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dc.contributor.authorChang, Tsung-Shengen_US
dc.contributor.authorTone, Kaoruen_US
dc.contributor.authorWu, Chen-Huien_US
dc.date.accessioned2017-04-21T06:56:31Z-
dc.date.available2017-04-21T06:56:31Z-
dc.date.issued2016-10-16en_US
dc.identifier.issn0377-2217en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.ejor.2016.04.005en_US
dc.identifier.urihttp://hdl.handle.net/11536/133861-
dc.description.abstractConventional data envelopment analysis (DEA) models are designed for measuring the productive efficiency of decision making units (DMUs) based merely on historical data. However, in many practical applications, such past results are not sufficient for evaluating a DMU\'s performance in highly volatile operating environments, such as those with highly volatile crude oil prices and currency exchange rates. That is, in such environments, a DMU\'s whole performance may be seriously distorted if its future performance, which is sensitive to crude oil price volatility and/or currency fluctuations, is ignored in the evaluation process. However, despite its importance, to our knowledge, there are no DEA models proposed in the literature that explicitly take future performance volatility into account. Hence, this research aims at developing a new system of DEA models that incorporate a DMU\'s uncertain future performance, and thus can be applied to fully measure their efficiency. (C) 2016 Elsevier B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectData envelopment analysisen_US
dc.subjectVolatilityen_US
dc.subjectForecasten_US
dc.subjectDynamicen_US
dc.subjectEntropyen_US
dc.titleDEA models incorporating uncertain future performanceen_US
dc.identifier.doi10.1016/j.ejor.2016.04.005en_US
dc.identifier.journalEUROPEAN JOURNAL OF OPERATIONAL RESEARCHen_US
dc.citation.volume254en_US
dc.citation.issue2en_US
dc.citation.spage532en_US
dc.citation.epage549en_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:000377732300015en_US
Appears in Collections:Articles