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dc.contributor.authorZhou-Kangas, Yue
dc.contributor.authorMiettinen, Kaisa
dc.date.accessioned2019-05-20T06:50:31Z
dc.date.available2019-05-20T06:50:31Z
dc.date.issued2019
dc.identifier.citationZhou-Kangas, Y., & Miettinen, K. (2019). Decision making in multiobjective optimization problems under uncertainty : balancing between robustness and quality. <i>OR Spektrum</i>, <i>41</i>(2), 391-413. <a href="https://doi.org/10.1007/s00291-018-0540-4" target="_blank">https://doi.org/10.1007/s00291-018-0540-4</a>
dc.identifier.otherCONVID_28713257
dc.identifier.otherTUTKAID_79463
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/64056
dc.description.abstractAs an emerging research field, multiobjective robust optimization employs minmax robustness as the most commonly used concept. Light robustness is a concept in which a parameter, tolerable degradations, can be used to control the loss in the objective function values in the most typical scenario for gaining in robustness. In this paper, we develop a lightly robust interactive multiobjective optimization method, LiRoMo, to support a decision maker to find a most preferred lightly robust efficient solution with a good balance between robustness and the objective function values in the most typical scenario. In LiRoMo, we formulate a lightly robust subproblem utilizing an achievement scalarizing function which involves a reference point specified by the decision maker. With this subproblem, we compute lightly robust efficient solutions with respect to the decision maker’s preferences. With LiRoMo, we support the decision maker in understanding the lightly robust efficient solutions with an augmented value path visualization. We use two measures ‘price to be paid for robustness’ and ‘gain in robustness’ to support the decision maker in considering the trade-offs between robustness and quality. As an example to illustrate the advantages of the method, we formulate and solve a simple investment portfolio optimization problem.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofseriesOR Spektrum
dc.rightsCC BY 4.0
dc.subject.othermultiobjective robust optimization
dc.subject.otherinteractive methods
dc.subject.otherlight robust efficiency
dc.subject.otherhandling uncertainty
dc.subject.othertrade-off between robustness and quality
dc.subject.otherdecision support
dc.titleDecision making in multiobjective optimization problems under uncertainty : balancing between robustness and quality
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201905152625
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2019-05-15T15:15:09Z
dc.description.reviewstatuspeerReviewed
dc.format.pagerange391-413
dc.relation.issn0171-6468
dc.relation.numberinseries2
dc.relation.volume41
dc.type.versionpublishedVersion
dc.rights.copyright© The Authors, 2018.
dc.rights.accesslevelopenAccessfi
dc.subject.ysomonitavoiteoptimointi
dc.subject.ysooptimointi
dc.subject.ysopäätöksenteko
dc.subject.ysoepävarmuus
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p32016
jyx.subject.urihttp://www.yso.fi/onto/yso/p13477
jyx.subject.urihttp://www.yso.fi/onto/yso/p8743
jyx.subject.urihttp://www.yso.fi/onto/yso/p1722
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1007/s00291-018-0540-4


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CC BY 4.0
Except where otherwise noted, this item's license is described as CC BY 4.0