Detection of level of satisfaction and fuzziness patterns for MCDM model with modified flexible S-curve MF

Pandian M. Vasant; Arijit Bhattacharya; Bijan Sarkar; Sanat Kumar Mukherjee

doi:10.1016/j.asoc.2006.10.005

Detection of level of satisfaction and fuzziness patterns for MCDM model with modified flexible S-curve MF

Pandian M. Vasant, Arijit Bhattacharya, Bijan Sarkar, Sanat Kumar Mukherjee

Research output: Contribution to journal › Article › peer-review

61 Citations (Scopus)

Abstract

The present research work deals with a logistic membership function (MF), within non-linear MFs, in finding out fuzziness patterns in disparate level of satisfaction for Multiple Criteria Decision-Making (MCDM) problem. This MF is a modified form of general set of S-curve MF. Flexibility of this MF in applying to real world problem has also been validated through a detailed analysis. An example illustrating an MCDM model applied in an industrial engineering problem has been considered to demonstrate the veracity of the proposed technique. The approach presented here provides feedback to the decision maker, implementer and analyst and gives a clear indication about the appropriate application and usefulness of the MCDM model. The key objective of this paper is to guide decision makers in finding out the best candidate-alternative with higher degree of satisfaction with lesser degree of vagueness under tripartite fuzzy environment.

Original language	English
Pages (from-to)	1044-1054
Number of pages	11
Journal	Applied Soft Computing
Volume	7
Issue number	3
Early online date	17 Nov 2006
DOIs	https://doi.org/10.1016/j.asoc.2006.10.005
Publication status	Published - Jun 2007

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10.1016/j.asoc.2006.10.005

Cite this

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title = "Detection of level of satisfaction and fuzziness patterns for MCDM model with modified flexible S-curve MF",

abstract = "The present research work deals with a logistic membership function (MF), within non-linear MFs, in finding out fuzziness patterns in disparate level of satisfaction for Multiple Criteria Decision-Making (MCDM) problem. This MF is a modified form of general set of S-curve MF. Flexibility of this MF in applying to real world problem has also been validated through a detailed analysis. An example illustrating an MCDM model applied in an industrial engineering problem has been considered to demonstrate the veracity of the proposed technique. The approach presented here provides feedback to the decision maker, implementer and analyst and gives a clear indication about the appropriate application and usefulness of the MCDM model. The key objective of this paper is to guide decision makers in finding out the best candidate-alternative with higher degree of satisfaction with lesser degree of vagueness under tripartite fuzzy environment.",

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AU - Sarkar, Bijan

AU - Mukherjee, Sanat Kumar

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