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Advances in Multiobjective Hybrid Genetic Algorithms for Intelligent Manufacturing and Logistics Systems

  • Mitsuo Gen
  • Kenichi Ida
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8210)

Abstract

Recently, genetic algorithms (GA) have received considerable attention regarding their potential as a combinatorial optimization for complex problems and have been successfully applied in the area of various engineering. We will survey recent advances in hybrid genetic algorithms (HGA) with local search and tuning parameters and multiobjective HGA (MO-HGA) with fitness assignments. Applications of HGA and MO-HGA will introduced for flexible job-shop scheduling problem (FJSP), reentrant flow-shop scheduling (RFS) model, and reverse logistics design model in the manufacturing and logistics systems.

Keywords

Hybrid genetic algorithms (HGA) Multiobjective HGA (MOHGA) Reentrant flow-shop scheduling (RFS) Flexible job-shop scheduling problem (FJSP) Multiobjective reverse logistics model 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Mitsuo Gen
    • 1
    • 2
  • Kenichi Ida
    • 3
  1. 1.Fuzzy Logic Systems InstituteIizukaJapan
  2. 2.National Tsing Hua UniversityTaiwan
  3. 3.Maebashi Institute of TechnologyMaebashiJapan

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