Optimal Adjustment in the Presence of Deterministic Process Drift and Random Adjustment Error

Abstract

A state-space process-control model involving adjustment error and deterministic drift of the process mean is presented. The optimal adjustment policy is developed by dynamic programming. This policy calls for a particular adjustment when a Kalman-filter estimator is outside a deadband defined by upper and lower action limits. The effects of adjustment cost, adjustment variance, and drift rate on the optimal policy are discussed. The optimal adjustment policy is computed for a real machining process, and a simulation study is presented that compares the optimal policy to two sensible suboptimal policies.

KEY WORDS:

• Control chart
• Deadband
• Dynamic programming
• Kalman filter
• Simulation
• State-space model