Metaheuristics optimization approaches for two-stage reentrant flexible flow shop with blocking constraint

Chatnugrob Sangsawang, Kanchana Sethanan*, Takahiro Fujimoto, Mitsuo Gen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)


This paper addresses a problem of the two-stage reentrant flexible flow shop (RFFS) with blocking constraint (FFS|2-stage,rcrc,block|Cmax). The objective is to find the optimal sequences in order to minimize the makespan. In this study, the hybridization of GA (HGA: hybrid genetic algorithm) with adaptive auto-tuning based on fuzzy logic controller and the hybridization of PSO (HPSO: hybrid particle swarm optimization) with Cauchy distribution were developed to solve the problem. The encoding and decoding routines that appropriate for blocking constraint and Relax-Blocking algorithm for improving chromosome and particle were suggested. Experimental results reveal that the HPSO and HGA algorithms give better solutions than the classical metaheuristics, GA and PSO, for all test problems respectively. Additionally, the relative improvement (RI) of the makespan solutions obtained by the proposed algorithms with respect to those of the current practice is performed in order to measure the quality of the makespan solutions generated by the proposed algorithms. The RI results show that the HGA and HPSO algorithms can improve the makespan solution by averages of 15.51% and 15.60%, respectively. We found that the performance of the HGA is not significantly competitive as compared to the HPSO but its computational times are significantly higher than those of the HPSO.

Original languageEnglish
Pages (from-to)2395-2410
Number of pages16
JournalExpert Systems with Applications
Issue number5
Publication statusPublished - 2015 Apr 1
Externally publishedYes


  • Blocking constraint
  • Hard disk drive (HDD)
  • Hybrid genetic algorithm (HGA)
  • Hybrid particle swarm optimization (HPSO)
  • Reentrant flexible flow shop (RFFS)
  • manufacturing

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


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