Journal of Intelligent Manufacturing, 13, 339±351, 2002 # 2002 Kluwer Academic Publishers. Manufactured in The Netherlands. Robust design of ¯exible manufacturing systems using, colored Petri net and genetic algorithm KAZUHIRO SAITOU, SAMIR MALPATHAKandHELGE QVAM Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, 48109-2125, USA E-mail: kazu@umich.edu Received April and accepted November 2001 A method is presented for the robust design of ¯exible 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 ®ring 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 recon®guration cost due to production plan changes. A genetic algorithm, coupled with the shortest imminent operation time (SIO) dispatching rule, is used to simultaneously ®nd 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 speci®cations so that optimizing for a particular production plan creates inevitably bottlenecks in product ¯ow and/or deadlock under other production plans. Keywords: Flexible manufacturing systems, robust design, colored Petri nets, genetic algorithms, part families. 1. Introduction Flexible manufacturing systems (FMS) are a class of manufacturing systems that can be quickly con®gured to produce multiple types of products (jobs). The adoption of an FMS, over a dedicated manufacturing system (DMS), is often motivated by the need of agile manufacturing that can quickly adopt changes in production plans (batch sizes for all jobs) due to market demand ¯uctuations. While the increased ¯exibility of FMS could provide greater overall productivity under various production scenarios, it imposes increased complexity in the allocation of available resources to different operations required in making each product, and the scheduling of the sequence of activities to accomplish the best production ef®ciency (Lee and DiCeasre, 1994). In order to quickly adapt to ¯uctuating market demands, the resource allocation and the sche- dulingÐreferred to as con®guration in this paperÐ of an FMS should not simply be optimized for the current production plan. Rather, it should ideally be optimized for robustness against the variation in production plans, so that the system can deal with the variation with minimal recon®guration while achieving consistently ef®cient production under all production plans of interest (Saitou and Malpathak, 1999; Saitou and Quam, 1998) Assuming the forecasts of production plan varia- tions are available, let us consider a scenario where an FMS simultaneously produces two types of products A and B with various fractions while the total number of production per unit time (e.g., a day) is kept constant. When A and B are very similar in their job speci®cations, then, it is conjectured that one would not need to consider robustness optimization since the