Robust design of flexible manufacturing systems using, colored Petri net and genetic algorithm

A method is presented for the robust design of flexible manufacturing systems (FMS) that undergo the forecasted product plan variations. The resource allocation and the operation schedule of a FMS are modeled as a colored Petri net and an associated transition firing sequence. The robust design of the colored Petri net model is formulated as a multi-objective optimization problem that simultaneously minimizes the production costs under multiple production plans (batch sizes for all jobs), and the reconfiguration cost due to production plan changes. A genetic algorithm, coupled with the shortest imminent operation time (SIO) dispatching rule, is used to simultaneously find the near-optimal resource allocation and the event-driven schedule of a colored Petri net. The resulting Petri net is then compared with the Petri nets optimized for a particular production plan in order to address the effectiveness of the robustness optimization. The simulation results suggest that the proposed robustness optimization scheme should be considered when the products are moderately different in their job specifications so that optimizing for a particular production plan creates inevitably bottlenecks in product flow and/or deadlock under other production plans.