Multi-attribute Group Research Articles

N-Bipolar Soft Expert Sets and Their Applications in Robust Multi-Attribute Group Decision-Making

This paper presents N-bipolar soft expert (N-BSE) sets, a novel framework designed to enhance multi-attribute group decision-making (MAGDM) by incorporating expert input, bipolarity, and non-binary evaluations. Existing MAGDM approaches often lack the ability to simultaneously integrate positive and negative assessments, especially in nuanced, multi-valued evaluation spaces. The proposed N-BSE model addresses this limitation by offering a comprehensive, mathematically rigorous structure for decision-making (DM). Fundamental operations of the N-BSE model are defined and analyzed, ensuring its theoretical consistency and applicability. To demonstrate its practical utility, the N-BSE model is applied to a general case study on sustainable energy solutions, illustrating its effectiveness in handling complex DM scenarios. An algorithm is proposed to streamline the DM process, enabling systematic and transparent identification of optimal alternatives. Additionally, a comparative analysis emphasizes the advantages of the N-BSE model over existing MAGDM frameworks, highlighting its capacity to integrate diverse expert opinions, evaluate both positive and negative attributes, and support multi-valued assessments. By bridging the gap between theoretical development and practical application, this paper contributes to advancing DM methodologies.

Charging Method Selection of a Public Charging Station Using an Interval-Valued Picture Fuzzy Bidirectional Projection Based on VIKOR Method with Unknown Attribute Weights

Excessive use of fossil fuel-powered vehicles is a major problem for the entire world today, because of which greenhouse gases are increasing day by day. As a result, climate change and global warming have grown to be serious problems that affect both the environment and life on Earth. However, the effective way of reducing greenhouse gases is to use electric vehicles for commuting. The assessment and selection of the best possible way of charging an electric vehicle is a convoluted decision-making challenge due to the presence of assorted contradictory criteria. Additionally, individual decision makers’ minds and insufficient data are obstacles to doing this. In this regard, interval-valued picture fuzzy sets have been considered as a compatible tool to handle vagueness. In this paper, a multi-attribute group decision-making problem with the bidirectional projection-based VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is considered where the weights are partially known. The objective weights of the attributes in this model are determined using the deviation-based approach. The compromised solution is also assessed using the VIKOR approach. Both the interval-valued image fuzzy Schweizer–Sklar power weighted geometric operator and the interval-valued picture fuzzy Schweizer–Sklar power weighted averaging operator are used in this process. Lastly, a numerical example showing the most suitable way to charge an electric vehicle is given to demonstrate the suggested methodology. To evaluate the robustness and efficacy of the suggested strategy, a comparative analysis with current techniques and a sensitivity analysis of the parameters are also carried out.

An overview of pink eye infection to evaluate its medications: group decision-making approach with 2-tuple linguistic T-spherical fuzzy WASPAS method.

An infectious eye illness known as pink eye results in ocular redness, irritation, and mucus. Schools are an especially vulnerable region for dissemination because they can propagate that contagious disease quickly via direct or indirect interactions. Choosing the right medication to treat pink eye infection is typically thought of as an intricate multi-attribute group decision-making concern. The goal of this research is to construct a multi-attribute group decision-making framework that assesses six pink eye treatment medications, including Bleph-10, Moxeza, Zymar, Romycin, Polytrim, and Bacticin. The constructed multi-attribute group decision-making framework includes the following scenario: (1) In contrast to other types of fuzzy sets, the 2-tuple linguistic T-spherical fuzzy set (2TLT-SFS) looks to be a potent tool for dealing with informational inconsistencies in decision-making scenarios; (2) in order to render the 2TLT-SF accumulation details processing more flexible, the addition, multiplication, scalar multiplication, and exponential laws that are predicated on the Schweizer-Sklar collection of t-conorms and t-norms are described; (3) the Schweizer-Sklar weighted average and Schweizer-Sklar weighted geometric operators are then put forward employing the aforementioned operations to combine the data; (4) subsequently, using newly developed operators (referred to as 2TLT-SF Schweizer-Sklar weighted average and 2TLT-SF Schweizer-Sklar weighted geometric), this work enhances the conventional weighted aggregated sum product assessment (WASPAS) approach. The computation procedure for this methodology is thoroughly given to rank the alternatives; (5) to confirm the viability of the suggested approach, thorough computational and simulation assessments are conducted. An examination of the developed and existing research is compared to demonstrate the benefits of the suggested analysis.

Stock Market Prediction Based Multi-Attribute Decision Making Model Using Picture Fuzzy Z^-Information

As demonstrated in the section above, the stock market place is a dynamic factor, which makes it possible for traders and investors to make good decisions based on the information acquired through accurate prediction. This research aims at improving the prediction of stock market by applying a new method to Multi-attribute Group Decision making (MAGDM). MAGDM goes through a cycle of evaluating and ranking several criteria hence enhancing the decision-making aspects further. To overcome the shortcomings of prior models, some EU and FU is incorporated by combining Zadeh’s Z^-numbers with Picture Fuzzy Sets (PFSs). This integration is to enhance the ability of the model to address completely unclear decisions utilizing the peculiarities of Z^-numbers. To compare decisions between decision-makers, we proposed picture fuzzy Z^-numbers (PFZ^N) and for their aggregation, introduced picture fuzzy weighted averaging, picture fuzzy ordered weighted averaging, picture fuzzy hybrid averaging, picture fuzzy weighted geometric, picture fuzzy ordered weighted geometric and picture fuzzy hybrid geometric operators based algebraic T-norm (T-N) and T-conorm (T-CNs) To verify the efficiency of our suggested technique, we compare these operators with the Combined Compromised Solution (CoCoSo) model focusing on the stock market analysis. Our results, therefore, show how these operators are important in improving decision making accuracy and precision in conditions of risk. This research laid down the basis for enhancing decision-making and dealing with uncertainty in different fields especially in the application of stock market prediction. The proposed methodology can be attributed to providing a systematic and a more efficient way of dealing with uncertainty which in one way or the other has an outcome of enhancing the credibility of the decision making process in the financial sector.

An applied study of multi-stage multi-attribute group decision-making based on intuitionistic fuzzy sets—selection of hydrogen energy projects in the context of “dual-carbon"

In group decision-making scenarios involving intuitionistic fuzzy numbers for attribute values, attribute weights tend to be subjective. To enhance objectivity and scientific rigor in decision-making, this study develops a multi-stage, multi-attribute group decision-making model based on intuitionistic fuzzy sets. The model integrates both subjective and objective attribute weights and uses a product method to calculate comprehensive weights. A new “double-consideration” method is proposed to determine time weights, making it particularly suitable for multi-stage decision-making. The model also aggregates multi-stage information through an intuitionistic fuzzy weighted average operator and employs Pearson’s correlation coefficient to assign expert weights, promoting group consensus. The improved TOPSIS (G-TOPSIS) method is then applied to the selection of hydrogen energy projects within the “dual-carbon” framework. Comparative and sensitivity analyses verify the model’s feasibility and effectiveness, offering valuable guidance for hydrogen energy project selection in alignment with “dual-carbon” goals.

Medical Diagnosis Based on Multi-Attribute Group Decision-Making Using Extension Fuzzy Sets, Aggregation Operators and Basic Uncertainty Information Granule

Medical Diagnosis Based on Multi-Attribute Group Decision-Making Using Extension Fuzzy Sets, Aggregation Operators and Basic Uncertainty Information Granule

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.

Constructing a knee osteoarthritis transplant via the neutrality aggregation operators under spherical fuzzy Z-numbers framework.

Understanding the biomechanics of osteoarthritis is necessary for designing a biomedical knee implant to reduce pain, increase mobility, and enhance the patient's quality of life. The most appropriate implant design may be chosen by using Multi-Attribute Group Decision-Making (MAGDM) techniques, which include a number of variables including material characteristics, biomechanical performance, cost-effectiveness, and patient-specific requirements. Compared to conventional fuzzy set structures, Spherical Fuzzy -Number Sets ( S) provide an enhanced method for resolving uncertainty in MAGDM and are more suited for handling complicated decision-making situations. In order to get crucial weight information, this work uses spherical fuzzy entropy measures ( ) to suggest a unique distance metric specifically designed for spherical fuzzy -numbers. Furthermore, to solve issues in MAGDM, especially when the preferences and criterion weights of decision-makers are uncertain, we offer Aggregation Operators based on Neutrality Aggregation ( ) T-norm and T-Conorm. We first investigate the notion, their accuracy and scoring functions, and the fundamental ideas that underlie their work. Next, we create a method for generating decisions for MAGDM situations by using information. Our suggested operators provide a flexible and resilient structure for collecting information by integrating operations into the framework. This makes them particularly useful for developing knee implants for osteoarthritis. This work advances the field of MAGDM for biomedical engineering both theoretically and practically. We provide comparison findings to confirm our technique and show its efficacy and field usefulness.

Deconstruction of Intelligent Vehicle Cyber-Physical System Based on Fuzzy Soft Set and Multi-Attribute Group Decision Making

Deconstruction of Intelligent Vehicle Cyber-Physical System Based on Fuzzy Soft Set and Multi-Attribute Group Decision Making

A novel approach to multi-attribute group decision-making: Optimistic and pessimistic three-state three-way decision models

A novel approach to multi-attribute group decision-making: Optimistic and pessimistic three-state three-way decision models

Stochastic-simulation-based multi-attribute group decision-making method under uncertain environment and the application

Stochastic-simulation-based multi-attribute group decision-making method under uncertain environment and the application

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.

Dynamic Evaluation of Adaptive Product Design Concepts Using m-Polar Linguistic Z-Numbers

Adaptive design focuses on creating flexible products that meet evolving demands and enhance sustainability. However, evaluating adaptive design concepts poses significant challenges due to the dynamic nature of product features over time and the inherent uncertainty in decision-makers’ (DMs’) evaluations. Most traditional frameworks rely on static models that fail to capture the temporal evolution of attributes and often overlook decision-makers’ (DMs’) confidence levels, resulting in incomplete or unreliable evaluations. To bridge these gaps, we propose the m-polar linguistic Z-number (mLZN) to address these issues. This framework uses the dynamic representation capabilities of m-polar fuzzy sets (mFSs) and the symmetrical structure of linguistic Z-numbers (LZNs), which effectively integrate linguistic evaluations with corresponding confidence levels, providing a balanced and robust approach to handling uncertainty. This approach models design characteristics across multiple periods while accounting for DMs’ confidence levels. Based on this framework, we develop mLZN weighted and geometric aggregation operators, computation rules, and ranking methods to support dynamic multi-attribute group decision-making (MAGDM). The proposed framework’s effectiveness is demonstrated through a case study on adaptive furniture design for children, which showcases its ability to dynamically evaluate key attributes, including safety, ease of use, fun, and comfort. Furthermore, we validate its robustness and feasibility through comprehensive sensitivity and comparative analyses.

A Two-Stage Multi-attribute Group Decision-Making Model Based on Regret Theory and Three-Way Decision Method with R-Numbers

A Two-Stage Multi-attribute Group Decision-Making Model Based on Regret Theory and Three-Way Decision Method with R-Numbers

Interval-value Pythagorean Fuzzy Prioritized Aggregation Operators for Selecting an Eco-Friendly Transportation Mode Selection

When selecting an environmentally friendly mode of transportation, many factors must be taken into account, such as capacity, delivery time, cost-effectiveness, and environmental impact. This is a difficult decision-making problem since it calls for the simultaneous evaluation and prioritization of several criteria. A thorough and methodical approach is required to make an informed decision that satisfies particular transportation needs while adhering to sustainability goals. The process entails balancing various trade-offs to determine the most environmentally friendly mode of transportation. When solving multi-attribute group decision-making (MAGDM) problems, prioritization is essential. Prioritization in fuzzy systems has been implemented through a variety of techniques and approaches. This paper tackles the MAGDM problem within the Pythagorean fuzzy (PyF) framework, taking into account the different requirements for experts and characteristics. We present new Aczel Alsina aggregation operators (AOs), whose efficient handling of uncertainties makes a major contribution to fuzzy mathematics. We suggest several PyF AOs, such as the PyF-prioritized Aczel Alsina geometric (PyFPAAG) and PyF-prioritized Aczel Alsina averaging (PyFPAAA), which are based on the Aczel Alsina t-norm and t-conorm. We show these AOs satisfy the aggregation criteria by examining their monotonicity, boundedness, and idempotency properties. The prioritization weights are derived from expert knowledge, enabling the suggested operators to capture the prioritization phenomenon among the aggregated arguments. Using a MAGDM technique, the proposed AOs are used to evaluate eco-friendly transportation modes. Their significance is confirmed by contrasting them with other well-known AOs.

Discrete Pseudo-Quasi Overlap Functions and Their Applications in Fuzzy Multi-Attribute Group Decision-Making

The overlap function, a continuous aggregation function, is widely used in classification, decision-making, image processing, etc. Compared to applications, overlap functions have also achieved fruitful results in theory, such as studies on the fundamental properties of overlap functions, various generalizations of the concept, and the construction of additive and multiplicative generators based on overlap functions. However, most of the research studies on the overlap functions mentioned above assume commutativity and continuity, which can limit their practical applications. In this paper, we remove the symmetry and continuity from overlap functions and define discrete pseudo-quasi-overlap functions on finite chains. Meanwhile, we also discuss their related properties. Then, we introduce pseudo-quasi-overlap functions on sub-chains and construct discrete pseudo-quasi-overlap functions on finite chains using these sub-chain functions. Unlike quasi-overlap functions on finite chains generated by the ordinal sum, discrete pseudo-quasi-overlap functions on finite chains constructed through pseudo-quasi-overlap functions on different sub-chains are dissimilar. Eventually, we remove the continuity from pseudo-automorphisms and propose the concept of pseudo-quasi-automorphisms. Based on this, we utilize pseudo-overlap functions, pseudo-quasi-automorphisms, and integral functions to obtain discrete pseudo-quasi-overlap functions on finite chains; moreover, we apply them to fuzzy multi-attribute group decision-making. The results indicate that—compared to overlap functions and pseudo-overlap functions—discrete pseudo-quasi-overlap functions on finite chains have stronger flexibility and a wider range of practical applications.

Cross-functional group decision making with heterogeneous cooperation for digital transformation in supply chain resilience

Supply chain resilience plays a critical role in gaining competitive advantages for companies. The resilience of supply chains can be achieved by leveraging emerging digital technologies to realize digital transformation. It is necessary to select an appropriate digitalization technology under such background. The wide-spanning of digital transformation and technology selection needs cross-functional integration of various expertise. However, in the process of making decisions by leveraging expert wisdom, differences in experts’ willingness to cooperate lead to difficulties in reaching a consensus. The existing literature fails to incorporate both non-cooperation and proactive-cooperation into the consensus reaching process. Thus, in this study, we introduce a cross-functional multi-attribute group decision making model for digitalization technology selection. To manage potential non-cooperative behaviors in the group consensus reaching process, the proposed model allows experts to have proactive cooperation, i.e., making more contributions than recommended feedback suggestions provided by the moderator. Proactive cooperation can make up for the loss caused by the non-cooperative behaviors of experts. A knowledge mining method is proposed to mine academic and practical preferences for attributes. Two consensus mechanisms are put forward for the meso decision-making process in functional teams and the macro decision-making process in the whole group, respectively. An illustrative example regarding the technology selection in shipbuilding industry is provided to verify the applicability of our model. Numerical experiments suggest that our model will improve the efficiency of consensus reaching process.

Multi-attribute group decision-making method using single-valued neutrosophic credibility numbers with fairly variable extended power average operators and GRA-MARCOS

Data trading platform (DTP) selection is a classic multi-attribute group decision-making (MAGDM) problem. As an extension of intuitionistic fuzzy sets (IFSs), single-valued neutrosophic credibility numbers (SvNCNs) can express both fuzzy evaluation information and the credibility level of the information, offering better expressiveness in describing fuzzy decision-making information. However, existing studies on aggregation operators and decision-making methods in the SvNCN environment are inadequate. Therefore, this paper proposes a MAGDM technique based on the fairly weighted variable extended power average (SvNCNFWVEPA) operators of SvNCNs and grey relational analysis (GRA)-Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method. The main contributions are as follows: (1) we propose fairly operation rules for aggregating SvNCNs in an unbiased manner; (2) addressing the lack of SvNCN measurement research, we introduce preference distance and entropy measures for SvNCNs, then utilize the entropy measure to compute the objective weights of the attributes; (3) to effectively aggregate SvNCN information, inspired by the variable power geometric (VPG) operators and the extended power average (EPA) operators, we propose the variable extended power average (VEPA) operator for scientifically handling extreme values, extending it to SvNCNs with the SvNCNs fairly variable extended power average (SvNCNFVEPA) operators and their extended form; (4) we introduce the GRA method to calculate the degree of utility of alternatives relative to ideal and anti-ideal alternatives, forming the GRA-MARCOS method. This method can reflect both indicator differences and the similarity of alternatives, thereby rendering the evaluation results more scientific and objective; (5) to illustrate the application of the method to the MAGDM problem, we apply it to the example of DTP selection. Parameter sensitivity analysis and comparative analysis with other existing methods demonstrate that our proposed method is more scientific and flexible.

A Novel ARAS-H Approach for Normal T-Spherical Fuzzy Multi-Attribute Group Decision-Making Model with Combined Weights

A Novel ARAS-H Approach for Normal T-Spherical Fuzzy Multi-Attribute Group Decision-Making Model with Combined Weights

Bounded rationality consensus reaching process with regret theory and weighted Moment estimation for multi-attribute group decision making

Probabilistic linguistic term sets perform a particularly active role in the field of decision-making, particularly regarding decision-makers (DMs) who are inclined to convey evaluative information through natural linguistic variables. To effectively improve the current dilemma of multi-attribute group decision-making (MAGDM), this article put forward a new probabilistic linguistic MAGDM method with weighted Moment estimation. First, taking into account the psychological aspect of regret aversion among DMs, we use regret theory to transform the original decision-making matrix into the utility matrix, in which DMs usually exhibit limited rationality during the process of MAGDM. Then, a combined weighting method and a weighted Moment estimation model are investigated to determine the attribute weights, which are more scientifically and reasonably. Subsequently, in the process of consensus reaching process, a new trust propagation mechanism is designed to derive the weights of experts and the adjustment coefficients, in which we consider the shortest and longest propagation paths among DMs. Finally, an empirical validation of the MAGDM method's applicability is conducted utilizing raw coal quality assessment, accompanied by sensitivity and comparative analyses that underscore its advantages and robustness.