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dc.contributor.author이동희-
dc.date.accessioned2019-11-22T01:59:24Z-
dc.date.available2019-11-22T01:59:24Z-
dc.date.issued2017-03-
dc.identifier.citationCOMPUTERS & INDUSTRIAL ENGINEERING, v. 105, page. 76-83en_US
dc.identifier.issn0360-8352-
dc.identifier.issn1879-0550-
dc.identifier.urihttps://www.sciencedirect.com/science/article/abs/pii/S0360835216305022?via%3Dihub-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/113459-
dc.description.abstractIn this paper, considering the uncertainty associated with the fitted response surface models and the satisfaction degrees of the response values with respect to the given targets, we construct the robust membership functions of the responses in three cases and explain their practical meanings. We translate the feasible regions of multiple responses optimization (MRO) problems into partial derivative-level sets and incorporate the model uncertainty with the confidence intervals simultaneously to ensure the robustness of the feasible regions. Then we develop the robust fuzzy programming (RFP) approach to solve the multiple responses optimization (MRO) problems. The key advantage of the presented method is that it takes account of the location effect, dispersion effect and model uncertainty of the multiple responses simultaneously and thus can ensure the robustness of the solution. An example from literatures is illustrated to show the practicality and effectiveness of the proposed algorithm. Finally some comparisons and discussions are given to further illustrate the developed approach. (C) 2016 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipThe work was supported in part by the National Natural Science Foundation of China (Nos. 71225006, 71532008), in part by the National Research Foundation of Korea (No. 2015R1C1A1A01051952), and in part by the project of the National Natural Science Foundation of China and the National Research Foundation of Korea.en_US
dc.language.isoen_USen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.subjectRobust fuzzy programming approachen_US
dc.subjectMultiple responses optimizationen_US
dc.subjectRobust desirability membership functionsen_US
dc.subjectpartial derivative-level setsen_US
dc.titleRobust fuzzy programming method for MRO problems considering location effect, dispersion effect and model uncertaintyen_US
dc.typeArticleen_US
dc.relation.volume105-
dc.identifier.doi10.1016/j.cie.2016.12.021-
dc.relation.page76-83-
dc.relation.journalCOMPUTERS & INDUSTRIAL ENGINEERING-
dc.contributor.googleauthorHe, Yingdong-
dc.contributor.googleauthorHe, Zhen-
dc.contributor.googleauthorLee, Dong-Hee-
dc.contributor.googleauthorKim, Kwang-Jae-
dc.contributor.googleauthorZhang, Lin-
dc.contributor.googleauthorYang, Xiaoxi-
dc.relation.code2017006362-
dc.sector.campusS-
dc.sector.daehakDIVISION OF INDUSTRIAL INFORMATION STUDIES[S]-
dc.sector.departmentDIVISION OF INDUSTRIAL INFORMATION STUDIES-
dc.identifier.piddh-
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