OPTIMIZATION AND DECISION MAKING STAGES FOR ANALYSIS OF DUAL RESPONSE PROBLEM: MEDIAN-MAD, NSGA-II, TOPSIS

Yıl 2019, Cilt: 20 Sayı: 4, 458 - 473, 30.12.2019

Özlem Türkşen , Ahmet Kocatürk , Gözde Karakoç

https://doi.org/10.18038/estubtda.515702

Öz

The
purpose of this study is to obtain a proper set of input variables for
replicated response measured data set by applying dual response strategy in
robust framework with multi-objective perspective. The replicated response
measures were transformed to dual response by using robust statistics, median (MED) and median absolute deviation (MAD), instead of mean-standard deviation
statistics which were very commonly used in many existing studies. A compromise
solution of proposed robust dual response model was obtained via a
multi-criteria decision making approach since the optimization was achieved in
multi-objective point of view. In this study, well-known two methods, NSGA-II
and TOPSIS, were preferred for optimization and decision making stages,
respectively. Quality of printing ink data set was used an application from the
literature. It is seen from the analysis results that the performance of the
proposed robust dual response model was encouraging with the most satisfactory
input settings.

Anahtar Kelimeler

Replicated response measures, Dual response problem, Robust statistics, Multiobjective optimization, Multi-criteria decision making

Kaynakça

• Myers RH, Carter WH. Response surface techniques for dual response systems. Technometrics 1973; 15(2): 301-317.
• Vining GG, Myers RH. Combining Taguchi and response surface philosophies: a dual response approach. Journal of Quality Technology 1990; 22: 38-45.
• Lin DKJ, Tu W. Dual Response Surface Optimization. Journal of Quality Technology 1995; 27(1): 34-39.
• Copeland KAF, Nelson PR. Dual Response Optimization via Direct Function Minimization. Journal of Quality Technology 1996; 28(3): 331-336.
• Del Castillo E. Multiresponse Process Optimization via Constrained Confidence Regions. Journal of Quality Technology 1996; 28(1): 61-70.
• Kim KJ, Lin DKJ. Dual Response Surface Optimization: A Fuzzy Modeling Approach. Journal of Quality Technology 1998; 30(1): 1-10.
• Khoo LP, Chen CH. Integration of response surface methodology with genetic algorithms. The International Journal of Advanced Manufacturing Technology 2001; 18(7): 483-489.
• Ding R, Lin DK, Wei D. Dual-response surface optimization: A weighted MSE approach. Quality engineering 2004; 16(3): 377-385.
• Jeong IJ, Kim KJ, Chang SY. Optimal weighting of bias and variance in dual response surface optimization. Journal of Quality Technology 2005; 37(3): 236-247.
• Shaibu AB, Cho BR. Another view of dual response surface modeling and optimization in robust parameter design. The International Journal of Advanced Manufacturing Technology 2009; 41: 631-641.
• Lee DH, Jeong IJ, Kim KJ. A posterior preference articulation approach to dual-response-surface optimization. IIE Transactions 2010; 42: 161–171.
• Baba I, Midi H, Rana S, Ibragimov G. An Alternative Approach of Dual Response Surface Optimization Based on Penalty Function Method. Mathematical Problems in Engineering 2015; 1-6.
• Le TH, Shin S. A literature review on RSM-based robust parameter design (RPD): Experimental design, estimation modeling, and optimization methods. Journal of the Korean Society for Quality Management 2018; 46(1): 39-74.
• Park C, Cho B. Development of Robust Design Under Contamined and Non-normal Data. Quality engineering 2003; 15(3): 463-469.
• Lee SB, Park C, Cho BR. Development of a highly efficient and resistant robust design. International Journal of Production Research 2007; 45(1): 157-167.
• Deb K, Pratap A, Agarwal S, Meyarivan T. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 2002; 6(2): 182-197.
• Chen SJ, Hwang CL. Fuzzy multiple attribute decision making methods. In: Fuzzy multiple attribute decision making. Berlin, Heidelberg: Springer, 1992. pp. 289-486.
• Hwang CL, Yoon K. Multiple Attribute Decision Making. In: Lecture Notes in Economics and Mathematical Systems. Berlin: Springer-Verlag, 1981.
• Khuri AI, Cornell JA. Response surfaces: designs and analyses. CRC press 1996; 152.
• Ames AE, Mattucci N, Macdonald S, Szonyi G, Hawkins DM. Quality loss functions for optimization across multiple response surfaces. Journal of Quality Technology 1997; 29(3): 339-346.
• Del Castillo E, Montgomery DC. A Nonlinear Programming Solution to the Dual Response Problem. Journal of Quality Technology 1993; 25(3): 199-204.
• Cho BR, Philips MD, Kapur KC. Quality improvement by RSM modeling for robust design. In: The 5th Industrial Engineering Research Conference; 1996; Minneapolis. pp. 650-655.
• Köksoy O, Doğanaksoy N. Joint Optimization of Mean and Standard Deviation Using Response Surface Methods. Journal of Quality Technology 2003; 35(3): 239-252.
• Shin S, Cho BR. Studies on a biobjective robust design optimization problem. IIE Transactions 2009; 41(11): 957-968.
• Truong NKV, Shin S. Development of a new robust design methodology based on Bayesian perspectives. International Journal of Quality Engineering and Technology 2012; 3(1): 50-78.
• Nha VT, Shin S, Jeong SH. Lexicographical dynamic goal programming approach to a robust design optimization within the pharmaceutical environment. European Journal of Operational Research 2013; 229(2): 505-517.
• Jeong IJ, Lee DH. Generating evenly distributed nondominated solutions in dual response surface optimization. Quality Technology & Quantitative Management 2017; 1-17.
• Lee DH, Kim BR, Yang JK, Oh SH. Dual Response Surface Optimization using Multiple Objective Genetic Algorithms. Journal of the Korean Institute of Industrial Engineers 2017; 43(3): 164-175.
• Park C, Leeds M. A highly efficient robust design under data contamination. Computers & Industrial Engineering 2016; 93: 131-142.
• Chen CT, Lin CT, Huang SF. A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics 2006; 102: 289-301.
• Box GEP, Draper NR. Wiley series in probability and mathematical statistics. Empirical model-building and response surfaces. Oxford: John Wiley & Sons, 1987.
• Türkşen Ö, Tez M. An application of nelder-mead heuristic-based hybrid algorithms: estimation of compartment model parameters. International Journal of Artificial Intelligence 2016; 14(1): 112-129.