Finding the numerical compensation in multiple criteria decision-making problems under fuzzy environment

In this paper, we have developed a methodology to derive the level of compensation numerically in multiple criteria decision-making (MCDM) problems under fuzzy environment. The degree of compensation is dependent on the tranquility and anxiety level experienced by the decision-maker while taking the...

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Veröffentlicht in:International journal of systems science Jg. 48; H. 6; S. 1301 - 1310
Hauptverfasser: Gupta, Mahima, Mohanty, B. K.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Taylor & Francis 26.04.2017
Taylor & Francis Ltd
Schlagworte:
Agglomeration
Anxiety
Compensation
Criteria
Decision making
degree of compensation
Fuzzy logic
Fuzzy sets
Goal programming
Indexing
Integer programming
Mathematical models
Multiple criteria analysis
Multiple criterion
Risk
Risk levels
risk prone
tranquility
ISSN:0020-7721, 1464-5319
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Zusammenfassung:In this paper, we have developed a methodology to derive the level of compensation numerically in multiple criteria decision-making (MCDM) problems under fuzzy environment. The degree of compensation is dependent on the tranquility and anxiety level experienced by the decision-maker while taking the decision. Higher tranquility leads to the higher realisation of the compensation whereas the increased level of anxiety reduces the amount of compensation in the decision process. This work determines the level of tranquility (or anxiety) using the concept of fuzzy sets and its various level sets. The concepts of indexing of fuzzy numbers, the risk barriers and the tranquility level of the decision-maker are used to derive his/her risk prone or risk averse attitude of decision-maker in each criterion. The aggregation of the risk levels in each criterion gives us the amount of compensation in the entire MCDM problem. Inclusion of the compensation leads us to model the MCDM problem as binary integer programming problem (BIP). The solution to BIP gives us the compensatory decision to MCDM. The proposed methodology is illustrated through a numerical example.
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ISSN:0020-7721
1464-5319
DOI:10.1080/00207721.2016.1252990