An efficient hybridized genetic algorithm architecture for the flexible job shop scheduling problem

Nasr Al-Hinai, T. Y. Elmekkawy

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

The work presented in this paper proposes hybridized genetic algorithm architecture for the Flexible Job Shop Scheduling Problem (FJSP). The efficiency of the genetic algorithm is enhanced by integrating it with an initial population generation algorithm and a local search method. The usefulness of the proposed methodology is illustrated with the aid of an extensive computational study on 184 benchmark problems with the objective of minimizing the makespan. Results highlight the ability of the proposed algorithm to first obtain optimal or near-optimal solutions, and second to outperform or produce comparable results with these obtained by other best-known approaches in literature.

Original languageEnglish
Pages (from-to)64-85
Number of pages22
JournalFlexible Services and Manufacturing Journal
Volume23
Issue number1
DOIs
Publication statusPublished - Mar 2011

Fingerprint

Genetic algorithms
Job shop scheduling
Genetic algorithm
Local search
Usefulness
Optimal solution
Methodology
Benchmark
Makespan

Keywords

  • Flexible job shop
  • Genetic algorithms
  • Local search
  • Meta heuristic approaches
  • Scheduling problems

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Cite this

An efficient hybridized genetic algorithm architecture for the flexible job shop scheduling problem. / Al-Hinai, Nasr; Elmekkawy, T. Y.

In: Flexible Services and Manufacturing Journal, Vol. 23, No. 1, 03.2011, p. 64-85.

Research output: Contribution to journalArticle

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