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. © Springer International Publishing 2013.
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CITATION STYLE
Gen, M., & Ida, K. (2013). Advances in multiobjective hybrid genetic algorithms for intelligent manufacturing and logistics systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8210 LNCS, pp. 379–389). Springer Verlag. https://doi.org/10.1007/978-3-319-02750-0_41
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