Efficient selection of scheduling rule combination by combining design of experiment and ordinal optimization-based simulation

In a fab with heterogeneous machine groups, the number of scheduling policies grows in a combinatorial way because each machine group has its specific dispatching rules. In this paper, we design a fast simulation methodology by an innovative combination of the notions of ordinal optimization (OO) and design of experiments (DOE) to efficiently select a good scheduling policy for fab operation, Instead of finding the exact performance among scheduling policies, our approach compares their relative orders of performance to a specified level of confidence. The DOE method is exploited to largely reduce the number of scheduling policies to be evaluated by the OO-based simulation. Simulation results of applications to scheduling wafer fabrications show that most of the OO-based DOE simulations require 2 to 3 orders of magnitude less computation time than those of traditional approach, and the speedup is up to 7,000 times in certain cases.