%P 279-291
%A P. Vasant
%A A. Bhattacharya
%V 38
%T Sensing degree of fuzziness in MCDM model using modified flexible S-curve MF
%N 4
%R 10.1080/00207720601117108
%D 2007
%J International Journal of Systems Science
%L scholars283
%O cited By 13
%X 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.
%K 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