A Review of Bayesian Reliability Approaches to Multiple Response Surface Optimization

2006 ◽  
pp. 281-302
Keyword(s):  
Response Surface ◽  
Multiple Response ◽  
Response Surface Optimization ◽  
Surface Optimization ◽  
Multiple Response Surface Optimization

Multiple response surface optimization with correlated data

2012 ◽  
Vol 64 (5-8) ◽  
pp. 841-855 ◽  
Author(s):  
Ali Salmasnia ◽  
Reza Baradaran Kazemzadeh ◽  
Mirmehdi Seyyed-Esfahani ◽  
Taha Hossein Hejazi
Keyword(s):  
Response Surface ◽  
Correlated Data ◽  
Multiple Response ◽  
Response Surface Optimization ◽  
Surface Optimization ◽  
Multiple Response Surface Optimization

Multiple Response Surface Optimization Considering the Decision Maker’s Preference Information

2012 ◽  
Vol 433-440 ◽  
pp. 1646-1652
Author(s):  
Amineh Zadbood ◽  
Kazem Noghondarian
Keyword(s):  
Response Surface ◽  
Optimization Problems ◽  
Industrial Applications ◽  
Special Focus ◽  
Multiple Response ◽  
Preference Information ◽  
Interactive Method ◽  
Response Surface Optimization ◽  
Multiple Response Surface Optimization

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 have taken this into attention. The purpose of this paper is to take a detailed look at the most prominent approaches that has been suggested so far in MRS, and also to 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. A case study is applied to show that interactive method with existing MRS approach leads to better results.

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