Estimating Clearing Functions for Production Resources Using Simulation Optimization

We implement a gradient-based simulation optimization approach, the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm, to estimate clearing functions (CFs) that describe the expected output of a production resource as a function of its expected workload from empirical data. Instead of trying to optimize the fit of the CF to the data, we seek values of the CF parameters that optimize the expected performance for the system when the fitted CFs are used to develop release schedules. A simulation model of a scaled-down wafer fabrication facility is used to generate the data and evaluate the performance of the CFs obtained from the SPSA. We show that SPSA significantly improves the production plan by either searching for better CF parameters or by directly optimizing releases. Note to Practitioners-The problem of planning work releases into large production facilities made up of resources subject to queueing behavior is pervasive in many industries, notably in semiconductor wafer fabrication. Most current planning methods fail to recognize the nonlinear relationship between workload and cycle times imposed by queueing behavior. We present an optimization model for planning work releases into such facilities using nonlinear CFs. We estimate the parameters of the CFs using simulation optimization, and show that the resulting functions yield significantly better plant performance than prior methods, which have been shown to perform better than the linear programming models that are the current state-of-the-art.