A GA embedded dynamic search algorithm over a Petri net model for an FMS scheduling

In this paper, a genetic algorithm (GA) embedded dynamic search strategy over a Petri net model provides a new scheduling method for a flexible manufacturing system (FMS). The chromosome representation of the search nodes is constructed directly from the Petri net model of an FMS, recording the information about all conflict resolutions, such as resource assignments and orders for resource allocation. The GA operators may enforce some change to the chromosome information in the next generation. A Petri net based schedule builder receives a chromosome and an initial marking as input, and then produces a near-optimal schedule. Due to the NP-complete nature of the scheduling problem of an FMS, we also propose a dynamic FMS scheduler incorporating the proposed GA embedded search scheme, which generates successive partial schedules, instead of generating a full schedule for all raw parts, as the production evolves