Genetic algorithm based scheduling in a dynamic manufacturing environment

The application of adaptive optimization methods to production scheduling has recently become a research topic of broad interest. Genetic algorithm approaches to scheduling concentrate on static problems, whereas real world scheduling tends to be dynamic. The paper briefly outlines the application of a genetic algorithm to the dynamic job shop problem. In a second step the job shop is regarded as a non deterministic optimization problem arising from the occurrence of job releases. A temporal decomposition of the non deterministic problem leads to a scheduling control that combines simulation and adaptive search