A Robust Scheduling for Reconfigurable Manufacturing System Using Petri Nets and Genetic Algorithm

A genetic algorithm (GA) embedded with extended timed-place Petri nets (ETPN) is proposed. Four basic ETPN modules are presented. All possible behaviors of reconfigurable manufacturing system (RMS) can be completely modeled by the modules. Firing sequences of the Petri nets model are used as chromosomes representation in the GA. Thus the selection, crossover and mutation operators deal with the elements of Petri nets model instead of the ones in the problem space. Meanwhile, the new multiple-objective optimization is introduced based on two objectives minus; reconfiguration cost and balanced production. In addition, computational simulation suggest that the proposed ETPN-GA scheduling algorithm has a significant impact on RMS scheduling, by providing obvious improvements over the conventional scheduling method in practice. An example of reconfigurable production line scheduling is presented to illustrate the efficiency of the proposed approach