@article{scholars10659,
           title = {An enhanced possibilistic programming model with fuzzy random confidence-interval for multi-objective problem},
             doi = {10.1007/978-3-319-66984-7{$_1$}{$_3$}},
            note = {cited By 1; Conference of 1st International Conference on the Computer Science and Engineering, COMPSE 2016 ; Conference Date: 11 November 2016 Through 12 November 2016; Conference Code:252929},
          volume = {741},
           pages = {217--235},
         journal = {Studies in Computational Intelligence},
       publisher = {Springer Science and Business Media Deutschland GmbH},
            year = {2018},
            isbn = {9783319669830},
          author = {Arbaiy, N. and Samsudin, N. A. and Mustapa, A. and Watada, J. and Lin, P.-C.},
            issn = {1860949X},
        abstract = {Mathematical models are established to represent real-world problems. Since the real-world faces various types of uncertainties, it makes mathematical model suffers with insufficient uncertainties modeling. The existing models lack of explanation in dealing uncertainties. In this paper, construction of mathematical model for decision making scenario with uncertainties is presented. Primarily, fuzzy random regression is applied to formulate a corresponding mathematical model from real application of a multi-objective problem. Then, a technique in possibilistic theory, known as modality optimization is used to solve the developed model. Consequently, the result shows that a well-defined multi-objective mathematical model is possible to be formulated for decision making problems with the uncertainty. Indeed, such problems with uncertainties can be solved efficiently with the presence of modality optimization. {\^A}{\copyright} Springer International Publishing AG 2018.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079148145&doi=10.1007\%2f978-3-319-66984-7\%5f13&partnerID=40&md5=258427d126d4c85fbba2be7cfaf0956c}
}