Economics and Information Theory

As a popular tool for modeling the qualitative assessment information, the hesitant fuzzy linguistic term sets (HFLTSs) can allow the decision makes or experts to give several possible linguistic terms to rate the objects with respect to the criterion. Although there exist many multi-criteria decision-making methods put forward for handling the HFLTSs, they were developed based on the assumption that the decision makers can always provide completely rational assessments and they do not take the decision makers’ psychological behaviors into consideration. In this paper, the traditional TODIM (an acronym in Portuguese for interactive multi-criteria decision making) method is extended to handle the HFLTSs based on the novel comparison function and distance measure. Firstly, we put forward a novel function for comparing two HFLTSs more effectively. After that, a novel hesitance degree function as well as some novel distance measures are given for HFLTSs. Then we apply them to extend the traditional TODIM method for solving the HFLTSs. Finally, a practical example concerning the evaluation and ranking of several satellite launching centers is provided to illustrate the validity and applicability of the proposed method.

TODIM-based multi-criteria decision-making method with hesitant fuzzy linguistic term sets

The Maclaurin symmetric mean (MSM) and dual Maclaurin symmetric mean (DMSM) operators are two aggregation operators to aggregate the q-rung orthopair fuzzy numbers (q-ROFNs) into a single element. The complex q-rung orthopair fuzzy framework is more effective to describe fuzzy information in real decision-making problems. The complex q-rung orthopair fuzzy sets (Cq-ROFSs) are more superior to the complex intuitionistic fuzzy sets (CIFSs) and complex pythagorean fuzzy sets (CPFSs) to cope with uncertain and difficult information in the environment of fuzzy set theory. Thus, this manuscript proposes some complex q-rung orthopair fuzzy Maclaurin symmetric mean (Cq-ROFMSM), complex q-rung orthopair fuzzy weighted Maclaurin symmetric mean (Cq-ROFWMSM), complex q-rung orthopair fuzzy dual Maclaurin symmetric mean (Cq-ROFDMSM) and complex q-rung orthopair fuzzy weighted dual Maclaurin symmetric mean (Cq-ROFWDMSM) operators. Moreover, some properties and special cases of our proposed methods are also introduced. Then we present a multi-attributive group decision-making based on proposed methods. Further, a numerical example is provided to illustrate the flexibility and accuracy of the proposed operators. Last, the proposed methods are compared with existing methods to examine the best emerging technology enterprises.

Maclaurin symmetric mean operators and their applications in the environment of complex q-rung orthopair fuzzy sets

Nowadays, companies have to improve their practices in the management of green supply chain with increased awareness of environmental issues worldwide. Selecting the optimum green supplier is crucial for green supply chain management, which is a challenging multi-criteria decision making (MCDM) problem.

/pdf/green-supplier-evaluation-and-selection-using-cloud-model-32anzel6fw.pdf

Green Supplier Evaluation and Selection Using Cloud Model Theory and the QUALIFLEX Method

Dempster-Shafer evidence theory has wide applications in many fields. Recently, A new entropy called Deng entropy was proposed in evidence theory. Some scholars have pointed out that Deng Entropy does not satisfy the additivity in uncertain measurements. However, irreducibility may have a huge effect. The derived entropy from complex systems is often irreducible. Inspired by this, generalized belief entropy is proposed. The belief entropy implies the relationship between Deng entropy, Renyi entropy, Tsallis entropy. In addition, numerical examples demonstrate the flexibility of the proposed Renyi-Deng (R-D) entropy to measure the uncertainty of basic probability assignment (BPA). Finally, a method for identifying contradictory evidence based on Renyi-Deng (R-D) entropy is proposed. The experiment show the effectiveness of the proposed method.

/pdf/generalized-belief-entropy-and-its-application-in-7dxgyhwqi5.pdf

Generalized Belief Entropy and Its Application in Identifying Conflict Evidence

In this communication, a new two parametric intuitionistic fuzzy entropy has been introduced and validated. Some of the properties of the proposed measure are also discussed. In addition to this, a new multiple attribute decision-making (MADM) method based on weighted correlation coefficients and the proposed IF entropy is introduced. The necessity of proposed MADM method has been reasonably established. The method is effectively explained with the help of two numerical examples, and the performance is genuinely compared with some existing MADM methods in the literature. Attribute weights play an important role in the solution of MADM problem. In this paper, two methods of obtaining attributes weights are discussed.

A Novel Fuzzy Decision-Making Method Using Entropy Weights-Based Correlation Coefficients Under Intuitionistic Fuzzy Environment

In the present communication, we have proposed a new dissimilarity measure based on Jensen inequality and α-norm divergence measure proposed by Boekee and Lubbe. Besides proving some of its major properties, the performance of the proposed dissimilarity measure is genuinely compared with some other existing measures in the literature through an example based on pattern recognition. Further, a method to solve multiple attribute decision making (MADM) problems based on the proposed dissimilarity measure is given and its performance is compared with some other well-known MADM methods.

A Jensen- α -Norm Dissimilarity Measure for Intuitionistic Fuzzy Sets and Its Applications in Multiple Attribute Decision Making

In this paper, we introduce a quantity measure which is called (R, S)-norm entropy and discuss some of its major properties with Shannon’s and other entropies in the literature. Based on this (R, S)-norm entropy, we have proposed a new (R, S)-norm fuzzy information measure and discussed its validity and properties. Further, we have given its comparison with other fuzzy information measures to prove its effectiveness. Attribute weights play an important role in multiple-attribute decision-making problems. In the present communication, two methods of determining the attribute weights are introduced. First is the case when the information regarding attribute weights is incompletely known or completely unknown and second is when we have partial information about attribute weights. For the first case, the extension of ordinary entropy weight method is used to calculate attribute weights and minimum entropy principle method based on solving a linear programming model is used in the second case. Finally, two methods are explained through numerical examples.

An ( R , S )-norm fuzzy information measure with its applications in multiple-attribute decision-making

Intuitionistic Fuzzy Sets (IFSs) introduced by Atanassov are well suitable to deal with hesitancy and vagueness. In this communication, a new bi-parametric exponential information measure based on IFSs is introduced. Besides establishing its validity, some of its major properties are also discussed. Further, a new multi-criteria decision-making method based on the proposed IF measure and weighted correlation coefficients is introduced. The proposed method is utilized in detecting the fault in a machine that is not working properly through a numerical example.

A new multi-criteria decision-making method based on intuitionistic fuzzy information and its application to fault detection in a machine

Role of intuitionistic fuzzy sets (IFSs) in solving multiple attribute decision making (MADM) problem has been established by many researchers. Depending upon the situation, we need different types of entropies based on IFSs. In this paper, we have proposed a new two parametric intuitionistic fuzzy entropy in the settings of IFSs theory. Besides proving some major properties, the proposed entropy is genuinely compared with some other existing entropies in literature. Based on the proposed entropy, a new method for solving MADM problems is introduced. Attribute weights play an eminent role in solving MADM problems. In this communication, two methods of determining the attribute weights are discussed. First is when information about attributes is completely unknown and second is when we have partial information regarding attribute weights. The two methods are effectively explained with the help of three examples. The attribute weights identification based on the proposed intuitionistic fuzzy entropy is offered in context of IFSs.

Rajesh Joshi

Papers

A Novel Fuzzy Decision-Making Method Using Entropy Weights-Based Correlation Coefficients Under Intuitionistic Fuzzy Environment

A Jensen- α -Norm Dissimilarity Measure for Intuitionistic Fuzzy Sets and Its Applications in Multiple Attribute Decision Making

An ( R , S )-norm fuzzy information measure with its applications in multiple-attribute decision-making

A new multi-criteria decision-making method based on intuitionistic fuzzy information and its application to fault detection in a machine

A New Parametric Intuitionistic Fuzzy Entropy and its Applications in Multiple Attribute Decision Making