Sensing degree of fuzziness in MCDM model using modified flexible S-curve MF

Vasant, P. and Bhattacharya, A. (2007) Sensing degree of fuzziness in MCDM model using modified flexible S-curve MF. International Journal of Systems Science, 38 (4). pp. 279-291. ISSN 00207721

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Abstract

It is hard to sense the degree of vagueness while using a Multiple Criteria Decision-Making (MCDM) model in industrial engineering problems. Selection of best candidate-alternative is an important issue when the attributes of the candidate-alternatives are conflicting in nature and they have incommensurable units. An MCDM model makes it possible to select the candidate-alternative that suits best for the investor. An example illustrating an MCDM model applied in plant-site selection problem has been considered in this article to demonstrate the veracity of the proposed methodology. The degree of vagueness hidden in the proposed approach has been investigated using a flexible modified logistic membership function (MF). 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 article is to guide decision makers in finding out the best candidate-alternative with higher degree of satisfaction and lesser degree of vagueness.

Item Type: Article
Additional Information: cited By 13
Uncontrolled Keywords: Decision making; Industrial engineering; Mathematical models; Problem solving, Degree of fuzziness; Fuzzy MCDM; Level of satisfaction; Plant location selection; S-curve membership function, Membership functions
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 15:15
Last Modified: 09 Nov 2023 15:15
URI: https://khub.utp.edu.my/scholars/id/eprint/283

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