Interval-valued Intuitionistic Hesitant Fuzzy Set Research Articles

Avenues of decarbonisation in the dynamics of processed food supply chains: Towards responsible production consumption

Nowadays, the demand for processed food items is surging. To fulfil the enhanced demand, a significant impact is laid on the environment, which enhances the carbon footprint being generated. Hence, to overcome this, the avenues of decarbonisation need to be explored. The presented work is aimed at promoting the decarbonisation of the existing practices within the processed food supply chains. It finds strong compliance with the sustainable development goal (SDG-12), focusing on responsible production-consumption mechanisms. For the same, key enactors of decarbonisation are identified and mapped with the practices at various stages of food supply chains, i.e. upstream, downstream, and other allied practices. Based upon these enactors, a relational, hierarchical framework is developed to provide a comprehensive perspective on complex intricacies. This framework is analysed with an innovative approach which comprises the fundamentals of Interval-Valued Intuitionistic Hesitant Fuzzy Set with the Entropy measures. It results in the outranking of the enactors relative to its importance in the decarbonisation of processed food supply chains. Furthermore, the empirical findings are validated by the sensitivity analysis to felicitate robust decision-making. The outcomes of the presented work provide a roadmap and stepped approach to achieve the decarbonisation goals and make production-consumption mechanisms sustainable. It finds implications in the development of the framework, policy formulation, and decisional attributes for the decarbonisation of food supply chains. It focuses on the adoption of strategies that align with global efforts to mitigate climate change and promote a sustainable future.

Multi-attribute decision-making based on dual probabilistic interval-valued intuitionistic hesitant fuzzy weighted heronian mean aggregation operator

Probabilistic interval-valued intuitionistic hesitant fuzzy sets (PIVIHFSs) can well describe the evaluation information of decision-makers (DMs) in multi-attribute decision-making (MADM) problems. However, PIVIHFSs only depict the situation where both membership and non-membership information occur with equal probability while ignoring the situations of non-equal possibility due to DMs’ subjective preferences. In this paper, we develop dual probabilistic interval-valued intuitionistic hesitant fuzzy sets (DPIVIHFSs) concept based on the truncated normal distribution. The DPIVIHFSs overcome the shortcomings of PIVIHFSs and are more interpretable. Then, the operations and ranking method of DPIVIHFSs are introduced. Furthermore, we study MADM methods in dual probabilistic interval-valued intuitionistic hesitant fuzzy environments by aggregation operators (AOs). We propose a series of AOs including the DPIVIHF heronian mean (DPIVIHFHM) operator and the DPIVIHF weighted heronian mean (DPIVIHFWHM) operator. The basic properties of the presented are discussed and proved. Finally, a novel method for solving the MADM problem is proposed based on the DPIVIHFWHM operator and a numerical example of express company selection strategy is used to illustrate the effectiveness of the method. The proposed method in this article can capture more fuzzy and uncertain information when solving MADM problems and have a wider application range.

A data-driven group emergency decision-making method based on interval-valued intuitionistic hesitant fuzzy sets and its application in COVID-19 pandemic

The outbreak of Corona Virus Disease 2019 (COVID-19) makes people more concerned about the validity and timeliness of emergency decision making. When an emergency occurs, it is difficult for decision makers (DMs) to give accurate assessment information in the early stage due to the urgency of time, the incompleteness of information, and the limitations of DMs’ cognition and knowledge. Hence, we use interval-valued intuitionistic hesitant fuzzy sets rather than exact numbers to better characterize the fuzziness and uncertainty of emergencies. In addition, the Internet has become a major platform for the public to express their opinions or concerns, so we can collect the user-generated content on social media to help DMs determine appropriate emergency decision-making criteria which are the premise and basis of scientific decisions. However, there is likely to be some correlation between the obtained criteria. To this end, we first extend the Bonferroni mean (BM) operator to the interval-valued intuitionistic hesitant fuzzy environment, and propose three interval-valued intuitionistic hesitant fuzzy BM operators to capture the interrelation of fuzzy input variables, including an interval-valued intuitionistic hesitant fuzzy BM operator, a simplified interval-valued intuitionistic hesitant fuzzy BM operator, and a simplified interval-valued intuitionistic hesitant fuzzy weighted BM (SIVIHFWBM) operator. Then, a new group emergency decision-making method based on the SIVIHFWBM operator and social media data is proposed, and the specific steps of ranking all emergency plans are put forward. Moreover, our method is applied to evaluate emergency plans for the prevention and control of COVID-19. Finally, the effectiveness and feasibility of the method are verified by the sensitivity analysis, validity test, and comparative analysis.

Interval Valued q- Rung Orthopair Hesitant Fuzzy Choquet Aggregating Operators in Multi-Criteria Decision Making Problems

Abstract: In this paper, we introduce Interval valued q- Rung Orthopair Hesitant fuzzy sets (IVq-ROHFS) with motivation of Interval valued pythagorean Hesitant fuzzy sets [44] as a new concept. Then, we give some basic operations as complement, union, intersection, addition, scalar multiplication, scalar power. Also, we combine to (IVq-ROHFS) and choquet integral , together with aggregating operators, and develop to Interval valued q- rung orthopair hesitant fuzzy Choquet averaging operator (IVq-ROHCA) and Interval valued q- rung orthopair hesitant fuzzy Choquet geometric operator (IVq-ROHCG). Then, we offer to indicate soft approach of proposed IVq-ROHCA and IVq-ROHCG an example adopted from Interval-valued intuitionistic hesitant fuzzy Choquet integral based TOPSIS (IVIHCI) [43]. The obtained results are agreement with IVIHCI but presented IVq-ROHCA and IVq-ROHCG have more advantages than existing structures as Interval-valued intuitionistic hesitant fuzzy sets (IVIHFS), interval-valued Pythagorean Hesitant fuzzy sets (IVPHFS) with reasons changing according to need, requirement, prefer of decision makers and moreover, being IVIHFS and IVPHFS are special cases of IVq-ROHFS. It is open from comparative analysis that the while some of offered approaches are giving no solution for some values, our operators present to needed results.

An Interval-Valued Intuitionistic Hesitant Fuzzy Methodology and Application.

Using advantages of interval-valued intuitionistic hesitant fuzzy sets (IVIHFS) for describing the hesitant and intuitionistic decisions of experts and identifying the limitations of previous research works about optimization techniques, this paper introduces a new optimization technique and provides a new computational algorithm, applicable in various real life multiobjective optimization problem (MOOP) of engineering and management sectors, and for this, a new operation between IVIHFSs is first introduced. On the basis of this concept, a stepwise computational algorithm is constructed, and it is an extension of both fuzzy and intuitionistic fuzzy optimization techniques. Finally, the proposed algorithm is illustrated using a production planning problem, and the obtained results are compared with the existing optimization techniques.

Generalized interval-valued hesitant intuitionistic fuzzy soft sets

This paper aims to extend the Interval-valued Intuitionistic Hesitant Fuzzy Set to a Generalized Interval-valued Hesitant Intuitionistic Fuzzy Soft Set (GIVHIFSS). Definition of a GIVHIFSS and some of their operations are defined, and some of their properties are studied. In these GIVHIFSSs, the authors have defined complement, null, and absolute. Soft binary operations like operations union, intersection, a subset are also defined. Here is also verified De Morgan’s laws and the algebraic structure of GIVHIFSSs. Finally, by using the comparison table, a different approach to GIVHIFSS based decision-making is presented.

Analysis of Electricity Pricing in Emerging Economies With Hybrid Multi-Criteria Decision-Making Technique Based on Interval-Valued Intuitionistic Hesitant Fuzzy Sets

This study aims to analyze the factors that influence electricity prices. For this purpose, a hybrid multi-criteria decision-making (MCDM) model based on interval-valued intuitionistic hesitant fuzzy (IVIHF) sets is proposed. Firstly, a large literature review is carried out and 10 different factors that can affect electricity prices are determined. After that, IVIHF decision making trial and evaluation laboratory (DEMATEL) methodology is considered to find which factors are more significant in electricity prices. The finding shows that inflation rate and technological improvement are the most important criteria that affect electricity prices. Thus, the countries should consider expected future inflation rates to take necessary precautions to minimize volatility in electricity prices. Moreover, countries should follow current technologies regularly and make effective research and development to provide electricity with lower prices. In the second stage of the analysis, emerging 7 (E7) economies are ranked with respect to the performance related to the management of electricity price volatility. In this context, IVIHF VIšeKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approach is considered. It is concluded that Russia and China are the most successful countries in keeping electricity prices away from volatility whereas India and Turkey get the latest order. Furthermore, a comparative evaluation is also implemented by considering IVIHF technique for order preference by similarity to ideal solution (TOPSIS) methodology to check the consistency of the analysis results. The results of both approaches are quite similar which gives information about the consistency of the ranking results. On the other hand, a sensitivity analysis is performed to ten different cases consecutively. It is determined that the ranking results are coherent by considering the changes in the criteria weights.

Multiple attribute decision making based on probabilistic interval-valued intuitionistic hesitant fuzzy set and extended TOPSIS method

Multiple attribute decision making based on probabilistic interval-valued intuitionistic hesitant fuzzy set and extended TOPSIS method

Interval-valued intuitionistic hesitant fuzzy entropy based VIKOR method for industrial robots selection

In this paper, we proposed interval valued intuitionistic hesitant fuzzy entropy for determine the importance of the criteria and interval valued intuitionistic hesitant fuzzy VIKOR method for ranking the alternatives. Industrial Robots are utilized to perform complicated and hazardous tasks accurately and also used to enhance the quality and efficiency of the work. Selecting an industrial robot for performing a particular task depends on the work and the associated criteria of the robot. The materials handled by the robots are different like powdered, adhesive, bulky, brittle etc. In this manner, choosing a suitable robot from the set of available industrial robots to handle a particular material is a challenging task. To get a more conscionable decision result, a decision organization contains a lot of decision makers. The interval- valued intuitionistic hesitant fuzzy set is utilized as a competent mathematical tool for enunciate individuals hesitant thinking. An interval- valued intuitionistic hesitant fuzzy set (IVIHFS) concedes a set of several possible interval- valued intuitionistic fuzzy membership and non- membership values. Finally, the proposed interval- valued intuitionistic hesitant fuzzy entropy and VIKOR techniques utilized for industrial robot selection.

Measures of Probabilistic Interval-Valued Intuitionistic Hesitant Fuzzy Sets and the Application in Reducing Excessive Medical Examinations

Distance and similarity measures act as the links from the expression of fuzzy information to various application fields such as decision making, cluster analysis, medical diagnosis, and so on. Some scholars have paid attention to improving the approaches of defining the distance and similarity measures. One of the most common practical ways to update the measures is to add an item about hesitancy into the traditional definitions of them. This way is very effective to promote the performances of decision making. However, there are still some drawbacks in the existing distance and similarity measures even though some of them have contained the hesitant information. In this paper, we first define a probabilistic interval-valued intuitionistic hesitant fuzzy set (PIVIHFS) as an extended mathematical expression of fuzzy sets. Afterward, the axiomatic concepts are given for the distance, similarity, and entropy measures of PIVIHFSs. Then, we investigate the relationships among the new distance, similarity, and entropy measures. Besides, we establish some new measure models deduced by the axiomatic concepts of PIVIHFSs. After that, we compare the performances of the new distance measure models and several related existing ones, such as Hamming distance measure and Euclidean distance measure, so do the similarity measures. Finally, we illustrate their applicability concerning the medical diagnosis, which can be used to reduce unnecessary medical examinations.

Entropy of interval-valued intuitionistic hesitant fuzzy set and its application to group decision making problems

Very often, there exist some situations with a high degree of uncertainty and fuzziness when a group of decision makers are not agreeing on common membership and non-membership values. In such cases, interval-valued intuitionistic hesitant fuzzy set (IVIHFS) is useful in multi-criteria group decision making (MCGDM) problem to represent the assessments of decision criteria. Entropy has been an important and useful tool for MCGDM methods in measuring fuzziness in information and to quantify the uncertain information. In the present study, we have defined entropy measures for IVIHFS along with a novel approach to aggregate interval valued hesitant fuzzy (IVHF) information into interval valued intuitionistic hesitant fuzzy (IVIHF) information. To do this we have defined satisfactory and dissatisfactory intervals in the present study. Proposed aggregation method is used in expansion of a MCGDM in which criteria weights are determined using proposed IVIHF entropy measure. A descriptive example of candidate selection problem is taken in the study to explain methodology of proposed MCGDM method. A comparison analysis is also performed with validity test to validate the performance of proposed IVIHF entropy based MCGDM method.

Interval-valued intuitionistic hesitant fuzzy Choquet integral based TOPSIS method for multi-criteria group decision making

Inter-dependency among the decision criteria and difficulty of establishing a common membership grade are two important issues to be addressed in multi-criteria group decision making (MCGDM) problems in the environment of uncertainty. The main purpose of this paper is to define the Choquet integral operator for interval-valued intuitionistic hesitant fuzzy sets (IVIHFS) and to extend the technique for order preference by similarity to ideal solution (TOPSIS) method using Choquet integral operator in interval-valued intuitionistic hesitant fuzzy environment. In the present study we define interval-valued intuitionistic hesitant fuzzy Choquet integral (IVIHFCI) operator and extend the definition of hamming distance for the elements of IVIHFS. Using IVIHFCI operator and hamming distance for IVIHFS, we extend TOPSIS method for MCGDM for interval-valued intuitionistic hesitant fuzzy environment considering the interaction phenomena among the decision criteria. An illustrative example has also been taken in the present study to understand the proposed method.

New Operations over Interval Valued Intuitionistic Hesitant Fuzzy Set

Hesitancy is the most common problem in decision making, for which hesitant fuzzy set can be considered as a useful tool allowing several possible degrees of membership of an element to a set. Recently, another suitable means were defined by Zhiming Zhang [1], called interval valued intuitionistic hesitant fuzzy sets, dealing with uncertainty and vagueness, and which is more powerful than the hesitant fuzzy sets. In this paper, four new operations are introduced on interval-valued intuitionistic hesitant fuzzy sets and several important properties are also studied.

New Operations over Interval Valued Intuitionistic Hesitant Fuzzy Set

Hesitancy is the most common problem in decision making, for which hesitant fuzzy set can be considered as a useful tool allowing several possible degrees of membership of an element to a set. Recently, another suitable means were defined by Zhiming Zhang (1), called interval valued intuitionistic hesitant fuzzy sets, dealing with uncertainty and vagueness, and which is more powerful than the hesitant fuzzy sets. In this paper, four new operations are introduced on interval-valued intuitionistic hesitant fuzzy sets and several important properties are also studied.

Interval-Valued Intuitionistic Hesitant Fuzzy Aggregation Operators and Their Application in Group Decision-Making

Hesitant fuzzy sets, permitting the membership of an element to be a set of several possible values, can be used as an efficient mathematical tool for modelling people’s hesitancy in daily life. In this paper, we extend the hesitant fuzzy set to interval-valued intuitionistic fuzzy environments and propose the concept of interval-valued intuitionistic hesitant fuzzy set, which allows the membership of an element to be a set of several possible interval-valued intuitionistic fuzzy numbers. The aim of this paper is to develop a series of aggregation operators for interval-valued intuitionistic hesitant fuzzy information. Then, some desired properties of the developed operators are studied, and the relationships among these operators are discussed. Furthermore, we apply these aggregation operators to develop an approach to multiple attribute group decision-making with interval-valued intuitionistic hesitant fuzzy information. Finally, a numerical example is provided to illustrate the application of the developed approach.