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dc.contributor.authorHartikainen, Markus
dc.contributor.authorEyvindson, Kyle
dc.contributor.authorMiettinen, Kaisa
dc.contributor.authorKangas, Annika
dc.contributor.editorPardalos, Panos M.
dc.contributor.editorConca, Piero
dc.contributor.editorGiuffrida, Giovanni
dc.contributor.editorNicosia, Giuseppe
dc.date.accessioned2017-01-23T14:28:47Z
dc.date.available2017-01-23T14:28:47Z
dc.date.issued2016
dc.identifier.citationHartikainen, M., Eyvindson, K., Miettinen, K., & Kangas, A. (2016). Data-Based Forest Management with Uncertainties and Multiple Objectives. In P. M. Pardalos, P. Conca, G. Giuffrida, & G. Nicosia (Eds.), <i>Machine Learning, Optimization, and Big Data : Second International Workshop, MOD 2016, Volterra, Italy, August 26-29, 2016, Revised Selected Papers</i> (pp. 16-29). Springer. Lecture Notes in Computer Science, 10122. <a href="https://doi.org/10.1007/978-3-319-51469-7_2" target="_blank">https://doi.org/10.1007/978-3-319-51469-7_2</a>
dc.identifier.otherCONVID_26483352
dc.identifier.otherTUTKAID_72637
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/52802
dc.description.abstractIn this paper, we present an approach of employing multiobjective optimization to support decision making in forest management planning. The planning is based on data representing so-called stands, each consisting of homogeneous parts of the forest, and simulations of how the trees grow in the stands under different treatment options. Forest planning concerns future decisions to be made that include uncertainty. We employ as objective functions both the expected values of incomes and biodiversity as well as the value at risk for both of these objectives. In addition, we minimize the risk level for both the income value and the biodiversity value. There is a tradeoff between the expected value and the value at risk, as well as between the value at risk of the two objectives of interest and, thus, decision support is needed to find the best balance between the conflicting objectives. We employ an interactive method where a decision maker iteratively provides preference information to find the most preferred management plan and at the same time learns about the interdependencies of the objectives.
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofMachine Learning, Optimization, and Big Data : Second International Workshop, MOD 2016, Volterra, Italy, August 26-29, 2016, Revised Selected Papers
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.subject.othermultiobjective optimization
dc.subject.otherinteractive multiobjective optimization
dc.subject.otherPareto optimality
dc.titleData-Based Forest Management with Uncertainties and Multiple Objectives
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201701161158
dc.contributor.laitosTietotekniikan laitosfi
dc.contributor.laitosDepartment of Mathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2017-01-16T10:15:14Z
dc.relation.isbn978-3-319-51468-0
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange16-29
dc.relation.issn0302-9743
dc.type.versionacceptedVersion
dc.rights.copyright© 2016 Springer International Publishing AG. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational workshop on machine learning, optimization and big data
dc.subject.ysometsäsuunnittelu
dc.subject.ysoepävarmuus
jyx.subject.urihttp://www.yso.fi/onto/yso/p1863
jyx.subject.urihttp://www.yso.fi/onto/yso/p1722
dc.relation.doi10.1007/978-3-319-51469-7_2
dc.type.okmA4


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