Generalization of EF-based Assignment Strategies for Cycle Time Optimization at Complex wet Stations.

When solution approaches are developed to facilitate decision processes in real manufacturing systems, fundamental information constraints may apply. The data required by theoretically efficient decision support tools may not be available or accessible; as a consequence, the possibility of implementing the solutions developed is compromised. In this paper, assignment strategies subjected to information constraints and previously developed for a particular wet station are generalized to wet stations characterized by different tool configurations and production recipes. These strategies have been designed to be integrated with data management systems and make use of real time data to minimize the stations' average cycle time. The experiments run aim at supporting the hypothesis that progressive incorporation of details on the system status in assignment strategies enhances the strategies' performance. The relevance of data-driven simulation-based decision support is demonstrated with reference to wet stations operating in a real semiconductor manufacturing facility.