Flexible measures in production process: A DEA-based approach

Alireza Amirteimoori; Ali Emrouznejad

RAIRO - Operations Research (2011)

  • Volume: 45, Issue: 1, page 63-74
  • ISSN: 0399-0559

Abstract

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Data envelopment analysis (DEA) has been proven as an excellent data-oriented efficiency analysis method for comparing decision making units (DMUs) with multiple inputs and multiple outputs. In conventional DEA, it is assumed that the status of each measure is clearly known as either input or output. However, in some situations, a performance measure can play input role for some DMUs and output role for others. Cook and Zhu [Eur. J. Oper. Res.180 (2007) 692–699] referred to these variables as flexible measures. The paper proposes an alternative model in which each flexible measure is treated as either input or output variable to maximize the technical efficiency of the DMU under evaluation. The main focus of this paper is on the impact that the flexible measures has on the definition of the PPS and the assessment of technical efficiency. An example in UK higher education intuitions shows applicability of the proposed approach.

How to cite

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Amirteimoori, Alireza, and Emrouznejad, Ali. "Flexible measures in production process: A DEA-based approach." RAIRO - Operations Research 45.1 (2011): 63-74. <http://eudml.org/doc/276364>.

@article{Amirteimoori2011,
abstract = { Data envelopment analysis (DEA) has been proven as an excellent data-oriented efficiency analysis method for comparing decision making units (DMUs) with multiple inputs and multiple outputs. In conventional DEA, it is assumed that the status of each measure is clearly known as either input or output. However, in some situations, a performance measure can play input role for some DMUs and output role for others. Cook and Zhu [Eur. J. Oper. Res.180 (2007) 692–699] referred to these variables as flexible measures. The paper proposes an alternative model in which each flexible measure is treated as either input or output variable to maximize the technical efficiency of the DMU under evaluation. The main focus of this paper is on the impact that the flexible measures has on the definition of the PPS and the assessment of technical efficiency. An example in UK higher education intuitions shows applicability of the proposed approach. },
author = {Amirteimoori, Alireza, Emrouznejad, Ali},
journal = {RAIRO - Operations Research},
keywords = {Data envelopment analysis; efficiency; flexible measure; data envelopment analysis},
language = {eng},
month = {6},
number = {1},
pages = {63-74},
publisher = {EDP Sciences},
title = {Flexible measures in production process: A DEA-based approach},
url = {http://eudml.org/doc/276364},
volume = {45},
year = {2011},
}

TY - JOUR
AU - Amirteimoori, Alireza
AU - Emrouznejad, Ali
TI - Flexible measures in production process: A DEA-based approach
JO - RAIRO - Operations Research
DA - 2011/6//
PB - EDP Sciences
VL - 45
IS - 1
SP - 63
EP - 74
AB - Data envelopment analysis (DEA) has been proven as an excellent data-oriented efficiency analysis method for comparing decision making units (DMUs) with multiple inputs and multiple outputs. In conventional DEA, it is assumed that the status of each measure is clearly known as either input or output. However, in some situations, a performance measure can play input role for some DMUs and output role for others. Cook and Zhu [Eur. J. Oper. Res.180 (2007) 692–699] referred to these variables as flexible measures. The paper proposes an alternative model in which each flexible measure is treated as either input or output variable to maximize the technical efficiency of the DMU under evaluation. The main focus of this paper is on the impact that the flexible measures has on the definition of the PPS and the assessment of technical efficiency. An example in UK higher education intuitions shows applicability of the proposed approach.
LA - eng
KW - Data envelopment analysis; efficiency; flexible measure; data envelopment analysis
UR - http://eudml.org/doc/276364
ER -

References

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  2. J. Beasley, Comparing university departments. Omega8 (1990) 171–183.  
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  6. W.D. Cook and K. Bala, Performance measurement with classification information: An enhanced additive DEA model. Omega31 (2003) 439–450.  
  7. W.D. Cook and J. Zhu, Building performance standards into DEA structures. IIE Transactions37 (2005) 267–275.  
  8. W.D. Cook and J. Zhu, Classifying inputs and outputs in DEA. Eur. J. Oper. Res.180 (2007) 692–699.  Zbl1123.90044
  9. W. D. Cook, M. Hababou and H. Tuenter, Multi-component efficiency measurement and shared inputs in data envelopment analysis: An application to sales and service performance in bank branches. J. Prod. Anal.14 (2000) 209–224.  
  10. W.W. Cooper, L.M. Seiford and K. Tone, Introduction to data envelopment analysis and its uses. Springer Publisher (2006).  
  11. A. Emrouznejad, G. Tavares and B. Parker, Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA. Socio- Econ. Plan. Sci.42 (2008) 151–157.  
  12. M. Toloo, On classifying inputs and outputs in DEA: A revised model. Eur. J. Oper. Res.198 (2009) 358–360.  Zbl1163.90674

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