Flexible job shop scheduling based on Petri-net model: Ant colony optimization and genetic algorithm hierarchical evolutionary optimization approach

A controlled timed Petri-net was used to model discrete events in flexible production lines scheduling. This model was synthesized by process-flow subnet, resource subnet and scheduling subnet by means of synchronous common transition interconnection. A hierarchical evolutionary optimization approach for flexible job shop scheduling problems was proposed based on the simulation evaluation via the Petri-net model. The processing route of jobs was optimized by an ant colony optimization (ACO) approach, and, on the basis of the optimal processing route, a genetic algorithm was proposed to optimize the sequence of operations processed on each machine. On the principle of ACO, the representation of pheromone, the strategy of solution construction and pheromone updating problem of jobs were proposed. A benchmark problem solving result indicates that the proposed algorithm is effective.