Abstract
The aim of this paper is to evaluate the performance of genetic algorithms for the flowshop scheduling problem with an objective of minimizing the makespan. First we examine various genetic operators for the scheduling problem. Next we compare genetic algorithms with other search algorithms such as local search, taboo search and simulated annealing. By computer simulations, it is shown that genetic algorithms are a bit inferior to the others. Finally, we show two hybrid genetic algorithms: genetic local search and genetic simulated annealing. Their high performance is demonstrated by computer simulations.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Keywords
Job shop schedulingSimulated annealingComputer scienceScheduling (production processes)Genetic algorithmQuality control and genetic algorithmsMathematical optimizationAlgorithmLocal search (optimization)Machine learningMathematicsMeta-optimization
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Publication Info
- Year
- 2002
- Type
- article
- Pages
- 812-817
- Citations
- 230
- Access
- Closed
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Cite This
Tadahiko Murata,
Hisao Ishibuchi
(2002).
Performance evaluation of genetic algorithms for flowshop scheduling problems.
, 812-817.
https://doi.org/10.1109/icec.1994.349951
Identifiers
- DOI
- 10.1109/icec.1994.349951