Enhancement of performance of Genetic Algorithm for job shop scheduling problems through inversion operator

It is observed that the optimization technique Genetic Algorithm is gaining more importance over the past several years. With high computing power we are able to apply soft computing techniques to solve complex problems in less time. An approach through Genetic Algorithm to solve job shop scheduling problems using inversion operator has been tried, with make-span objective. Computational experiments of this attempt have shown better solutions coupled with appreciable reduction in computer processing time. A set of 20 selected benchmark problems were tried with the proposed heuristic for validation and the results are encouraging. The inversion operator is found to perform better.