Constrained SPSA controller for operations processes

Continuous quality improvement calls for employing methodologies that assist in continual reduction of variations in process performance characteristics around their target values. The study considers a case in which some of the operations process parameters/inputs are required to take values in pre-specified ranges. To improve the process performance while accounting for these requirements, the study employs a neural network-based model-free controller along with the penalty function. Simultaneous perturbation stochastic gradient approximation method is used to iteratively estimate the weights of neural network and as a result to estimate the control values. Furthermore, the study uses a special cause control chart to monitor the performance of the controller in reducing the process variations and to signal the change in the process dynamics. Simulation findings indicate that the neural network model-free provides control values that result in fewer nonconforming outputs than when the requirements are not incorporated in optimization process.