Bambang Widjanorko Otok
Faculty of Mathematics and Natural Sciences, Sepuluh Nopember Institute of Technology (ITS), Surabaya

Published : 2 Documents
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FUZZY MODELING APPROACH AND GLOBAL OPTIMIZATION FOR DUAL RESPONSE SURFACE

Jurnal Teknik Industri Vol 9, No 2 (2007): DECEMBER 2007
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Original Source | Check in Google Scholar | Full PDF (220.088 KB)

Abstract

Dual Response Surface (DRS) with Lagrange multiplier is one of the most familiar classical multi response surface methods. Classical DRS optimization doesn´t concern about the quality characteristic of responses. In this paper, fuzzy approach is proposed for modeling DRS and quality characteristic of response simultaneously. The proposed method represented the object´s quality characteristic physically. The proposed method is applied to composite carbon drilling process and resulting nonlinear function that to be determined its optimal point. Many optimization methods fail to reach global optimum point because the non linear function is multimodal. Therefore, we used genetic algorithm for finding the global optimum point.

FUZZY MODELING APPROACH AND GLOBAL OPTIMIZATION FOR DUAL RESPONSE SURFACE

Jurnal Teknik Industri Vol 9, No 2 (2007): DECEMBER 2007
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Original Source | Check in Google Scholar | Full PDF (220.088 KB)

Abstract

Dual Response Surface (DRS) with Lagrange multiplier is one of the most familiar classical multi response surface methods. Classical DRS optimization doesnt concern about the quality characteristic of responses. In this paper, fuzzy approach is proposed for modeling DRS and quality characteristic of response simultaneously. The proposed method represented the objects quality characteristic physically. The proposed method is applied to composite carbon drilling process and resulting nonlinear function that to be determined its optimal point. Many optimization methods fail to reach global optimum point because the non linear function is multimodal. Therefore, we used genetic algorithm for finding the global optimum point.