An application of effective genetic algorithms for solving hybrid flow shop scheduling problems

Cengiz Kahraman*, Orhan Engin, Ihsan Kaya, Mustafa Kerim Yilmaz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

94 Citations (Scopus)

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 (Cmax) for HFS problems.

Original languageEnglish
Pages (from-to)134-147
Number of pages14
JournalInternational Journal of Computational Intelligence Systems
Volume1
Issue number2
DOIs
Publication statusPublished - Jun 2008

Keywords

  • Completion time
  • Genetic algorithm
  • Hybrid flow shop scheduling

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