This study pursues to solve a batch of nonlinear parameter estimation (NPE) problems where a model interpreting the independent and the dependent variables is given and fixed but corresponding data sets vary. Specifically, we assume that the model does not have an explicit form and the discrepancy between a value from a data set and a corresponding value from the model is unknown. Due to the complexity of the problem, one may prefer to use heuristic algorithms rather than gradient-based algorithms, but the performance of the heuristic algorithms depends on their initial settings. In this study, we suggest two schemes to improve the performance of heuristic algorithms to solve the target problem. Most of all, we apply a Bayesian optimization to find the best parameters of the heuristic algorithm for solving the first NPE problem of the batch and apply the parameters of the heuristic algorithm for solving other NPE problems. Besides, we save a list of simulation outputs obtained from the Bayesian optimization and then use the list to construct the initial population set of the heuristic algorithm. The suggested schemes were tested in two simulation studies and were applied to a real example of measuring the critical dimensions of a 2-dimensional high-aspect-ratio structure of a wafer in semiconductor manufacturing.

This research focuses on a scheduling problem in the semiconductor probing facility. Probing facility is composed of identical parallel machines and the parallel machines form three workstations for the tests with different recipes. Each machine can be set to three different tests and sequence-dependent setup times are required between operations due to temperature and probe card loading/unloading. Precedence relationship exists between three tests of each wafer lot. The scheduling problem for the probing facility is a parallel machine scheduling problem with precedence relationship and sequence dependent setup time. We develop heuristic algorithm to minimize makespan for the scheduling problem and numerical experiments are conducted to evaluate the performance.

This paper considers a 2-stage assembly flowshop scheduling problem where each job is completed by assembling multiple components. The problem has the objective measure of minimizing total completion time. The problem is shown to be NP-complete in the str