Measurement of Returns-to-Scale using Interval Data Envelopment Analysis Models

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dc.contributor.author Hatami-Marbini, A. en
dc.contributor.author Ghelej Beigi, Z. en
dc.contributor.author Hougaard, J. L. en
dc.contributor.author Gholami, K. en
dc.date.accessioned 2018-01-02T15:37:09Z
dc.date.available 2018-01-02T15:37:09Z
dc.date.issued 2017-12-27
dc.identifier.citation Hatami-Marbini, A. et al. (2017). Measurement of Returns-to-Scale using Interval Data Envelopment Analysis Models. Computers & Industrial Engineering en
dc.identifier.uri http://hdl.handle.net/2086/15044
dc.description The 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 link en
dc.description.abstract The 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.iso en en
dc.publisher Elsevier en
dc.subject Data envelopment analysis en
dc.subject Imprecise data en
dc.subject Returns-to-scale en
dc.title Measurement of Returns-to-Scale using Interval Data Envelopment Analysis Models en
dc.type Article en
dc.identifier.doi https://doi.org/10.1016/j.cie.2017.12.023
dc.peerreviewed Yes en
dc.funder N/A en
dc.projectid N/A en
dc.cclicence CC-BY-NC en
dc.date.acceptance 2017-12-24 en


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