Enhanced Genetic Algorithm-Based Fuzzy Multiobjective Strategy to Multiproduct Batch Plant Design

  • Alberto A. Aguilar-Lasserre
  • Catherine Azzaro-Pantel
  • Luc Pibouleau
  • Serge Domenech
Part of the Advances in Soft Computing book series (AINSC, volume 41)

Abstract

The design of such plants necessary involves how equipment may be utilized, which means that plant scheduling and production must form an integral part of the design problem. This work proposes an alternative treatment of the imprecision (demands) by using fuzzy concepts. In this study, we introduce a new approach to the design problem based on a multi-objective genetic algorithm, taking into account simultaneously maximization of the net present value \(N\tilde{P}V\) and two other performance criteria, i.e. the production delay/advance and a flexibility criterion. The methodology provides a set of scenarios that are helpful to the decision’s maker and constitutes a very promising framework for taken imprecision into account in new product development stage. Besides, a hybrid selection method Pareto rank-tournament was proposed and showed a better performance than the classical Goldberg’s wheel, systematically leading to a higher number of non-dominated solutions.

Keywords

Multiobjective Optimization Genetic Algorithm Fuzzy Arithmetic Batch Plant Design 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Alberto A. Aguilar-Lasserre
    • 1
  • Catherine Azzaro-Pantel
    • 2
  • Luc Pibouleau
    • 2
  • Serge Domenech
    • 2
  1. 1.Division of Research and Postgraduate Studies, Instituto Tecnologico de, Orizaba, Av. Instituto Tecnologico 852, Col Emiliano Zapata. 09340, Orizaba, VeracruzMéxico
  2. 2.Laboratoire de Génie Chimique-UMR 5503 CNRS/INP/UPS 5, Rue Paulin Talabot- BP1301, 534-61-5252 

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