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dc.contributor.authorTone, Kaoruen_US
dc.contributor.authorChang, Tsung-Shengen_US
dc.contributor.authorWu, Chen-Huien_US
dc.date.accessioned2020-05-05T00:01:28Z-
dc.date.available2020-05-05T00:01:28Z-
dc.date.issued2020-05-01en_US
dc.identifier.issn0377-2217en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.ejor.2019.09.055en_US
dc.identifier.urihttp://hdl.handle.net/11536/153901-
dc.description.abstractThis paper proposes slacks-based measure (SBM) data envelopment analysis (DEA) models that handle negative data. Unlike existing negative data allowable DEA models, the proposed SBM DEA models are consistent with ordinary SBM models and units invariant, they handle various types of returns to scale, and they avoid division by zero. These new SBM DEA models transform original negative inputs and outputs into positive counterparts based on a newly defined "base point". Hence, these models are referred to as the BP-SBM DEA models. In addition to the basic BP-SBM DEA models, this research further develops data-oriented and application-oriented BP-SBM DEA-type models for different application problems involving negative data. Numerical examples are provided to illustrate various aspects and implementation details of these models. (C) 2019 Elsevier B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectData envelopment analysisen_US
dc.subjectSlacks-based measureen_US
dc.subjectNegative dataen_US
dc.subjectBP-SBMen_US
dc.subjectDivision by zero irrationalityen_US
dc.titleHandling negative data in slacks-based measure data envelopment analysis modelsen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.ejor.2019.09.055en_US
dc.identifier.journalEUROPEAN JOURNAL OF OPERATIONAL RESEARCHen_US
dc.citation.volume282en_US
dc.citation.issue3en_US
dc.citation.spage926en_US
dc.citation.epage935en_US
dc.contributor.department運輸與物流管理系 註:原交通所+運管所zh_TW
dc.contributor.departmentDepartment of Transportation and Logistics Managementen_US
dc.identifier.wosnumberWOS:000513985000010en_US
dc.citation.woscount0en_US
Appears in Collections:Articles