

MCGDM approach based on (p, q, r)-spherical fuzzy Frank aggregation operators: applications in the categorization of renewable energy sources

The growing demand for energy, driven by population growth and technological advancements, has made ensuring a sufficient and sustainable energy supply a critical challenge for humanity. Renewable energy sources, such as biomass, solar, wind, and hydro, are inexhaustible and environmentally friendly, offering a viable solution to both the energy crisis and the fight against global warming. However, selecting the optimal renewable energy source remains a complex decision-making problem due to the varying characteristics and impacts of these sources. Motivated by the need for more accurate and nuanced decision-making tools in this domain, this paper introduces a novel multicriteria group decision-making (MCGDM) approach based on \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:(p,q,r)-$$\\end{document}spherical fuzzy Frank aggregation operators. By integrating Frank t-norm with \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:(p,q,r)-$$\\end{document}spherical fuzzy sets, we develop aggregation operators (AOs) that effectively manage membership, neutral, and non-membership degrees through parameters \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:p$$\\end{document}, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:q$$\\end{document}, and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:r$$\\end{document}. These AOs provide a more refined framework for decision-making, leading to improved outcomes. We apply this approach to evaluate and identify the superior and optimal renewable energy source using artificial data, demonstrating the advantages of the proposed operators compared to existing methods. This work contributes to the field by offering a robust tool for addressing the energy crisis and advancing sustainable energy solutions.

Enterprise risk management in supply chain operation: a fuzzy risk prioritization approach

PurposeTraditional risk prioritization methods in Enterprise Risk Management (ERM) rely on precise data, which is often not available in real-world contexts. This study addresses the need for a robust model that can handle uncertain and imprecise information for more accurate risk assessment.Design/methodology/approachWe propose a group decision-making approach using fuzzy numbers to represent risk attributes and preferences. These are converted into fuzzy risk scores through defuzzification, providing a reliable method for risk ranking.FindingsThe proposed fuzzy risk prioritization framework improves decision-making and risk awareness in businesses. It offers a more accurate and robust ranking of enterprise risks, enhancing control and performance in supply chain operations by effectively representing uncertainty and accommodating multiple decision-makers.Practical implicationsThe adoption of this fuzzy risk prioritization framework can lead to significant improvements in enterprise risk management across various industries. By accommodating uncertainty and multiple decision-makers, organizations can achieve more reliable risk assessments, ultimately enhancing operational efficiency and strategic decision-making. This model serves as a guide for firms seeking to refine their risk management processes under conditions of imprecise information.Originality/valueThis study introduces a novel weighted fuzzy Risk Priority Number method validated in the risk management process of an integrated steel plant. It is the first to apply this fuzzy approach in the steel industry, demonstrating its practical effectiveness under imprecise information. The results contribute significantly to risk assessment literature and provide a benchmarking tool for improving ERM practices.

Archimedean [formula omitted]-norm and [formula omitted]-conorm coupled q-rung orthopair fuzzy TOPSIS method for unknown criteria weighting information

To evaluate some real-life multi-criteria decision-making (MCDM) problems, the decision-makers (DMs) need an interactive and flexible MCDM approach that works well for extended decision-making space and has the ability to apply any suitable operational laws to fuse the information under incomplete criteria weighting information. The objective of this study is to propose a multi-criteria group decision-making (MCGDM) approach to evaluate any real-life MCDM problem under incomplete criteria weighting information. To achieve the objective, the paper proposes an integrated MCGDM approach in which the q-rung orthopair fuzzy set (q-ROFS) is used to provide the extended decision-making space to the DMs, a transformative approach is applied to find the DMs’ weights, the maximizing deviation method is implemented to find the criteria weights, Archimedean t-norm and t-conorm (ATU)-based operational laws are adopted to fuse the information, and the TOPSIS method is applied to rank the alternatives. The proposed integrated MCGDM approach has been applied to a real-life MCDM problem associated with the evaluation of different strategies implemented by governments to contain the COVID-19 pandemic. A sensitivity analysis is conducted to examine the impact of changes in the parameters involved in the proposed MCGDM approach on the results. A comparative analysis is also performed with some existing classical and aggregation operator-based MCDM methods to demonstrate the consistency of the proposed approach. Findings suggest that the proposed MCGDM approach is general and flexible enough to evaluate any real-life MCDM problem under unknown criteria weighting conditions.

Large-Scale Satisfaction Rating-Driven Selection of New Energy Vehicles: A Basic Uncertain Linguistic Information Bonferroni Mean-Based MCGDM Approach Considering Criteria Interaction

The continuous revolution of new energy technologies and the introduction of subsidy policies have promoted green consumers’ willingness to purchase new energy vehicles and automotive online service platforms have disclosed vehicle reputation and consumer satisfaction ratings information. However, due to issues such as uncertain data quality, large data volumes, and the emergence of positive reviews, the cost for potential car buyers to acquire useful decision-making knowledge has increased. Therefore, it is necessary to develop a scientific decision-making method that leverages the advantages of large-scale consumer satisfaction ratings to support potential car buyers in efficiently acquiring credible decision-making knowledge. In this context, the Bonferroni mean (BM) is a prominent operator for aggregating associated attribute information, while basic uncertain linguistic information (BULI) represents both information and its credibility in an integrated manner. This study proposes an embedded-criteria association learning BM operator tailored to large-scale consumer satisfaction ratings-driven scenarios and extends it to the BULI environment to address online ratings aggregation problems. Firstly, to overcome the limitations of BM with weighted interaction (WIBM) when dealing with independent criteria, we introduce an adjusted WIBM operator and extend it to the BULI environment as the BULIWIBM operator. We discuss fundamental properties such as idempotence, monotonicity, boundedness, and degeneracy. Secondly, addressing the constraints on interaction coefficients in BM due to subjective settings, we leverage expert knowledge to explore potential temporal characteristics hidden within large-scale consumer satisfaction ratings and develop a method for learning criteria and interaction coefficients. Finally, we propose a conversion method between user credibility-based ratings and BULI. By combining this method with the proposed adjusted BM operator, we construct a multi-criteria group decision-making (MCGDM) approach for product ranking driven by large-scale consumer satisfaction ratings. The effectiveness and scientific rigor of our proposed methods are demonstrated through solving a new energy vehicle selection problem on an online service platform and conducting comparative analysis. The case analysis and comparative analysis results demonstrate that the interaction coefficients, derived from expert knowledge and 42,520 user ratings, respectively, fell within the ranges of [0.2391, 0.7857] and [0.6546, 1.0]. The comprehensive interaction coefficient lay within the range of [0.4674, 0.7965], effectively mitigating any potential biases caused by subjective or objective factors. In comparison to online service platforms, our approach excels in distinguishing between alternative vehicles and significantly impacts their ranking based on credibility considerations.

Dry port location selection for integration with inland waterway transport in developing countries: A case study in Northern Vietnam

Dry port has emerged as a critical element of transport infrastructure, eliciting substantial research and investment for its development. The strategic selection of dry port locations not only enhances the effectiveness of connections between seaports and hinterlands but also supports the sustainable advancement of the logistics industry, given that dry port operations can integrate with more environmentally friendly transportation modes, particularly inland waterway transport. Extensive research has been conducted to identify optimal dry port locations within the framework of inland waterway container terminals. Nevertheless, these studies primarily focus on developed economies, leaving a notable research void in developing countries. This paper aims to propose a methodological framework for selecting the most suitable dry port location, with a particular emphasis on integration with inland waterway transport in developing nations. This study implements a combination of the Best-Worst Method (BWM) and Elimination Et Choix Traduisant la Realité III (ELECTRE III) in this domain. An analytical case study of Northern Vietnam, considering five alternative dry ports, is conducted to demonstrate the efficacy of the proposed framework. Twenty-seven Vietnamese experts, categorized into three groups—policymakers and consultants, dry port investors and operators, and dry port users—participate in the decision-making process, contributing insights to this case study. An aggregated group decision-making approach is employed. Four principal criteria—economic, accessibility, location, and environmental—are utilized to assess and rank the five alternatives. The findings reveal that a reduction in transport cost is the most critical sub-criterion, while environmental considerations and railway accessibility receive the lowest priority.

An Integrated T-Spherical Fuzzy Einstein Interaction Aggregator Group Decision-Making Approach: A Case Study of Concrete 3D Printing Robot Application in Vietnam

This study introduces the integrated T-spherical fuzzy Einstein interaction aggregator group decision-making approach, a novel framework designed to enhance multi-criteria decision-making (MCDM). Implementing the case study of concrete 3D printing technology in Vietnam, this approach integrates T-spherical fuzzy sets with Einstein aggregation operators to handle the complexities of uncertain and subjective expert judgments effectively. The methodology provides a robust mechanism for evaluating and prioritizing the barriers and strategies associated with the implementation of concrete 3D printing. Findings from this study underline the significance of technological advancements and strategic financial incentives, with R&amp;D strategy emerging as the top priority. This research contributes to both theoretical advancements in decision-making frameworks and offers practical insights for industries looking to integrate emerging technologies. Moreover, it demonstrates the application of advanced fuzzy set theories in real-world settings, providing a valuable tool for decision-makers facing similar technological adoption challenges.

Application of multi-criteria group decision-making for water quality management.

As waste discharge into numerous river systems escalates, the pollution of water bodies typically rises. Given the limited capacity of rivers to withstand pollution and their constrained self-cleaning capabilities, treated pollutants from waste discharge must be released into the river. Despite numerous models and algorithms proposed for managing river water quality to meet standards, literature, to our awareness, lacks the utilization of a comprehensive multi-criteria group decision-making approach for water quality management, particularly in river systems. Therefore, this research introduces a new, comprehensive multi-criteria group decision-making for the management of water quality in the Haraz River basin, located in Iran. To do so, the water quality of the basin, a one-dimensional water quality model, QUAL2Kw, was employed to simulate and calibrate the water quality along the river. The simulation results revealed that the downstream water quality violates the water quality standards. To mitigate this issue, various scenarios for waste load allocation (WLA) were evaluated, including no wastewater treatment, primary wastewater treatment, advanced secondary wastewater treatment utilizing the activated sludge (AS) method, and advanced wastewater treatment via the membrane bioreactor (MBR) method. Utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Fuzzy TOPSIS group decision-making model, it was determined that the optimal solution was the implementation of secondary wastewater treatment utilizing the activated sludge method for the 11 PS of pollution, while still adhering to Iranian water quality standard. In addition, the findings of the present study indicate that the implementation of primary wastewater treatment, advanced secondary wastewater treatment utilizing AS, and advanced wastewater treatment through MBR within the study area led to a significant enhancement in water quality. This enhancement ranged from 35 to 105% across various scenarios when compared to conditions where no actions were taken to the treatment of water.

A fuzzy reliability assessment methodology for city gas stations based on an extended T-S fault tree

City gas stations (CGSs) play a crucial role in ensuring a stable and safe supply of natural gas to urban users. However, as the service time of stations increases and the performance of components deteriorates, concerns about the safety and reliability of these station have grown among operators and local government authorities. This paper proposes a fuzzy reliability assessment methodology for CGSs that considers the polymorphism of component faults and the uncertainties associated with fault relationships, failure probabilities, and fault magnitudes. The methodology utilizes T-S fuzzy gates to describe the correlation among events and constructs a T-S fuzzy fault tree for CGSs. Component fault states are represented using fuzzy numbers, and a fuzzy group decision-making approach is introduced to evaluate the current fault magnitude of components. To handle the uncertainty caused by sparse failure sample data, a Bayesian updating estimation method is presented to estimate the failure probabilities of components. Furthermore, T-S fuzzy importance analysis is applied to identify the weak points in the CGS system. The effectiveness of the developed methodology is demonstrated through a case study of reliability analysis of a city gas distribution station. The research findings provide valuable support for optimizing the design and implementing preventive maintenance of CGSs.

Group decision-making approach based on the distance measure of linguistic intuitionistic fuzzy sets and VIKOR technique

Group decision-making approach based on the distance measure of linguistic intuitionistic fuzzy sets and VIKOR technique

Evaluation and selection of E-learning websites using intuitionistic fuzzy confidence level based Dombi aggregation operators with unknown weight information

In recent years, the surge in online learning has eclipsed traditional classroom teaching, elevating the prominence of e-learning platforms. However, the quest for a reliable methodology to evaluate these platforms has remained pressing. While several studies have explored this domain, none have integrated decision experts’ confidence levels. This study bridges this gap by seamlessly incorporating confidence levels into Dombi aggregation operators within an intuitionistic fuzzy framework. The study introduces two innovative operators: the confidence-level intuitionistic fuzzy Dombi weighted averaging operator (CIFDWA) and the confidence-level intuitionistic fuzzy Dombi weighted geometric operator (CIFDWG). These operators are employed to form a novel multi-attribute group decision-making (MAGDM) approach, which is demonstrated by applying them to select the optimal e-learning website in a real-world scenario. The “Stepwise Weight Assessment Ratio Analysis (SWARA)” method is utilized to determine criteria weights. The findings highlight the critical importance of sub-criteria such as topic diversity, pricing, content credibility, data security, and user interface simplicity. The analysis reveals Unacademy as the leading platform, closely followed by Byju’s. Through meticulous comparative analysis and sensitivity evaluations, we underscore the stability and coherence of the proposed model. The study’s novelty lies in its innovative approach to integrating confidence levels in multi-attribute group decision-making scenarios, promising broader applications across various real-world contexts. Educational advisers and management can leverage this methodology to empower students with informed choices.

A picture fuzzy CIMAS-ARTASI model for website performance analysis in human resource management

The central emphasis of human resource management resides in the precise delineation of job responsibilities, engaging in systematic inquiry to identify well-suited candidates, and discerningly electing the most qualified individual from the pool whose qualifications align with the job description. The selection of a well-suited candidate is dependent on the creation of an appropriate candidate pool. Digital platforms are preferred over traditional approaches to reach human resources. The main objective of this research is to identify the most suitable digital platform for posting job advertisements based on website performance. The multiple-attribute group decision-making approach is adopted in this research, considering both qualitative and quantitative criteria. A decision support system for website selection is developed, where expert importance levels are calculated based on picture fuzzy sets (PFS). The weight levels of criteria are determined using the introduced PFS-based criteria importance assessment (CIMAS) method. The ranking of websites is calculated using the proposed PFS-based alternative ranking technique based on adaptive standardized intervals (ARTASI) method. These methods are hybridized into the PFS-CIMAS-ARTASI model. Additionally, an algorithm for this hybrid model is developed. A case study is conducted to demonstrate the applicability of the PFS-CIMAS-ARTASI hybrid model for website performance calculations. Robustness tests based on various sensitivity analysis scenarios are performed. The research results indicate that PFS-CIMAS-ARTASI is both applicable and robust. Comprehensive managerial implications are presented and elaborated on.

Effective multi-attribute group decision-making approach to study astronomy in the probabilistic linguistic q-rung orthopair fuzzy VIKOR framework

This study employs a novel fuzzy logic-based framework to address multi-attribute group decision-making problems commonly encountered in modern astronomy. Our approach utilizes the probabilistic linguistic q-rung orthopair fuzzy set (PLq-ROFS) to handle the inherent uncertainties associated with astronomical data. The PLq-ROFS offers significant advantages over existing fuzzy sets like probabilistic hesitant, linguistic intuitionistic, and linguistic Pythagorean fuzzy sets, which comprise both stochastic and non-stochastic uncertainties simultaneously. To aggregate the probabilistic linguistic decision information effectively, we propose two novel operators: the PLq-ROF weighted power average (PLq-ROFWPA) and the PLq-ROF weighted power geometric (PLq-ROFWPG). These operators form the foundation of a novel method within the PLq-ROF environment. Furthermore, this study integrates the PLq-ROF framework with the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) model, a widely used decision-making (DM) tool known for its ability to balance group utility maximization with individual regret minimization. This integration leads to the PLq-ROF-VIKOR model, a novel approach for ranking alternative solutions based on the subjective preferences of decision-makers. The effectiveness of the proposed method is demonstrated through a real-world case study in astronomy, accompanied by both parameter and comparative analyses. These analyses highlight the efficiency and accuracy of the PLq-ROF-VIKOR model, ultimately leading to the conclusion that cosmology is the most optimal key finding in this case study.