A solution selection approach to multiresponse surface optimization based on a clustering method

Abstract

In Multi-Response Surface Optimization (MRSO), responses are often in conflict. To obtain a satisfactory compromise, the preference information of a Decision Maker (DM) on the tradeoffs among the responses should be considered. One of the promising alternatives is a posterior preference articulation approach. It first generates nondominated solutions and then makes the DM select the best one from the nondominated solutions. In this article, a solution selection approach is presented. It takes the posterior approach and employs a clustering method to aid the selection process of the DM. The DM can obtain the satisfactory compromise solution easily by the proposed method.

Optimization of degree of conformance for multi‐response systems considering model imprecision: A data‐driven metaheuristic approach

“…Several survey research studies have been carried out to classify the existing approach of multiresponse optimization (MRO). [1][2][3][4][5][6] There is an important question that should be answered before optimization. What measure(s) of response variables would be considered for optimization?…”

“…In the case of multiple response surfaces, some aggregation or priority ordering method requires resolving the problem of multiple objective decision‐making. Several survey research studies have been carried out to classify the existing approach of multi‐response optimization (MRO) 1–6 …”

Multiresponse optimization of a multistage manufacturing process using a patient rule induction method

“…Second, it is common to have multiple responses at each stage. The authors have encountered several multiresponse problems associated with the multistage process in various industries, such as the machinery, chemical, semiconductor, and steel industries 2 . Despite its importance, few studies have been conducted to optimize the multiresponse of the multistage process.…”