Industrial and Systems Engineering Department, Wayne State University
Industrial and Systems Engineering, Wayne State University
Department of Industrial and Operations Engineerin, University of Michigan
The quality is typically modeled as the univariate or multivariate distribution of quality characteristic/s. In recent applications of statistical process control, quality profiles in which the relationship between a response and explanatory variable/s is captured and monitored are increasingly used to model the quality. Several techniques have been developed to enhance the speed of detecting changes in parameters of polynomial profiles. In this paper, we consider the effect of allocating the explanatory variable X in optimizing the performance of one of well-known methods referred to as EWMA4 method. An optimization model is built and solved using the genetic algorithm to find the optimal location of - values that minimizes the average of the run length distribution (ARL). The effect of location optimization is studied using a simulation study and results are compared with non-optimized strategies in terms of average run length criterion.