<?xml version="1.0" encoding="UTF-8"?>

<article xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article" dtd-version="1.3" xml:lang="en">
 <front>
  <journal-meta>
   <journal-id journal-id-type="publisher">setsci</journal-id>
   <journal-title-group>
    <journal-title>SETSCI Conference Proceedings</journal-title>
   </journal-title-group>
   <issn publication-format="electronic">2687-5527</issn>
   <publisher>
    <publisher-name>SET Technology</publisher-name>
   </publisher>
  </journal-meta>
  <article-meta>
   <article-id pub-id-type="doi">10.36287/setsci.4.6.081</article-id>
   <article-id pub-id-type="publisher-id">19f3f866-2468-4d6a-aeed-945ca2eb5802</article-id>
   <title-group>
    <article-title>Development of a Dual Response Optimization Model under Non-standard Experimental Design Situations</article-title>
   </title-group>
   <contrib-group>
    <contrib contrib-type="author" corresp="yes">
     <name>
      <surname>Özdemir</surname>
      <given-names>Akın</given-names>
     </name>
     <xref ref-type="aff" rid="aff1"/>
    </contrib>
   </contrib-group>
   <aff id="aff1">
    <institution>Bayburt University</institution>
   </aff>
   <pub-date date-type="pub">
    <day>22</day>
    <month>12</month>
    <year>2019</year>
   </pub-date>
   <volume>9</volume>
   <fpage>316</fpage>
   <lpage>319</lpage>
   <permissions>
    <copyright-statement>© 2019 SET Technology</copyright-statement>
    <copyright-year>2019</copyright-year>
    <copyright-holder>SET Technology</copyright-holder>
    <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
     <license-p>CC BY 4.0</license-p>
    </license>
   </permissions>
   <abstract>
    <p>The design of experiments is a highly effective offline quality improvement method to optimize the existing and new processes or products. In the literature, standard experimental situations have been paid a lot of attention. In a number of non-standard experimental situations, special experimental design techniques should be considered in order to conduct an experiment for design factors. Indeed, an I-optimal design, a computer-generated special experimental design, is a good choice to predict the mean and variance responses under non-standard experimental design situations. In this research work, an I-optimal design is selected to generate experimental design points for a non-standard experimental situation. Then, an I-optimal design-based dual response optimization model is proposed in order to obtain an optimum operating condition for design factors while minimizing the process variance as small as possible. Comparison studies are also conducted. Finally, a numerical example is conducted in order to illustrate the effectiveness of the proposed optimization model.</p>
   </abstract>
   <kwd-group xml:lang="en">
    <kwd>Quality improvement, Dual Response Model, Non-standard Experimental Design, I-optimal design, Optimization</kwd>
   </kwd-group>
   <kwd-group xml:lang="en">
    <kwd>Quality improvement, Dual Response Model, Non-standard Experimental Design, I-optimal design, Optimization</kwd>
   </kwd-group>
  </article-meta>
 </front>
 <back>
  <ref-list>
   <ref id="ref1">
    <mixed-citation>J. J. Borkowski, “A comparison of prediction variance criteria for response surface designs,” J. Qual. Technol., vol. 35(1), pp. 70-77, 2003.</mixed-citation>
   </ref>
   <ref id="ref2">
    <mixed-citation>G. E. P. Box and N. R. Draper, “The choice of a second order rotatable design,” Biometrika, vol. 50(3/4): pp. 335-352, 1963.</mixed-citation>
   </ref>
   <ref id="ref3">
    <mixed-citation>N. R. Draper, “Center points in second-order response surface designs,” Technometrics, vol. 24(2), pp. 127-133, 1982.</mixed-citation>
   </ref>
   <ref id="ref4">
    <mixed-citation>G. E. P. Box and N. R. Draper, “A basis for the selection of response surface design,” J. Am. Stat. Assoc., vol. 54(287): pp. 622-654, 1959.</mixed-citation>
   </ref>
   <ref id="ref5">
    <mixed-citation>T. T. Allen and S. H. Tseng, “Variance plus bias optimal response surface designs with qualitative factors applied to stem choice modeling,” Qual. Reliab. Eng. Int., vol. 27(8), pp. 1199-1210, 2011.</mixed-citation>
   </ref>
   <ref id="ref6">
    <mixed-citation>H. H. Toro Díaz HH, H. L. Chan HL, and B. R. Cho, “Optimally designing experiments under non-standard experimental situations,” International Journal of Experimental Design and Process Optimisation, vol. 3(2), pp. 133-158, 2012.</mixed-citation>
   </ref>
   <ref id="ref7">
    <mixed-citation>R. H. Myers, D. C. Montgomery, and C. M. Anderson-Cook, Response Surface Methodology: Process and Product Optimization Using Designed Experiments (Wiley Series in Probability and Statistics), Hoboken, NJ: Wiley, 2016.</mixed-citation>
   </ref>
   <ref id="ref8">
    <mixed-citation>G. Taguchi, Introduction to Quality Engineering, UNIPUB/Kraus International: White Plains, NY; 1986.</mixed-citation>
   </ref>
   <ref id="ref9">
    <mixed-citation>G. G. Vining and R. H. Myers, “Combining Taguchi and response surface philosophies: a dual response approach,” J. Qual. Technol., vol. 22(1), pp. 38-45, 1990.</mixed-citation>
   </ref>
   <ref id="ref10">
    <mixed-citation>Y. Fathi, “A nonlinear programming approach to the parameter design problem,” Eur. J. Oper. Res., vol. 53(3), pp. 371-381, 1991.</mixed-citation>
   </ref>
   <ref id="ref11">
    <mixed-citation>E. Del Castillo and D. C. Montgomery, “A nonlinear programming solution to the dual response problem,” J. Qual. Technol., vol. 25(3), pp. 199-204, 1993.</mixed-citation>
   </ref>
   <ref id="ref12">
    <mixed-citation>D. K. Lin and W. Tu, “Dual response surface optimization,” J. Qual. Technol., vol. 27(1), pp. 34-39, 1995.</mixed-citation>
   </ref>
   <ref id="ref13">
    <mixed-citation>K. A. Copeland and P. R. Nelson, “Dual response optimization via direct function minimization,” J. Qual. Technol., vol. 28(3), pp. 331-336, 1996.</mixed-citation>
   </ref>
   <ref id="ref14">
    <mixed-citation>D. M. Steinberg and D. Bursztyn, “Noise factors, dispersion effects, and robust design,” Stat. Sinica, vol. 8(1), pp. 67-85, 1998.</mixed-citation>
   </ref>
   <ref id="ref15">
    <mixed-citation>T. J. Robinson and C. M. Anderson-Cook, “A closer look at D-optimality for screening designs,” Qual. Eng., vol. 23(1), pp. 1-14, 2010.</mixed-citation>
   </ref>
   <ref id="ref16">
    <mixed-citation>G. J. Park, T. H. Lee, K. H. Lee, and K. H. Hwang, “Robust design: an overview,” AIAA J., vol. 44(1), pp. 181-191, 2006.</mixed-citation>
   </ref>
   <ref id="ref17">
    <mixed-citation>M. Arvidsson and I. Gremyr, “Principles of robust design methodology,” Qual. Reliab. Eng. Int., vol. 24(1), pp. 23-35, 2008.</mixed-citation>
   </ref>
   <ref id="ref18">
    <mixed-citation>L. Ouyang, Y. Ma, J. H. Byun, J. Wang, and Y. Tu, “An interval approach to robust design with parameter uncertainty,” Int. J. Prod. Res., vol. 54(11), pp. 3201-3215, 2016.</mixed-citation>
   </ref>
   <ref id="ref19">
    <mixed-citation>A. Ozdemir and B. R. Cho, “A Nonlinear integer programming approach to solving the robust parameter design optimization problem,” Qual. Reliab. Eng. Int., vol. 32(8), pp. 2859-2870, 2016.</mixed-citation>
   </ref>
   <ref id="ref20">
    <mixed-citation>A. Ozdemir and B. R. Cho, “Response surface-based robust parameter design optimization with both qualitative and quantitative variables.” Eng. Optimiz., vol. 49(10), pp. 1796-1812, 2017.</mixed-citation>
   </ref>
   <ref id="ref21">
    <mixed-citation>A. Ozdemir and B. R. Cho, “Response surface optimization for a nonlinearly constrained irregular experimental design space,” Eng. Optimiz., vol. 51(12), pp. 2030-2048, 2019.</mixed-citation>
   </ref>
   <ref id="ref22">
    <mixed-citation>T. N. Tsai and M. Liukkonen, “Robust parameter design for the micro-BGA stencil printing process using a fuzzy logic-based Taguchi method,” Appl. Soft Comput., vol. 48, pp. 124-136, 2016.</mixed-citation>
   </ref>
   <ref id="ref23">
    <mixed-citation>A. Hot, T. Weisser, and S. Cogan, “An info-gap application to robust design of a prestressed space structure under epistemic uncertainties,” Mech. Syst. Signal Pr., vol. 91, pp. 1-9, 2017.</mixed-citation>
   </ref>
   <ref id="ref24">
    <mixed-citation>Y. Lu, S. Wang, C. Yan, and Z. Huang, “Robust optimal design of renewable energy system in nearly/net zero energy buildings under uncertainties,” Appl. Energ., vol. 187, pp. 62-71, 2017.</mixed-citation>
   </ref>
   <ref id="ref25">
    <mixed-citation>K. Chatterjee, K. Drosou, S. D. Georgiou, and C. Koukouvinos, “Response modelling approach to robust parameter design methodology using supersaturated designs,” J. Qual. Technol., vol. 50(1), pp. 66-75, 2018</mixed-citation>
   </ref>
  </ref-list>
 </back>
</article>
