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dc.contributor.authorAn, Qien_US
dc.contributor.authorFang, Shu-Cherngen_US
dc.contributor.authorLi, Han -Linen_US
dc.contributor.authorNie, Tiantianen_US
dc.date.accessioned2018-08-21T05:53:33Z-
dc.date.available2018-08-21T05:53:33Z-
dc.date.issued2018-06-01en_US
dc.identifier.issn0307-904Xen_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.apm.2017.09.047en_US
dc.identifier.urihttp://hdl.handle.net/11536/144845-
dc.description.abstractIn this paper, we significantly extend the applicability of state-of-the-art ELDP (equations for linearizing discrete product terms) method by providing a new linearization to handle more complicated non-linear terms involving both of discrete and bounded continuous variables. A general class of "representable programming problems" is formally proposed for a much wider range of engineering applications. Moreover, by exploiting the logarithmic feature embedded in the discrete structure, we present an enhanced linear reformulation model which requires half an order fewer equations than the original ELDP. Computational experiments on various engineering design problems support the superior computational efficiency of the proposed linearization reformulation in solving engineering optimization problems with discrete and bounded continuous variables. (C) 2017 Elsevier Inc. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectNonlinear discrete optimizationen_US
dc.subjectLinear reformulationen_US
dc.subjectPolynomial programmingen_US
dc.subjectSignomial programmingen_US
dc.titleEnhanced linear reformulation for engineering optimization models with discrete and bounded continuous variablesen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.apm.2017.09.047en_US
dc.identifier.journalAPPLIED MATHEMATICAL MODELLINGen_US
dc.citation.volume58en_US
dc.citation.spage140en_US
dc.citation.epage157en_US
dc.contributor.department資訊管理與財務金融系 註:原資管所+財金所zh_TW
dc.contributor.departmentDepartment of Information Management and Financeen_US
dc.identifier.wosnumberWOS:000430037400012en_US
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