Considering Preference Parameters in Multi Response Surface Optimization Approaches

Abstract

Most of the studies in Response Surface Methodology commonly involve one response or quality characteristics, whereas in most industrial applications considering all responses simultaneously is required. Multiple Response Surface (MRS) Optimization Problems often deal with responses that are conflicting. In dealing with incommensurate responses, incorporating a decision maker's preference information into the problem has lots of advantages although a few researches in MRS literature has taken this into attention. This paper tends to take a detailed look at the most prominent approaches that has been suggested so far in MRS, also review and discuss the classifications with a special focus on the decision maker's preference information. In today's competitive market satisfying the customer is of high importance. The DM can be a customer and reaching a compromise with an interactive method would help the firm to succeed in having loyal customers. Results of the case study shows that applying a meta-heuristic algorithm with existing MRS approaches lead to better findings .finally future areas for research are discussed.

Prediction of steel plate-based damper for improving the behavior of concentrically braced frames based on RSM and ML approaches for sustainable structures

“…By applying these techniques, RSM helps in understanding the relationship between factors and responses, optimizing processes, and improving product or process performance. In the context of structural design of steel structures, Response Surface Methodology (RSM) can be used to develop models that relate various design factors to the response variables of interest, such as structural performance, safety, or cost 46 . These models can then be used for optimization or prediction purposes.…”