Abstract
As a bridge links the upper enterprise planning system and the lower shop floor control system, enormous real-time information interact in shop floor, which poses great difficulty for scheduling of manufacturing execution system(MES). To meet the requirement of MES agility in the volatile information environment, dynamic scheduling becomes one of most widely used methods. In this paper, a modified immune genetic algorithm which incorporates artificial immune mechanism into genetic algorithm is presented to solve dynamic job shop scheduling problems. Owing to its good solving capability and computing speed, the algorithm could utilize real-time production information to generate predictive and reactive scheduling solutions. At last, the algorithm is applied in a MT10×10 job shop proved to be effective in obtaining better solutions than traditional genetic algorithm.
Published Version
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