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  • 标题:Uncertain Random Data Envelopment Analysis: Efficiency Estimation of Returns to Scale
  • 本地全文:下载
  • 作者:Bao Jiang ; Shuang Feng ; Jinwu Gao
  • 期刊名称:Advances in Mathematical Physics
  • 印刷版ISSN:1687-9120
  • 电子版ISSN:1687-9139
  • 出版年度:2021
  • 卷号:2021
  • 页码:1-8
  • DOI:10.1155/2021/6630317
  • 出版社:Hindawi Publishing Corporation
  • 摘要:Evaluating efficiency according to the different states of returns to scale (RTS) is crucial to resource allocation and scientific decision for decision-making units (DMUs), but this kind of evaluation will become very difficult when the DMUs are in an uncertain random environment. In this paper, we attempt to explore the uncertain random data envelopment analysis approach so as to solve the problem that the inputs and outputs of DMUs are uncertain random variables. Chance theory is applied to handling the uncertain random variables, and hence, two evaluating models, one for increasing returns to scale (IRS) and the other for decreasing returns to scale (DRS), are proposed, respectively. Along with converting the two uncertain random models into corresponding equivalent forms, we also provide a numerical example to illustrate the evaluation results of these models.
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