eprintid: 487 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/00/04/87 datestamp: 2023-11-09 15:16:07 lastmod: 2023-11-09 15:16:07 status_changed: 2023-11-09 15:14:40 type: article metadata_visibility: show creators_name: Jiménez, F. creators_name: Sánchez, G. creators_name: Vasant, P. creators_name: Verdegay, J.L. title: Fuzzy optimization via multi-objective evolutionary computation for chocolate manufacturing ispublished: pub note: cited By 3 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. © Springer Science + Business Media, LLC 2008. date: 2008 publisher: Springer International Publishing official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84976463510&doi=10.1007%2f978-0-387-76813-7_20&partnerID=40&md5=5e56809f2714715625edf5d2fdd156e2 id_number: 10.1007/978-0-387-76813-7₂₀ full_text_status: none publication: Springer Optimization and Its Applications volume: 16 pagerange: 523-537 refereed: TRUE issn: 19316828 citation: Jiménez, F. and Sánchez, G. and Vasant, P. and Verdegay, J.L. (2008) Fuzzy optimization via multi-objective evolutionary computation for chocolate manufacturing. Springer Optimization and Its Applications, 16. pp. 523-537. ISSN 19316828