TY - JOUR
T1 - Generalized robust window data envelopment analysis approach for dynamic performance measurement under uncertain panel data
AU - Peykani, Pejman
AU - Gheidar-Kheljani, Jafar
AU - Farzipoor Saen, Reza
AU - Mohammadi, Emran
N1 - Funding Information:
The authors would like to thank the anonymous reviewers and the editor-in-chief for their constructive comments and suggestions.
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2022/11
Y1 - 2022/11
N2 - This paper proposes a robust window data envelopment analysis (RWDEA) approach for assessing the dynamic performance of decision making units (DMU) in the presence of panel and uncertain data. To present the RWDEA method, generalized data envelopment analysis (GDEA) model, window analysis (WA) method, and robust optimization (RO) approach are taken into account. The proposed RWDEA approach can be used under different returns to scale (RTS) assumptions, including constant returns to scale (CRS), non-increasing returns to scale (NIRS), non-decreasing returns to scale (NDRS), and variable returns to scale (VRS). Notably, the RWDEA model is linear and can fully rank DMUs under deep uncertainty. To solve and show the validity of the proposed approach, the RWDEA model is implemented for evaluating the efficiency of the intellectual capital of 10 automotive and parts manufacturing companies. The results indicate that the RWDEA approach is applicable and useful for the dynamic efficiency assessment of DMUs in the presence of uncertain panel data. The RWDEA approach, by considering the uncertainties in the data and using panel data, provides more reliable results in comparison with the classical DEA models.
AB - This paper proposes a robust window data envelopment analysis (RWDEA) approach for assessing the dynamic performance of decision making units (DMU) in the presence of panel and uncertain data. To present the RWDEA method, generalized data envelopment analysis (GDEA) model, window analysis (WA) method, and robust optimization (RO) approach are taken into account. The proposed RWDEA approach can be used under different returns to scale (RTS) assumptions, including constant returns to scale (CRS), non-increasing returns to scale (NIRS), non-decreasing returns to scale (NDRS), and variable returns to scale (VRS). Notably, the RWDEA model is linear and can fully rank DMUs under deep uncertainty. To solve and show the validity of the proposed approach, the RWDEA model is implemented for evaluating the efficiency of the intellectual capital of 10 automotive and parts manufacturing companies. The results indicate that the RWDEA approach is applicable and useful for the dynamic efficiency assessment of DMUs in the presence of uncertain panel data. The RWDEA approach, by considering the uncertainties in the data and using panel data, provides more reliable results in comparison with the classical DEA models.
KW - Data envelopment analysis
KW - Dynamic efficiency
KW - Panel data
KW - Robust optimization
KW - Stock exchange
KW - Uncertainty
KW - Window analysis
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U2 - 10.1007/s12351-022-00729-7
DO - 10.1007/s12351-022-00729-7
M3 - Article
AN - SCOPUS:85134512141
SN - 1109-2858
VL - 22
SP - 5529
EP - 5567
JO - Operational Research
JF - Operational Research
IS - 5
M1 - 5
ER -