Zobrazit minimální záznam

dc.contributor.authorHatami-Marbini, Adel
dc.contributor.authorToloo, Mehdi
dc.date.accessioned2017-04-24T08:34:21Z
dc.date.available2017-04-24T08:34:21Z
dc.date.issued2017
dc.identifier.citationExpert Systems with Applications. 2017, vol. 73, p. 201-219.cs
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.urihttp://hdl.handle.net/10084/117022
dc.description.abstractSeveral researchers have adapted the data envelopment analysis (DEA) models to deal with two interrelated problems: weak discriminating power and unrealistic weight distribution. The former problem arises as an application of DEA in the situations where decision-makers seek to reach a complete ranking of units, and the latter problem refers to the situations in which basic DEA model simply rates units 100% efficient on account of irrational input and/or output weights and insufficient number of degrees of freedom. Improving discrimination power and yielding more reasonable dispersion of input and output weights simultaneously remain a challenge for DEA and multiple criteria DEA (MCDEA) models. This paper puts emphasis on weight restrictions to boost discriminating power as well as to generate true weight dispersion of MCDEA when a priori information about the weights is not available. To this end, we modify a very recent MCDEA models in the literature by determining an optimum lower bound for input and output weights. The contribution of this paper is sevenfold: first, we show that a larger amount for the lower bound on weights often leads to improving discriminating power and reaching realistic weights in MCDEA models due to imposing more weight restrictions; second, the procedure for sensitivity analysis is designed to define stability for the weights of each evaluation criterion; third, we extend a weighted MCDEA model to three evaluation criteria based on the maximum lower bound for input and output weights; fourth, we develop a super-efficiency model for efficient units under the proposed MCDEA model in this paper; fifth, we extend an epsilon-based minsum BCC-DEA model to proceed our research objectives under variable returns to scale (VRS); sixth, we present a simulation study to statistically analyze weight dispersion and rankings between five different methods in terms of non-parametric tests; and seventh, we demonstrate the applicability of the proposed models with an application to European Union member countries.cs
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofseriesExpert Systems with Applicationscs
dc.relation.urihttp://doi.org/10.1016/j.eswa.2016.12.030cs
dc.rights© 2016 Elsevier Ltd. All rights reserved.cs
dc.subjectdata envelopment analysis (DEA)cs
dc.subjectmultiple criteria DEA (MCDEA)cs
dc.subjectdiscriminating powercs
dc.subjectsimulationcs
dc.titleAn extended multiple criteria data envelopment analysis modelcs
dc.typearticlecs
dc.identifier.doi10.1016/j.eswa.2016.12.030
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume73cs
dc.description.lastpage219cs
dc.description.firstpage201cs
dc.identifier.wos000394632200015


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