Multi-Attribute Decision Making
Abazar Keikha
Abstract
Purpose: The aim of this paper is to propose a new extension of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to be applied with Hesitant Fuzzy Numbers (HFNs).Methodology: At first, the uncertainty of all enteries of evaluation matrix have been modeled by HFNs. ...
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Purpose: The aim of this paper is to propose a new extension of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to be applied with Hesitant Fuzzy Numbers (HFNs).Methodology: At first, the uncertainty of all enteries of evaluation matrix have been modeled by HFNs. Then, each step of the standard model of TOPSIS method will be updated, using the newly introduced HFNs’ mathematical tools, such as distance measures and aggregation operators of HFNs. The proposed method will be used to solve a Multi-Attribute Decision Making (MADM) problem. Finally, the credibility and comparison analysis of the obtained ranking order will be discussed.Findings: In this paper, the TOPSIS method as a popular method for solving MADM problems has been developed to be applied with HFNs.Originality/Value: In this paper, the TOPSIS method as a popular method for solving MADM problems has been developed to be applied with HFNs.
Multi-Attribute Decision Making
Abazar Keikha; Hassan Mishmast Nehi
Abstract
Purpose: Using hesitant fuzzy numbers as a combination of two common types of evaluation: self-evaluation and evaluation by judges, in order to make real and fair evaluations. Updating the Choquet integral method to apply with hesitant fuzzy numbers in the evaluation process, and use it to solve decision ...
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Purpose: Using hesitant fuzzy numbers as a combination of two common types of evaluation: self-evaluation and evaluation by judges, in order to make real and fair evaluations. Updating the Choquet integral method to apply with hesitant fuzzy numbers in the evaluation process, and use it to solve decision problems such as evaluating employees and organizations. Methodology: The method of conducting these studies is based on the pattern of library studies.</pFindings: Deficiencies such as showcasing the evaluators during the evaluation period on the one hand, and the lack of mastery of external judges on some organizational complexities and the apparent and hidden motivations of the evaluators for unrealistic evaluation in the self-evaluation process, on the other hand, are some of factors that challenge the evaluation results, and these defects in the hybrid evaluation model are eliminated using hesitant fuzzy numbers. In addition, evaluation indicators in many cases interact with each other and have so-called positive and negative effects on each other. Choquet Integral is able to take this into account and take the assessment one step closer to becoming more realistic. Therefore, its computational development with hesitant fuzzy numbers, which has been considered in this article, can helps the evaluation system and performance of employees and organizations.Originality/Value: Computational development of hesitant fuzzy numbers with the help of Choquet integral, using the Choquet integral of hesitant fuzzy numbers in solving multi-criteria decision making problems such as employee and organizational evaluation.