Genetic algorithm based scheduling in a dynamic manufacturing environment

Abstract

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. This 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 nondeterministic problem leads to a scheduling control that combines simulation and adaptive search.