Stochastic performance measurement in two-stage network processes: A data envelopment analysis approach

Alireza Amirteimoori; Saber Mehdizadeh; Sohrab Kordrostami

Kybernetika (2022)

  • Volume: 58, Issue: 2, page 200-217
  • ISSN: 0023-5954

Abstract

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In classic data envelopment analysis models, two-stage network structures are studied in cases in which the input/output data set are deterministic. In many real applications, however, we face uncertainty. This paper proposes a two-stage network DEA model when the input/output data are stochastic. A stochastic two-stage network DEA model is formulated based on the chance-constrained programming. Linearization techniques and the assumption of single underlying factor of the data are used to construct the equivalent deterministic linear programming model. The relationship between the stochastic efficiency of each stage and stochastic centralized efficiency of the whole process, at different confidence levels, is discussed. To illustrate the real applicability of the proposed approach, a real case on 16 commercial banks in China is given.

How to cite

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Amirteimoori, Alireza, Mehdizadeh, Saber, and Kordrostami, Sohrab. "Stochastic performance measurement in two-stage network processes: A data envelopment analysis approach." Kybernetika 58.2 (2022): 200-217. <http://eudml.org/doc/298901>.

@article{Amirteimoori2022,
abstract = {In classic data envelopment analysis models, two-stage network structures are studied in cases in which the input/output data set are deterministic. In many real applications, however, we face uncertainty. This paper proposes a two-stage network DEA model when the input/output data are stochastic. A stochastic two-stage network DEA model is formulated based on the chance-constrained programming. Linearization techniques and the assumption of single underlying factor of the data are used to construct the equivalent deterministic linear programming model. The relationship between the stochastic efficiency of each stage and stochastic centralized efficiency of the whole process, at different confidence levels, is discussed. To illustrate the real applicability of the proposed approach, a real case on 16 commercial banks in China is given.},
author = {Amirteimoori, Alireza, Mehdizadeh, Saber, Kordrostami, Sohrab},
journal = {Kybernetika},
keywords = {stochastic DEA; chance-constrained models; two-stage network systems; efficiency},
language = {eng},
number = {2},
pages = {200-217},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Stochastic performance measurement in two-stage network processes: A data envelopment analysis approach},
url = {http://eudml.org/doc/298901},
volume = {58},
year = {2022},
}

TY - JOUR
AU - Amirteimoori, Alireza
AU - Mehdizadeh, Saber
AU - Kordrostami, Sohrab
TI - Stochastic performance measurement in two-stage network processes: A data envelopment analysis approach
JO - Kybernetika
PY - 2022
PB - Institute of Information Theory and Automation AS CR
VL - 58
IS - 2
SP - 200
EP - 217
AB - In classic data envelopment analysis models, two-stage network structures are studied in cases in which the input/output data set are deterministic. In many real applications, however, we face uncertainty. This paper proposes a two-stage network DEA model when the input/output data are stochastic. A stochastic two-stage network DEA model is formulated based on the chance-constrained programming. Linearization techniques and the assumption of single underlying factor of the data are used to construct the equivalent deterministic linear programming model. The relationship between the stochastic efficiency of each stage and stochastic centralized efficiency of the whole process, at different confidence levels, is discussed. To illustrate the real applicability of the proposed approach, a real case on 16 commercial banks in China is given.
LA - eng
KW - stochastic DEA; chance-constrained models; two-stage network systems; efficiency
UR - http://eudml.org/doc/298901
ER -

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