Flowshop scheduling by hybridized genetic algorithms: Some new results

Recent studies suggest that one should hybridize genetic algorithms to take advantage of any resulting reduction of the search space and/or of structural features of the problem being solved. Such approach has been shown to be more productive than either the stand-alone application of heuristic methods or a pure GA search applied to combinatorial problems. This paper reports on the experience of appropriately combining good heuristic methods – both solution-generating and solution improvement types – with GA search in flowshop scheduling. Exceptional savings in computational effort are noted. Statistical tests endorse the results.