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Fuzzy Optimization via Multi-Objective Evolutionary Computation for Chocolate Manufacturing

In: Fuzzy Multi-Criteria Decision Making

Author

Listed:
  • Fernando Jiménez

    (University of Murcia)

  • Gracia Sánchez

    (University of Murcia)

  • Pandian Vasant

    (Universiti Teknologi Petronas)

  • José Luis Verdegay

    (University of Granada)

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.

Suggested Citation

  • Fernando Jiménez & Gracia Sánchez & Pandian Vasant & José Luis Verdegay, 2008. "Fuzzy Optimization via Multi-Objective Evolutionary Computation for Chocolate Manufacturing," Springer Optimization and Its Applications, in: Cengiz Kahraman (ed.), Fuzzy Multi-Criteria Decision Making, pages 523-537, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-76813-7_20
    DOI: 10.1007/978-0-387-76813-7_20
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