Supervisor synthesis and performance improvement for automated manufacturing systems by using Petri nets

Behavior management and performance evaluation are two significant issues in the design and operation of automated manufacturing systems (AMS). The former constrains their dynamics so as to avoid improper behavior and eliminate downtime, while the latter aims to evaluate cycle time. Although abundant research has been conducted on each issue independently, researchers rarely address both in a correlative way. Without accurate and prompt guidance to route parts, it is difficult for an AMS to manage the production process in an optimal manner. In the paradigm of Petri nets, we show that it is crucial to optimize certain local priorities for the sake of cycle time minimization or throughput maximization. Without making radical changes to the system structure, our method can well design some control parameters in order to meet system performance requirement. An algorithm of polynomial complexity is presented to derive supervisory controllers, if existing, to improve system performance.