Evaluation of decision-making units based on the weight-optimized DEA model

Jiasen Sun; Rui Yang; Xiang Ji; Jie Wu

Kybernetika (2017)

  • Volume: 53, Issue: 2, page 244-262
  • ISSN: 0023-5954

Abstract

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Data envelopment analysis (DEA) is a methodology for measuring best relative efficiencies of a group of peer decision-making units (DMUs) that take multiple inputs to produce multiple outputs. However, the traditional DEA model only aims to maximize the efficiency of the DMU under evaluation. This usually leads to very small weights (even zero weights) being assigned to some inputs or outputs. Correspondingly, these inputs or outputs have little or even no contribution to efficiency, which is unfair and irrational. The purpose of this paper is to address this problem. Two new weight-optimized models are proposed based upon the perspective of cross evaluation. Using the results of an Advanced Manufacturing Technology (AMT) example, it is found that all AMTs are fully sorted. The decision maker can easily choose the best AMT. In addition, unreasonable weights of AMTs are effectively avoided.

How to cite

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Sun, Jiasen, et al. "Evaluation of decision-making units based on the weight-optimized DEA model." Kybernetika 53.2 (2017): 244-262. <http://eudml.org/doc/288226>.

@article{Sun2017,
abstract = {Data envelopment analysis (DEA) is a methodology for measuring best relative efficiencies of a group of peer decision-making units (DMUs) that take multiple inputs to produce multiple outputs. However, the traditional DEA model only aims to maximize the efficiency of the DMU under evaluation. This usually leads to very small weights (even zero weights) being assigned to some inputs or outputs. Correspondingly, these inputs or outputs have little or even no contribution to efficiency, which is unfair and irrational. The purpose of this paper is to address this problem. Two new weight-optimized models are proposed based upon the perspective of cross evaluation. Using the results of an Advanced Manufacturing Technology (AMT) example, it is found that all AMTs are fully sorted. The decision maker can easily choose the best AMT. In addition, unreasonable weights of AMTs are effectively avoided.},
author = {Sun, Jiasen, Yang, Rui, Ji, Xiang, Wu, Jie},
journal = {Kybernetika},
keywords = {data envelopment analysis (DEA); efficiency; weight-optimized model; cross evaluation},
language = {eng},
number = {2},
pages = {244-262},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Evaluation of decision-making units based on the weight-optimized DEA model},
url = {http://eudml.org/doc/288226},
volume = {53},
year = {2017},
}

TY - JOUR
AU - Sun, Jiasen
AU - Yang, Rui
AU - Ji, Xiang
AU - Wu, Jie
TI - Evaluation of decision-making units based on the weight-optimized DEA model
JO - Kybernetika
PY - 2017
PB - Institute of Information Theory and Automation AS CR
VL - 53
IS - 2
SP - 244
EP - 262
AB - Data envelopment analysis (DEA) is a methodology for measuring best relative efficiencies of a group of peer decision-making units (DMUs) that take multiple inputs to produce multiple outputs. However, the traditional DEA model only aims to maximize the efficiency of the DMU under evaluation. This usually leads to very small weights (even zero weights) being assigned to some inputs or outputs. Correspondingly, these inputs or outputs have little or even no contribution to efficiency, which is unfair and irrational. The purpose of this paper is to address this problem. Two new weight-optimized models are proposed based upon the perspective of cross evaluation. Using the results of an Advanced Manufacturing Technology (AMT) example, it is found that all AMTs are fully sorted. The decision maker can easily choose the best AMT. In addition, unreasonable weights of AMTs are effectively avoided.
LA - eng
KW - data envelopment analysis (DEA); efficiency; weight-optimized model; cross evaluation
UR - http://eudml.org/doc/288226
ER -

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