Fuzzy Linguistic Term Sets Research Articles

Hamacher heronian mean operators for multi-critria decision-making under multi-valued picture fuzzy uncertain lingsuitic environment

Picture fuzzy set (PFS) and linguistic term set (LTS) are two significant notions in multi-criteria decision-making (MCDM). In practice, decision-makers sometimes need utilize the multiple probable membership degrees for an uncertain linguistic term to express evaluation information. Motivated by these, to better convey the vagueness and uncertainty of cognitive information, multi-valued picture fuzzy uncertain linguistic set combining picture hesitant fuzzy set with uncertain linguistic term set is proposed. We firstly define the concepts of multi-valued picture fuzzy uncertain linguistic set and multi-valued picture fuzzy uncertain linguistic number. Hamacher operations are more general and flexible in information fusion, thus, Hamacher operations and comparison method are developed at the same time. Improved generalized Heronian Mean operator can simultaneously reflect correlations between values and prevent the redundant calculation. Then, two operators of improved generalized weighted Heronian mean and improved generalized geometric weighted Heronian mean in view of Hamacher operations are proposed. Meanwhile, some distinguished properties and instances of two operators are explored as well. Moreover, a novel MCDM approach applying the developed operators is constructed. Ultimately, an illustrative example on vendor selection is performed, and sensitivity analysis and comparison analysis are provided to verify the powerfulness of the proposed method.

A new linguistic decision making method—FLM-VIKOR

Fuzzy linguistic term set is a powerful and flexible tool to express evaluation of experts in decision making. In this paper, we propose a VIKOR method based on fuzzy linguistic multiple set to solve the fuzzy linguistic multiset multi-criteria group decision making. Firstly, we develop the conception of fuzzy linguistic multiset, its correlate properties and operations. Then, we aggregate all decision matrices of all experts by these operations, obtaining final decision making matrix. Moreover, we develop the distance of two fuzzy linguistic multisets. Lastly, an illustrative example is given to testify the effectiveness of the developed method.

A prospective multi attribute decision making based reliability allocation method using fuzzy linguistic approach and minimum effort function

Reliability allocation has a great importance in the early design stage of a system. The result of this allocation directly affects the product's quality and robustness of the system. There are several reliability allocation techniques to allocate the target reliability. But these methods have several shortcomings. To overcome these shortcomings, a new allocation method considering future limiters depending upon the concept of prospective multi-attribute decision making (PMADM) has been proposed here. Also the fuzzy linguistic term sets, minimal variance OWGA weights and experts degree of orness have been combined to perform a more practical reliability allocation. Ultimately to recognise the nonlinear phenomenon for reliability and the element's potential for improvement, the proposed method takes an initiative to improve the initial target reliability using reliability growth effort function. An example of transceiver system is considered here to illustrate the efficiency and flexibility of the proposed approach to allocate the system reliability.

Hesitant Fuzzy Linguistic Aggregation Operators Based on the Hamacher t-norm and t-conorm

Hesitant fuzzy linguistic (HFL) term set, as a very flexible tool to represent the judgments of decision makers, has attracted the attention of many researchers. In recent years, some HFL aggregation operators have been developed to aggregate the HFL information. However, most of these operators are proposed based on the Algebraic product and Algebraic sum. In this paper, we presented some HFL aggregation operators to handle HFL information based on Hamacher triangle norms. We first define new operational laws on the HFL element according to Hamacher triangle norms. Then we present a family of HFL Hamacher aggregation operators, including the HFL Hamacher weighted averaging, HFL Hamacher weighted geometric, HFL Hamacher power weighted averaging and HFL Hamacher power weighted geometric operators and their generalized forms. We also investigate some special cases and properties of these operators in detail. Furthermore, we develop two approaches based on the proposed operators to deal with the multi-criteria decision-making problem with HFL information. Finally, a numerical example with regard to choosing a suitable city to release sharing car is provided to illustrate the feasibility of the proposed method, and the advantages of the proposed methods are shown by conducting a sensitivity and comparative analysis.

AN EFFICIENT FUZZY COMPUTATIONAL FRAMEWORK FOR CUSTOMER SEGMENTATION MODEL IN CREDIT ANALYSIS

In this paper we propose an efficient fuzzy computational framework for customer segmentation model in credit analysis. Normally segmentation methods cannot perform complex analysis so as to obtain the customer segments with high value. If the knowledge of experts in the data domain can be imparted to the generation of segments, this can bring in better results in the performance of classification models. In our approach customer attributes are selected after knowledge experts analysis and are segmented based on the limits set by them on the real numerical values. For each segment we generate the segmentation rules with definition of fuzzy basic linguistic term set. Each linguistic term set is assigned to a fuzzy membership function to generate the segmentation function. Combining the segmentation rules and generated functions the real valued numerical attributes are converted to fuzzified values in the interval [0, 1]. Both linguistic and numeric information are aggregated by a series of computations and a 2-tuple linguistic value is generated for each attribute in the database. The same term after a series of computations can be used in many decision making problems as it suffers no loss of information.

Risk analysis of chemical industry based on group consensus decision making method

As chemical industry has developed rapidly, some problems for our society have appeared, especially the problems related to chemical risk management. Because density of dangerous chemical products is relative high and components of chemical products are relative complex, it may increase the probability that dangerous chemical risk happens. Thus, for chemical enterprises, they not only need to improve economical level, but also need to control risk management. Meanwhile, although the existing studies have been focused on risk management of chemical industry, there are still some problems such as a rational method to assess chemical risk. In order to overcome these problems, this paper develops a new chemical risk assessment method based on group decision making method by considering group consensus of all chemical experts according to an advanced threshold. 2-tuple fuzzy linguistic term set is introduced to help the chemical experts express their risk assessments and final result analysis demonstrates that the proposed method is applicability in chemical risk assessment.