@article{scholars283,
           pages = {279--291},
         journal = {International Journal of Systems Science},
            year = {2007},
           title = {Sensing degree of fuzziness in MCDM model using modified flexible S-curve MF},
             doi = {10.1080/00207720601117108},
          volume = {38},
            note = {cited By 13},
          number = {4},
            issn = {00207721},
          author = {Vasant, P. and Bhattacharya, A.},
        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.},
        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},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34250875093&doi=10.1080\%2f00207720601117108&partnerID=40&md5=d3cdf8d9b10cf3860cb310a97b3c98f9}
}