Efficient Simulation-Based Composition of Scheduling Policies by Integrating Ordinal Optimization With Design of Experiment

Semiconductor wafer fab operations are characterized by complex and reentrant production processes over many heterogeneous machine groups with stringent performance requirements. Efficient composition of good scheduling policies from combinatorial options of wafer release and machine dispatching rules has posed a significant challenge to competitive fab operations. In this paper, we design a fast simulation-based methodology by an innovative integration of ordinal optimization (OO) and design of experiments (DOEs) to efficiently select a good scheduling policy for fab operations. Instead of finding the exact performance among scheduling policies, our approach compares their relative orders of performance to a specified level of confidence. Our new approach consists of three stages: performance estimation model construction using DOE, policy option screening process, and final simulation evaluation with intelligent computing budget allocation. The exponential convergence of OO is integrated into all the three stages to significantly improve computational efficiency. Simulation results of applications to scheduling wafer fabrications not only screen out good scheduling policies but also provide insights about how factors such as wafer release and the dispatching of each machine group may affect production cycle times and smoothness under a reentrant process flow. Most of the OO-based DOE simulations require 2-3 orders of magnitude less computation time than those of a traditional approach. Such a high speedup enables decision makers to explore much larger problems. Note to Practitioners - This paper designs a fast simulation-based methodology to compose a good scheduling policy from various dispatching rules of fab operations. The methodology innovatively applies DOE to estimate performance of dispatching rule combinations (policies) over various machines groups in a fab, screens out good enough policy options by using OO over the performance estimation, and allocates computation time intelligently to simulate potentially good options. Our study shows that OO-based DOE simulations require 2-3 orders of magnitude less computation time than those of a traditional approach. The high speedup enables fab managers to identify good scheduling policies from the many combinations of wafer release and dispatching rules.