@article{scholars487,
             doi = {10.1007/978-0-387-76813-7{$_2$}{$_0$}},
            year = {2008},
           title = {Fuzzy optimization via multi-objective evolutionary computation for chocolate manufacturing},
       publisher = {Springer International Publishing},
            note = {cited By 3},
          volume = {16},
         journal = {Springer Optimization and Its Applications},
           pages = {523--537},
        abstract = {This chapter outlines, first, a real-world industrial problem for product mix selection involving 8 variables and 21 constraints with fuzzy coefficients and, second, a multi-objective optimization approach to solve the problem. This problem occurs in production planning in which a decision maker plays a pivotal role in making decisions under a fuzzy environment. Decision maker should be aware of his/her level-of-satisfaction as well as degree of fuzziness while making the product mix decision. Thus, the authors have analyzed using a modified S-curve membership function for the fuzziness patterns and fuzzy sensitivity of the solution found from the multi-objective optimization methodology. An ad hoc Pareto-based multi-objective evolutionary algorithm is proposed to capture multiple nondominated solutions in a single run of the algorithm. Results obtained have been compared with the well-known multi-objective evolutionary algorithm NSGA-II. {\^A}{\copyright} Springer Science + Business Media, LLC 2008.},
            issn = {19316828},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84976463510&doi=10.1007\%2f978-0-387-76813-7\%5f20&partnerID=40&md5=5e56809f2714715625edf5d2fdd156e2},
          author = {Jim{\~A}{\copyright}nez, F. and S{\~A}!nchez, G. and Vasant, P. and Verdegay, J. L.}
}