Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm

Nasr Al-Hinai*, T. Y. Elmekkawy

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

194 Citations (Scopus)


This paper addresses the problem of finding robust and stable solutions for the flexible job shop scheduling problem with random machine breakdowns. A number of bi-objective measures combining the robustness and stability of the predicted schedule are defined and compared while using the same rescheduling method. Consequently, a two-stage Hybrid Genetic Algorithm (HGA) is proposed to generate the predictive schedule. The first stage optimizes the primary objective, minimizing makespan in this work, where all the data is considered to be deterministic with no expected disruptions. The second stage optimizes the bi-objective function and integrates machines assignments and operations sequencing with the expected machine breakdown in the decoding space. An experimental study and Analysis of Variance (ANOVA) is conducted to study the effect of different proposed measures on the performance of the obtained results. Results indicate that different measures have different significant effects on the relative performance of the proposed method. Furthermore, the effectiveness of the current proposed method is compared against three other methods; two are taken from literature and the third is a combination of the former two methods.

Original languageEnglish
Pages (from-to)279-291
Number of pages13
JournalInternational Journal of Production Economics
Issue number2
Publication statusPublished - Aug 2011
Externally publishedYes


  • Flexible job shop scheduling problem
  • Machine breakdowns
  • Robust
  • Stable

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Economics and Econometrics
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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