AN APPLICATION OF EFFECTIVE GENETIC ALGORITHMS FOR SOLVING HYBRID FLOW SHOP SCHEDULING PROBLEMS

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Kahraman C., Engin O., KAYA İ., Yılmaz M. K.

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, vol.1, no.2, pp.134-147, 2008 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 1 Issue: 2
  • Publication Date: 2008
  • Doi Number: 10.2991/ijcis.2008.1.2.4
  • Journal Name: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.134-147
  • Keywords: Hybrid flow shop scheduling, Genetic algorithm, completion time, BOUND ALGORITHM, 2-STAGE, BRANCH
  • Yıldız Technical University Affiliated: No

Abstract

This paper addresses the Hybrid Flow Shop (HFS) scheduling problems to minimize the makespan value. In recent years, much attention is given to heuristic and search techniques. Genetic algorithms (GAs) are also known as efficient heuristic and search techniques. This paper proposes an efficient genetic algorithm for hybrid flow shop scheduling problems. The proposed algorithm is tested by Carlier and Neron's (2000) benchmark problem from the literature. The computational results indicate that the proposed efficient genetic algorithm approach is effective in terms of reduced total completion time or makespan (C-max) for HFS problems.