Interval-Valued Intuitionistic Fuzzy Cognitive Maps for Supplier Selection

2017 ◽  
pp. 207-217 ◽  
Author(s):  
Petr Hajek ◽  
Ondrej Prochazka
Keyword(s):  
Supplier Selection ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Intuitionistic Fuzzy ◽  
Interval Valued

Interval-valued intuitionistic fuzzy multiple attribute decision making and their applications

2021 ◽  
Vol 25 (2) ◽  
pp. 251-277
Author(s):  
Hong-Jun Wang
Keyword(s):  
Supplier Selection ◽  
Fuzzy Numbers ◽  
Multiple Attribute Decision Making ◽  
Green Supply Chain Management ◽  
Green Supply Chain ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy ◽  
Operator Interval ◽  
Multiple Attribute ◽  
Interval Valued

In this paper, we expand the Muirhead mean (MM) operator and dual Muirhead mean (DMM) operator with interval-valued intuitionistic fuzzy numbers (IVIFNs) to propose the interval -valued intuitionistic fuzzy Muirhead mean (IVIFMM) operator, interval-valued intuitionistic fuzzy weighted Muirhead mean (IVIFWMM) operator, interval-valued intuitionistic fuzzy dual Muirhead mean (IVIFDMM) operator and interval-valued intuitionistic fuzzy weighted dual Muirhead mean (IVIFWDMM) operator. Then the MADM methods are proposed with these operators. In the end, we utilize an applicable example for green supplier selection in green supply chain management to prove the proposed methods.

scholarly journals Interval-valued fuzzy cognitive maps with genetic learning for predicting corporate financial distress

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1657-1662 ◽  
Author(s):  
Petr Hajek ◽  
Ondrej Prochazka
Keyword(s):  
Financial Distress ◽  
Causal Reasoning ◽  
Mean Squared Error ◽  
Nonlinear Problems ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Squared Error ◽  
Neuro Fuzzy ◽  
Model Complex ◽  
Interval Valued

Fuzzy cognitive maps (FCMs) integrate neural networks and fuzzy logic to model complex nonlinear problems through causal reasoning. Interval-valued FCMs (IVFCMs) have recently been proposed to model additional uncertainty in decision-making tasks with complex causal relationships. In traditional FCMs, optimization algorithms are used to learn the strengths of the relationships from the data. Here, we propose a novel IVFCM with real-coded genetic learning. We demonstrate that the proposed method is effective for predicting corporate financial distress based on causally connected financial concepts. Specifically, we show that this method outperforms FCMs, fuzzy grey cognitive maps and adaptive neuro-fuzzy systems in terms of root mean squared error.

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