Modeling, scheduling, and prediction in wafer fabrication systems using queueing Petri net and genetic algorithm

Wafer fabrication is one of the most competitive manufacturing business in the world. In order to survive in such a strongly competitive environment, finding an effective schedule which can result in higher machine utilization and throughput rate, shorter cycle time, and lower WIP (work-in-process) inventory becomes a major task. Besides that, in order to help customers to make ordering decisions as well as to let the manager control the processing conditions of the fab, we need to predict some performance measures efficiently. We propose a modeling tool called queueing-Petri net (Q-PN) which combines the characteristics of queueing theory and Petri nets. It can be used to model various details of the manufacturing systems as well as to evaluate its performance very efficiently. Then, a general Q-PN model is presented to simulate the semiconductor manufacturing system. Based on this model, we propose a genetic algorithm (GA) based scheduler and an analysis-based predictor. In the GA scheduler, the chromosome represents a combination of scheduling policies, including lot release policies, machine selection rules, dispatch rules and batch rules. So, when the GA finishes its optimization process, an optimal scheduling policy is produced. As for the predictor, because it inherits the analytical property of queueing theory from the Q-PN model, we can use it to predict those performance measures efficiently such as the exact due date of some particular lot.