Gradient-Based Simulation Optimization for Economic Design of Control Charts

We propose a gradient-based simulation optimization approach for economic design of control charts. A generalized likelihood ratio method is applied to estimate the gradient. Two stochastic approximation algorithms with increasing sample size in iterations and randomized sample size are developed to determine an optimal upper control limit for exponentially weighted moving average control chart. Numerical results show that the proposed method is an effective approach for economic design of control charts.