Track: Optimization
Abstract
This paper proposes a linear search Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm to maximize the signal-to-noise ratio (SNR) index in the least number of iterations and determine which succession measure converges and maximizes the signal-to-noise ratio (SNR) in the least number of iterations. The analysis and validation are performed with experimental simulation and validated with four case studies collected from the literature. The case studies were evaluated in ten experiments with different combinations of the succession measures 
and 
. The results obtained show that the proposed AAEPS is an iterative, efficient and easy to use method to maximize the quality indexes of production processes, which is feasible to implement within the six sigma methodology.