Genetic Algorithm based Scheduling in a Dynamic Manufacturing Environment

The application of adaptive optimization strategies to scheduling in manufacturing systems has recently become a research topic of broad interest. Population based approaches to scheduling predominantly treat static data models, whereas real-world scheduling tends to be a dynamic problem. This paper briefly outlines the application of a genetic algorithm to the dynamic job shop problem arising in production scheduling. First we sketch a genetic algorithm which can handle release times of jobs. In a second step a preceding simulation method is used to improve the performance of the algorithm. Finally the job shop is regarded as a nondeterministic optimization problem arising from the occurence of job releases. Temporal Decomposition leads to a scheduling control that interweaves both simulation in time and genetic search.