A new sequential Monte Carlo (SMC) adjustment method is presented for solving the machine setup adjustment problem when process parameters are unknown. In setup adjustment problems, the mean of the distribution of the quality characteristic of parts can change from lot to lot due to an improper setup operation. It is shown how a first SMC approach has performance equivalent to a recently proposed Markov chain Monte Carlo method but at a small fraction of the computational cost, allowing for on-line control. A second, modified SMC rule that avoids unnecessary adjustments that can inflate the variance is also presented. A simulation approach is presented that allows tuning of the modified SMC rule to provide robust adjustment with respect to the unknown process parameters. Applications in short-run manufacturing processes are discussed.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Modeling and Simulation
- Applied Mathematics