Dual Hesitant Fuzzy Research Articles

Multi-Attribute Decision-Making Based on Consistent Bidirectional Projection Measures of Triangular Dual Hesitant Fuzzy Set

Multi-Attribute Decision-Making Based on Consistent Bidirectional Projection Measures of Triangular Dual Hesitant Fuzzy Set

Some Novel Correlation Coefficients of Probabilistic Dual Hesitant Fuzzy Sets and their Application to Multi-Attribute Decision-Making

Some Novel Correlation Coefficients of Probabilistic Dual Hesitant Fuzzy Sets and their Application to Multi-Attribute Decision-Making

Site Selection of Wind Farms Based on Novel Probabilistic Dual Hesitant Fuzzy ExpTODIM and LogTODIM Methods

Site Selection of Wind Farms Based on Novel Probabilistic Dual Hesitant Fuzzy ExpTODIM and LogTODIM Methods

Three-way decisions with dual hesitant fuzzy covering-based rough set and their applications in medical diagnosis

As an extension of partition, fuzzy β-covering can provide a more realistic and accurate description of incomplete information. In this paper, we mainly integrate the idea of fuzzy β-covering with dual hesitant fuzzy (DHF) information and construct some novel three-way decision (3WD) models with DHF covering-based rough set. Firstly, we propose the notions of DHF β-covering, DHF β-minimal description and DHF β-maximal description, and then construct three types of DHF neighborhood operators. Meanwhile, we introduce DHF conditional probability by using DHF neighborhood operator. Secondly, in terms of DHF conditional probability, DHF covering-based probabilistic rough set, DHF covering-based decision-theoretic rough set and their 3WD models are established. In light of DHF neighborhood operator, we propose two pairs of optimistic and pessimistic DHF decision evaluation functions and build two 3WD models through them. Lastly, a numerical example is employed to elaborate the application of the above models, which is effective and credible to medicine screening for Alzheimer’s disease.

Conceptual design and configurations selection of S/H-UAVs based on Q-rung dual hesitant double layer FQFD

For the conceptual design phase of Unmanned Aerial Vehicles (UAVs), a process for conceptual design and configuration selection of Solar/Hydrogen powered UAVs (S/H-UAVs) is proposed. The design requirements of S/H-UAVs were analyzed firstly. The proposed process used Fuzzy Quality Function Deployment (FQFD) to establish logical and quantitative standards. Moreover, in order to appropriately describe the hesitancy of experts while making decision, it used Q-Rung Dual Hesitant Fuzzy Sets (QRDHFS) to score the correlationships. In addition, a decision-making framework is proposed to perform a logical selection of typical layouts based on defuzzification method and Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). The present process has been applied for S/H-UAVs. The resulting set of design requirements consists of three categories: Mission Requirements (MRs), Engineering Characteristics (ECs) and Technical Indicators (TIs). Four typical layouts of S/H-UAVs were sorted and determined. The performance of four typical layouts were evaluated and the Strut-Braced Wing (SBW) with external hydrogen storage was selected as the best layout for S/H-UAVs.

A Dual Hesitant Fuzzy Set Theoretic Approach in Fuzzy Reliability Analysis of a Fuzzy System

A Dual Hesitant Fuzzy Set Theoretic Approach in Fuzzy Reliability Analysis of a Fuzzy System

Multi-Criteria Decision Making Model For Hotel Selection Problem Under Complex Dual Hesitant Fuzzy Information

ABSTRACT The notion of the “complex dual hesitant fuzzy set (CDHFS)” is the combination of the “dual hesitant fuzzy set (DHFS)” and the “complex fuzzy set (CFS).” It is characterized by two degrees, namely the membership and nonmembership, in the form of a finite subset on a unit disc in the complex plane. CDHFS is useful for dealing with real-world problems involving uncertain or hard-to-predict information. Also, to approximate smoothly, the Einstein operators are well-known aggregation operators, while prioritized operators are effective tools for prioritization among criteria. Therefore, the goal of this study is to develop some prioritized aggregation operators under the CDHFS environment; namely the complex dual hesitant fuzzy prioritized averaging (CDHFPA) operator, the complex dual hesitant fuzzy prioritized geometric (CDHFPG) operator, the complex dual hesitant fuzzy Einstein prioritized averaging (CDHFEPA) operator, and complex dual hesitant fuzzy Einstein prioritized geometric (CDHFEPG) operator. Some properties of the proposed operators are investigated in detail. In addition, a multi-criteria decision-making (MCDM) method based on the proposed operators with the complex dual hesitant fuzzy setting is developed. Moreover, a numerical example is given for the application and effectiveness of the developed MCDM approach. A comparison study is also done with existing methods to show that the proposed MCDM method is better and more reliable. The study finds that if the expert’s preference is used to choose the right aggregation operators, the decision maker will have access to a wide range of compromise solutions.

An Enhanced Interactive and Multi-criteria Decision-Making (TODIM) Method with Probabilistic Dual Hesitant Fuzzy Sets for Risk Evaluation of Arctic Geopolitics

An Enhanced Interactive and Multi-criteria Decision-Making (TODIM) Method with Probabilistic Dual Hesitant Fuzzy Sets for Risk Evaluation of Arctic Geopolitics

A note on “Some dual hesitant fuzzy Hamacher aggregation operators and their applications to multiple attribute decision making”

As dual hesitant fuzzy sets can express the uncertainty of data efficiently, the aggregation of dual hesitant fuzzy information plays an important role in both theory and application. However, some existing dual hesitant fuzzy aggregation operators are not rigorous enough actually. In this note, we show that some theorems in an earlier paper by Ju et al. [1] (Journal of Intelligent & Fuzzy Systems 27 (2014) 2481–2495) are not correct, i.e., the dual hesitant fuzzy Hamacher weighted averaging operator (DHFHWA) and some other aggregation operators proposed by Ju et al. don’t satisfy idempotency and boundedness. Therefore, the purpose of this paper is to make researchers aware of that some aggregation operators in literature [1] are flawed and limited for many applications.

The Selection of Investment Priorities using Expanded Dual Hesitant Fuzzy Sets

The concept of expanded dual hesitant fuzzy set is a concept that can be applied in decision-making problems. In decision-making problems, expanded dual hesitant fuzzy set can be applied to represent the opinions of multiple experts or stakeholders in a more elaborate manner. It enables decision-makers to provide more detailed information about their preferences and hesitancy.

The Probabilistic Dual Hesitant Fuzzy Multi-Attribute Decision-Making Method Based on Cumulative Prospect Theory and Its Application

Addressing the complex issue of multi-attribute decision-making within a probabilistic dual hesitant fuzzy context, where attribute weights are unknown, a novel decision-making method based on cumulative prospect theory is proposed, named the probabilistic dual hesitant fuzzy multi-attribute decision-making method based on cumulative prospect theory. Firstly, a decision matrix is formulated, representing probabilistic dual hesitant fuzzy information. Secondly, according to the decision maker’s authentic preference and non-membership information sensitivity, a comprehensive score function suitable for probabilistic dual hesitant fuzzy elements is proposed. The attribute weights are then determined using the entropy method. Next, the value function and decision weight function from the cumulative prospect theory are employed to compute the cumulative prospect value attributed to each available scheme. In addition, a cumulative prospect matrix is constructed, enabling the establishment of scheme rankings based on the comprehensive cumulative prospect value. Finally, the analysis of specific cases and a comparative assessment of methods pertaining to the selection of emergency response schemes collectively demonstrate the rationality and efficacy of the decision-making method presented in this study.

Determining the Amount of Car Production to Obtain Maximum Profit Using the Concept of Dual Hesitant Fuzzy Set

In the context of decision-making problems related to car production, the dual-hesitant fuzzy set can be a tool to deal with uncertainty and hesitation in determining the optimal number of cars to be produced for each type of car. The score values associated with the dual-hesitant fuzzy set can represent the level of preference or confidence in producing the number of cars necessary to obtain maximum profit.

The cross-border e-commerce platform selection based on the probabilistic dual hesitant fuzzy generalized dice similarity measures

Abstract Cross-border e-commerce platform (CBECP) plays a very important role in the development of a cross-border e-commerce (CBEC). How to select the best CBECP scientifically and reasonably is a very critical multi-attribute group decision-making (MAGDM) issue. With the uncertainty of people’s cognition of the objective world, the decision-making process is full of a lot of fuzzy information. In view of the great advantages of probabilistic dual hesitation fuzzy set (FS) in expressing decision-making information, and in combination with the very extensive use of the Dice similarity measure (DSM), a new MAGDM method is proposed for the optimal CBECP selection (CBECPS) under the probabilistic dual hesitation fuzzy (PDHF) environment. First, on the basis of reviewing a large number of documents on the CBECPS for CBEC, the evaluation index system for the CBECPS is constructed; second, several new DSMs are proposed in the PDHF environment; third, based on the two newly proposed probabilistic dual hesitant weighted generalized Dice similarity measures, two novel MAGDM methods are provided for CBECPS, which are used for CBECPS; finally, the two established MAGDM techniques are compared with the existing decision-making methods, and the parameter analysis is carried out to illustrate the effectiveness and superiority of the two established MAGDM techniques. The two established techniques can not only be used for CBECPS of CBEC, but also be extended to similar related research.

Three-way group decision based on regret theory under dual hesitant fuzzy environment: An application in water supply alternatives selection

For actual multi-criteria group decision-making (MCGDM) issues with complexity and uncertainty, decision-makers may not be completely rational, and the psychological characteristics generally exert influences on the decision process. As an effective cognitive model, three-way decision (TWD) dedicates to handling uncertainty and is widely used in distinct fields. In this regard, this study incorporates regret emotion into TWD in the dual hesitant fuzzy (DHF) context, and accordingly designs a decision model for uncertain MCGDM issues considering bounded rationality. Specifically, by means of distance measures and score functions of dual hesitant fuzzy elements (DHFEs), the calculation method of loss functions and conditional probability are proposed firstly. Meanwhile, drawing from regret theory, the TWD rules considering regret emotion are generated under the DHF environment. Further, based on this, a detailed three-way group decision model based on regret theory, aimed at solving MCGDM problems under DHF uncertainty, water supply alternatives selection, is constructed. Lastly, a numerical example is employed to elaborate the application of the model.

Fuzzy Intelligence Learning Based on Bounded Rationality in IoMT Systems: A Case Study in Parkinson’s Disease

As a cause of interfering with routine activities, freezing of gait (FOG) is a severe syndrome of Parkinson’s disease (PD) and usually performs as an abrupt and momentary inability to effective stepping forward. Advanced wearable acceleration sensors based on socially implemented Internet of medical things (IoMT) devices can remotely provide a platform for recognizing FOG. However, due to the diverse data acquisition modes that appear in classic IoMT devices, the obtained data may contain imprecise, hesitant, and incomplete ones. Meanwhile, the bounded rationality owned by neurologists usually has a big impact on using wearable acceleration sensors to predict illnesses. Therefore, the objective of this article lies in exploring a fuzzy intelligence learning approach based on bounded rationality in IoMT systems and providing a valid scheme for biomedical data analysis. Specifically, a brand-new three-way group decision-making approach by means of TODIM (an acronym in Portuguese for interactive multicriteria decision-making) with incomplete dual hesitant fuzzy (DHF) information and its applications in detecting FOG in PD using IoMT devices are systematically explored. First, taking advantage of DHF sets (DHFSs) when depicting realistic group decision information, the concept of multigranulation (MG) incomplete DHF information systems is built. Second, adjustable MG DHF probabilistic rough sets (PRSs) are further put forward via DHF similarity relations. Third, a three-way group decision-making approach is constructed by virtue of adjustable MG DHF PRSs and TODIM. Finally, the validity, effectiveness, and practicality of the constructed three-way group decision-making approach are investigated by a University of California, Irvine (UCI) dataset with several experimental analyses in the background of FOG detection in PD using IoMT devices. The experimental result indicates that the developed fuzzy intelligence learning approach achieves reasonable diagnostic conclusions for FOG detection in PD from the perspective of uncertain information processing abilities, decision risks, and bounded rationality.

Complex dual hesitant fuzzy TODIM method and their application in Russia–Ukraine war’s impact on global economy

The primary objective of this research is to examine how the Russia–Ukraine war impacted the global economy. To achieve this goal, we propose operational laws, a score and accuracy function, and a divergence measure for complex dual hesitant fuzzy sets. Furthermore, we investigate the challenges associated with multi-criteria decision-making (MCR) using aggregation operators and the TODIM method with complex dual hesitant fuzzy (CDHF) information. Drawing inspiration from arithmetic aggregation operations, we introduce several aggregation operators for complex dual hesitant fuzzy information, including the complex dual hesitant fuzzy weighted average (CDHFWA) operator, complex dual hesitant fuzzy ordered weighted average (CDHFOWA) operator, and complex dual hesitant fuzzy hybrid average (CDHFHA) operator. We thoroughly analyze the unique characteristics of these proposed operators and use them in conjunction with the TODIM method to develop practical approaches for solving complex dual hesitant fuzzy multi-criteria decision-making problems. To validate our approach and demonstrate its applicability, we provide a practical example of how the Russia–Ukraine war affected the global economy. Our study confirms the effectiveness of our approach and highlights how it can be used to solve real-world problems.

Development of dual hesitant fuzzy prioritized operators based on Einstein operations with their application to multi-criteria group decision making

Development of dual hesitant fuzzy prioritized operators based on Einstein operations with their application to multi-criteria group decision making

Interval-valued dual hesitant fuzzy prioritized aggregation operators based on Archimedean t-conorm and t-norm and their applications to multi-criteria decision making

Interval-valued dual hesitant fuzzy prioritized aggregation operators based on Archimedean t-conorm and t-norm and their applications to multi-criteria decision making

Hybridizations of Archimedean copula and generalized MSM operators and their applications in interactive decision-making with q-rung probabilistic dual hesitant fuzzy environment

The q-rung probabilistic dual hesitant fuzzy sets (qRPDHFSs), which outperform dual hesitant fuzzy sets, probabilistic dual hesitant fuzzy sets, and probabilistic dual hesitant Pythagorean fuzzy sets, are used in this research to develop an interactive group decision-making approach. We first suggest the Archimedean Copula-based operations on q-rung probabilistic dual hesitant fuzzy (qRPDHF) components and investigate their key features before constructing the approach. We then create some new aggregation operators (AOs) in light of these operations, including the qRPDHF generalized Maclaurin symmetric mean (MSM) operator, qRPDHF geometric generalized MSM operator, qRPDHF weighted generalized MSM operator, and qRPDHF weighted generalized geometric generalized MSM operator. These aggregation operators are better than current operators on qRPDHF because they can take into account the interactions between a large number of criteria and probability distributions. The evaluation findings are distorted since the present methodologies do not take expert involvement into account in order to achieve the required consistency level. We employ the idea of interaction, consistency, resemblance, and consensus-building among the decision-makers in our method to get around this. We create an optimization model based on the cross-entropy of the qRPDHF components to estimate the weights of the criterion. We provide contextual research on the choice of open-source software LMS in order to demonstrate the relevance of the recommended AOs. Likewise, we ran a sensitivity test on the weights of the criterion to make sure that our model is consistent. The comparison investigation has demonstrated that the suggested approach can overcome the challenges of previous works.

A Dual Hesitant Fuzzy Sets-Based Methodology for Advantage Prioritization of Zero-Emission Last-Mile Delivery Solutions for Sustainable City Logistics

For the first time, the critical worldwide problem of prioritizing zero-emission last-mile delivery (LMD) solutions for sustainable city logistics is addressed and solved in this article. It not only aims to help city logistics companies sustainably decarbonize urban freight distribution but also provide valuable practical guidelines. To evaluate zero-emission LMD solutions, this article presents a novel multicriteria group decision-making methodology with dual hesitant fuzzy (DHF) sets. First, we propose some improved operations on DHF elements and investigate their vital properties. Second, based on these operations, we develop DHF improved weighted averaging operator to overcome the drawbacks of the existing operators on DHF sets. Third, for measuring the weights of criteria, a new model called the cross-entropy-based optimization model (CEBOM) is developed. Fourth, for the rational aggregation of the preferences, we formulate a new method namely score-based double normalized measurement alternatives and ranking according to the compromise solution (SDNMARCOS). The proposed DNMARCOS method couples the linear and vector normalization techniques. It is composed of the complete compensatory model and the incomplete compensatory model. Thus, SDNMARCOS is more robust compared to the available state-of-the-art approaches. To exhibit the applicability of the proposed DHF-CEBOM-SDNMARCOS methodology in real-world settings, a case study for one of the largest Austrian logistics companies in Serbia is provided. The research findings show that electric light commercial vehicles are the best LMD solution. Also, it is recommended to consider electric cargo bikes as a viable mid-term solution. The superiority of the introduced methodology is demonstrated through the comparative investigation.