An efficient adaptive sequential methodology for expensive response surface optimization

The preset response surface methodology (RSM) designs are commonly used in a wide range of process and design optimization applications. Although they offer ease of implementation and good performance, they are not sufficiently adaptive to reduce the required number of experiments and thus are not cost effective for applications with high cost of experimentation. We propose an efficient adaptive sequential methodology based on optimal design and experiments ranking for response surface optimization (O-ASRSM) for industrial experiments with high experimentation cost, limited experimental resources, and requiring high design optimization performance. The proposed approach combines the concepts from optimal design of experiments, nonlinear optimization, and RSM. By using the information gained from the previous experiments, O-ASRSM designs the subsequent experiment by simultaneously reducing the region of interest and by identifying factor combinations for new experiments. Given a given response target, O-ASRSM identifies the input factor combination in less number of experiments than the classical single-shot RSM designs. We conducted extensive simulated experiments involving quadratic and nonlinear response functions. The results show that the O-ASRSM method outperforms the popular central composite design, the Box-Behnken design, and the optimal designs and is competitive with other sequential response surface methods in the literature. Furthermore, results indicate that O-ASRSM's performance is robust with respect to the increasing number of factors.