TY  - JOUR
N1  - cited By 13
TI  - Sensing degree of fuzziness in MCDM model using modified flexible S-curve MF
SP  - 279
AV  - none
EP  - 291
SN  - 00207721
N2  - 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.
IS  - 4
KW  - Decision making; Industrial engineering; Mathematical models; Problem solving
KW  -  Degree of fuzziness; Fuzzy MCDM; Level of satisfaction; Plant location selection; S-curve membership function
KW  -  Membership functions
ID  - scholars283
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-34250875093&doi=10.1080%2f00207720601117108&partnerID=40&md5=d3cdf8d9b10cf3860cb310a97b3c98f9
A1  - Vasant, P.
A1  - Bhattacharya, A.
JF  - International Journal of Systems Science
VL  - 38
Y1  - 2007///
ER  -