A Distance-based Method for Computing Priorities of Intuitionistic Fuzzy Preference Relation and Its Application in AHP

2019 ◽  
Vol 23 (4) ◽  
pp. 329-340
Author(s):  
Akshay Hinduja ◽  
Manju Pandey
Keyword(s):  
Fuzzy Set ◽  
Fuzzy Ahp ◽  
Preference Relation ◽  
Intuitionistic Fuzzy Set ◽  
Analytic Hierarchy ◽  
Intuitionistic Fuzzy ◽  
Course Of Action ◽  
Fuzzy Preference Relations ◽  
Priority Method ◽  
Fuzzy Preference

The notion of multi-criteria decision-making is regarded as the process of finding the best possible alternative or course of action by decision-makers. Often, it entails handling vague, incomplete and inconsistent information. The intuitionistic fuzzy set (IFS) has been proven more effective than a fuzzy set in handling vagueness and uncertainty. The aim of the article is to incorporate the effectiveness of the IFS with the powerfulness of the analytic hierarchy process (AHP) and develop intuitionistic fuzzy AHP (IF-AHP) to cope with the decision problems involving imprecise and hesitant information. In this article, we develop a distance-based novel priority method, which derives unambiguous non-fuzzy priorities of the alternatives from intuitionistic fuzzy preference relations (IFPRs). The proposed priority method is simple in computation yet effective in results. To validate the method, we applied it to the adapted supplier selection problem. This article also presents a comparison of the proposed method with classic and fuzzy AHP using Monte-Carlo simulation approach.

An integrated dynamic intuitionistic fuzzy MADM approach for personnel promotion problem

Kybernetes ◽  
2015 ◽  
Vol 44 (10) ◽  
pp. 1422-1436 ◽  
Author(s):  
Ozkan Bali ◽  
Metin Dagdeviren ◽  
Serkan Gumus
Keyword(s):  
Fuzzy Set ◽  
Success Factors ◽  
Intuitionistic Fuzzy Set ◽  
Senior Managers ◽  
Analytic Hierarchy ◽  
Content Type ◽  
Dynamic Evaluation ◽  
Intuitionistic Fuzzy ◽  
Qualified Personnel ◽  
Fuzzy Madm

Purpose – One of the key success factors for an organization is the promotion of qualified personnel for vacant positions. Especially, the promotion of middle and senior managers play an important role in terms of organization’s success. In personnel promotion problem in which the candidates are nominated within the organization and they have been working for a specific period of time and are known in their organization, the candidates should be evaluated based on their recent as well as past performances to make right selection for the vacant position. For this reason, the purpose of this paper is to propose an integrated dynamic multi-attribute decision-making (MADM) model based on intuitionistic fuzzy set for solving personnel promotion problem. Design/methodology/approach – The proposed model integrates analytic hierarchy process (AHP) technique and the dynamic evaluation by intuitionistic fuzzy operator for personnel promotion. AHP is employed to determine the weight of attributes based on decision maker’s opinions, and the dynamic operator is utilized to aggregate evaluations of candidates for different years. Atanassov’s intuitionistic fuzzy set theory is utilized to represent uncertainty and vagueness in MADM process. Findings – A numerical example is presented to show the applicability of the proposed method for personnel promotion problem and a sensitivity analysis is conducted to demonstrate efficiency of dynamic evaluation. The findings indicate that the varying weights of years employed determined the best candidate for promotion. Originality/value – The novelty of this study is defining personnel promotion as a MADM problem in the literature for the first time and proposing an integrated dynamic intuitionistic fuzzy MADM approach for the solution, in which the candidates are evaluated at different years.

ON ADDITIVE CONSISTENT PROPERTIES OF THE INTUITIONISTIC FUZZY PREFERENCE RELATION

2010 ◽  
Vol 09 (06) ◽  
pp. 1009-1025 ◽  
Author(s):  
ZAIWU GONG ◽  
LIANSHUI LI ◽  
JIE CAO ◽  
FEIXUE ZHOU
Keyword(s):  
Preference Relation ◽  
Fundamental Principle ◽  
Human Judgment ◽  
Preference Relations ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Preference Relations ◽  
Weak Transitivity ◽  
Consistency Properties ◽  
Intuitionistic Fuzzy Preference Relation ◽  
Fuzzy Preference

We investigate the properties of additive consistent intuitionistic fuzzy preference relations (IFPR). Usually, consistency in fuzzy preference relations (FPR) is associated with transitivity such as general transitivity, weak transitivity, and restricted max–max transitivity. This paper extends the consistency properties of the FPR to those of the IFPR. Since weak transitivity is the minimal logical requirement and a fundamental principle of human judgment, this paper develops three determination theorems and the corresponding algorithms to judge the weak transitivity of an IFPR from different angles. Two numerical examples show that the three methods proposed are feasible and effective.

scholarly journals Complete Z-intuitionistic Fuzzy MULTIMOORA Method with AHP and its Application to COVID-19

2021 ◽  
Author(s):  
Yijin Zhang ◽  
Jie Huang ◽  
Zongbing Lin
Keyword(s):  
Fuzzy Set ◽  
Distance Measure ◽  
Intuitionistic Fuzzy Set ◽  
Uncertain Information ◽  
Analytic Hierarchy ◽  
Intuitionistic Fuzzy ◽  
Lack Of Information ◽  
Multicriteria Group Decision Making ◽  
Ranking Aggregation ◽  
Multimoora Method

Abstract For emergencies, the reliability of information can not be guaranteed. At the same time, due to the lack of information and knowledge, neither the criteria itself nor the credibility can be given a precise evaluation by decision-makers(DMs). Therefore, we combine intuitionistic fuzzy set and Z-number to get a new class of fuzzy set, complete Z-intuitionistic fuzzy set(CZIFS), and its degenerate form, A-type Z-intuitionistic fuzzy set(AZIFS) and B-type Z-intuitionistic fuzzy set(BZIFS). CZIFS can serve as a reliable tool to depict the hesitant degree both on the ambiguity and reliability of uncertain information. In addition, we introduce the score and accuracy functions and distance measure of complete Z-intuitionistic fuzzy number(CZIFN), with which we have considered both reliability information and DMs' preference on it. Then, we improve traditional MULTIMOORA by developing reference point(RP) model to consider both the risk and profile of alternatives and integrating analytic hierarchy process(AHP) in the process of ranking aggregation method to take into account the preference of DMs on three subordinate rankings. Besides, to solve multicriteria group decision making(MCGDM) problem, we develop improved MULTIMOORA method to the environment of CZIFN. Finally, to illustrate the proposed method, we give a numerical example, solving site selecting of Fangcang shelter hospital for COVID-19.

Decision making based on intuitionistic fuzzy preference relations with additive approximate consistency

2020 ◽  
Vol 39 (3) ◽  
pp. 4041-4058
Author(s):  
Fang Liu ◽  
Xu Tan ◽  
Hui Yang ◽  
Hui Zhao
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Real Interval ◽  
Preference Relations ◽  
Intuitionistic Fuzzy ◽  
Proposed Model ◽  
Fuzzy Preference Relations ◽  
Novel Concept ◽  
Fuzzy Weights ◽  
Fuzzy Preference

Intuitionistic fuzzy preference relations (IFPRs) have the natural ability to reflect the positive, the negative and the non-determinative judgements of decision makers. A decision making model is proposed by considering the inherent property of IFPRs in this study, where the main novelty comes with the introduction of the concept of additive approximate consistency. First, the consistency definitions of IFPRs are reviewed and the underlying ideas are analyzed. Second, by considering the allocation of the non-determinacy degree of decision makers’ opinions, the novel concept of approximate consistency for IFPRs is proposed. Then the additive approximate consistency of IFPRs is defined and the properties are studied. Third, the priorities of alternatives are derived from IFPRs with additive approximate consistency by considering the effects of the permutations of alternatives and the allocation of the non-determinacy degree. The rankings of alternatives based on real, interval and intuitionistic fuzzy weights are investigated, respectively. Finally, some comparisons are reported by carrying out numerical examples to show the novelty and advantage of the proposed model. It is found that the proposed model can offer various decision schemes due to the allocation of the non-determinacy degree of IFPRs.

The prediction of online time series with concept drift based on dynamic intuitionistic fuzzy cognitive map

2021 ◽  
Vol 25 (4) ◽  
pp. 949-972
Author(s):  
Nannan Zhang ◽  
Xixi Yao ◽  
Chao Luo
Keyword(s):  
Fuzzy Set ◽  
Sequence Data ◽  
Concept Drift ◽  
Intuitionistic Fuzzy Set ◽  
Cognitive Map ◽  
Fuzzy Cognitive Maps ◽  
Fuzzy Cognitive Map ◽  
Online Data ◽  
Intuitionistic Fuzzy ◽  
Public Data

Fuzzy cognitive maps (FCMs) have widely been applied for knowledge representation and reasoning. However, in real life, reasoning is always accompanied with hesitation, which is deriving from the uncertainty and fuzziness. Especially, when processing the online data, since the internal and external interference, the distribution and characteristics of sequence data would be considerably changed along with the passage of time, which further increase the difficulty of modeling. In this article, based on intuitionistic fuzzy set theory, a new dynamic intuitionistic fuzzy cognitive map (DIFCM) scheme is proposed for online data prediction. Combined with a novel detection algorithm of concept drift, the structure of DIFCM can be adaptively updated with the online learning scheme, which can effectively improve the representation of online information by capturing the real-time changes of sequence data. Moreover, in order to tackle with the possible hesitancy in the process of modeling, intuitionistic fuzzy set is applied in the construction of dynamic FCM, where hesitation degree as a quantitative index explicitly expresses the hesitancy. Finally, a series of experiments using public data sets verify the effectiveness of the proposed method.

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