In this paper, we propose a modeling tool named Queueing Colored Petri nets (QCPN) for performance evaluation and scheduling for wafer fabrication. The main idea of this tool is to combine colored timed Petri nets with the queueing systems, and it aims to make simulation over the model more efficient. Due to the wide acceptance of priority rules in the wafer manufacturing industry, we also proposed a mechanism to realize priority rules in the QCPN models. Since it is known that no single rule can dominate in any circumstance, we proposed a genetic algorithm (GA) to search for the optimal combination of a number of priority rules based on the status and performance measures of the fab. Our approach can be considered as taking the advantage of the lot execution sequence generated by priority rules to guide the search. This approach can reduce the solution space and help us find the good solution more quickly. In addition, the QCPN-based GA scheduler can greatly reduce the computation time so that this GA scheduler can meet the need for a rapidly changing environment. Note to Practitioners-Performance evaluation and scheduling are two functions required by fab managers and engineers. This paper proposed a tool which consists of a simulator and a scheduler. By connecting to the Manufacturing Execution System (MES) and providing the scheduling rules, we can see how the fab runs virtually with the simulator. General information such as throughput and average cycle time and specific information like lot activity history can be obtained. This can be used for decision making, delivery prediction, bottleneck seeking, and testing of newly developed heurisitcs. The implementation cost is only on data communication between the MES and the simulator and the incorporation of rule modules. The scheduler, which takes the simulator as the performance evaluation module, can generate the suitable scheduling rule based on the current fab status, preference of performance criteria, and rule candidates. There is almost no extra cost after the simulator is connected to the MES. The scheduler can be easily made faster by common parallelization techniques.

We propose a modeling tool named QCPN (queueing colored Petri net). The main idea of this tool is to combine the original CTPN (colored timed Petri net) with the queueing systems. With the QCPN model, we can predict the delivery date of any specific product under some scheduling policies efficiently and rather precisely. In the scheduling phase, we use the GA based approach to search for the optimal combination of a number of heuristic rules. This QCPN based GA scheduler can greatly reduce the computation time so as to meet the need for a rapidly changing environment.

A genetic algorithm (GA) embedded search strategy over a colored timed Petri net (CTPN) for wafer fabrication is proposed. Through the CTPN model, all possible behaviors of the wafer manufacturing systems, such as WIP status and machine status, can be completely tracked down by the reachability graph of the net. The chromosome representation of the search nodes in GA is constructed directly from the CTPN model, recording information about the appropriate scheduling policy for each workstation in the fabrication. A better chromosome found by GA is received by the CTPN based schedule builder, and a near-optimal schedule is then generated.