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dc.contributor.authorHatami-Marbini, A.en
dc.contributor.authorGhelej Beigi, Z.en
dc.contributor.authorHougaard, J. L.en
dc.contributor.authorGholami, K.en
dc.date.accessioned2018-01-02T15:37:09Z
dc.date.available2018-01-02T15:37:09Z
dc.date.issued2017-12-27
dc.identifier.citationHatami-Marbini, A. et al. (2017). Measurement of Returns-to-Scale using Interval Data Envelopment Analysis Models. Computers & Industrial Engineeringen
dc.identifier.urihttp://hdl.handle.net/2086/15044
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linken
dc.description.abstractThe economic concept of Returns-to-Scale (RTS) has been intensively studied in the context of Data Envelopment Analysis (DEA). The conventional DEA models that are used for RTS classification require well-defined and accurate data whereas in reality observations gathered from production systems may be characterized by intervals. For instance, the heat losses of the combined production of heat and power (CHP) systems may be within a certain range, hinging on a wide variety of factors such as external temperature and real-time energy demand. Enriching the current literature independently tackling the two problems; interval data and RTS estimation; we develop an overarching evaluation process for estimating RTS of Decision Making Units (DMUs) in Imprecise DEA (IDEA) where the input and output data lie within bounded intervals. In the presence of interval data, we introduce six types of RTS involving increasing, decreasing, constant, non-increasing, non-decreasing and variable RTS. The situation for non-increasing (non-decreasing) RTS is then divided into two partitions; constant or decreasing (constant or increasing) RTS using sensitivity analysis. Additionally, the situation for variable RTS is split into three partitions consisting of constant, decreasing and increasing RTS using sensitivity analysis. Besides, we present the stability region of an observation while preserving its current RTS classification using the optimal values of a set of proposed DEA-based models. The applicability and efficacy of the developed approach is finally studied through two numerical examples and a case study.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectData envelopment analysisen
dc.subjectImprecise dataen
dc.subjectReturns-to-scaleen
dc.titleMeasurement of Returns-to-Scale using Interval Data Envelopment Analysis Modelsen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.cie.2017.12.023
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2017-12-24en


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