A DEA model for two-stage parallel-series production processes

Alireza Amirteimoori; Feng Yang

RAIRO - Operations Research - Recherche Opérationnelle (2014)

  • Volume: 48, Issue: 1, page 123-134
  • ISSN: 0399-0559

Abstract

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Data envelopment analysis (DEA) has been widely used to measure the performance of the operational units that convert multiple inputs into multiple outputs. In many real world scenarios, there are systems that have a two-stage network process with shared inputs used in both stages of productions. In this paper, the problem of evaluating the efficiency of a set of specialized and interdependent components that make up a large DMU is considered. In these processes the first stage consists of two parallel components which are connected serially with the process in the second stage. The paper develops a DEA approach for measuring efficiency of decision processes which can be divided into two stages. This application of parallel-series production process involves shared resources and the paper determines an optimal split of shared resources among two components.

How to cite

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Amirteimoori, Alireza, and Yang, Feng. "A DEA model for two-stage parallel-series production processes." RAIRO - Operations Research - Recherche Opérationnelle 48.1 (2014): 123-134. <http://eudml.org/doc/275015>.

@article{Amirteimoori2014,
abstract = {Data envelopment analysis (DEA) has been widely used to measure the performance of the operational units that convert multiple inputs into multiple outputs. In many real world scenarios, there are systems that have a two-stage network process with shared inputs used in both stages of productions. In this paper, the problem of evaluating the efficiency of a set of specialized and interdependent components that make up a large DMU is considered. In these processes the first stage consists of two parallel components which are connected serially with the process in the second stage. The paper develops a DEA approach for measuring efficiency of decision processes which can be divided into two stages. This application of parallel-series production process involves shared resources and the paper determines an optimal split of shared resources among two components.},
author = {Amirteimoori, Alireza, Yang, Feng},
journal = {RAIRO - Operations Research - Recherche Opérationnelle},
keywords = {data envelopment analysis; efficiency; production; two-stage},
language = {eng},
number = {1},
pages = {123-134},
publisher = {EDP-Sciences},
title = {A DEA model for two-stage parallel-series production processes},
url = {http://eudml.org/doc/275015},
volume = {48},
year = {2014},
}

TY - JOUR
AU - Amirteimoori, Alireza
AU - Yang, Feng
TI - A DEA model for two-stage parallel-series production processes
JO - RAIRO - Operations Research - Recherche Opérationnelle
PY - 2014
PB - EDP-Sciences
VL - 48
IS - 1
SP - 123
EP - 134
AB - Data envelopment analysis (DEA) has been widely used to measure the performance of the operational units that convert multiple inputs into multiple outputs. In many real world scenarios, there are systems that have a two-stage network process with shared inputs used in both stages of productions. In this paper, the problem of evaluating the efficiency of a set of specialized and interdependent components that make up a large DMU is considered. In these processes the first stage consists of two parallel components which are connected serially with the process in the second stage. The paper develops a DEA approach for measuring efficiency of decision processes which can be divided into two stages. This application of parallel-series production process involves shared resources and the paper determines an optimal split of shared resources among two components.
LA - eng
KW - data envelopment analysis; efficiency; production; two-stage
UR - http://eudml.org/doc/275015
ER -

References

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  1. [1] A. Amirteimoori, A DEA two-stage decision process with shared resources. Central Eur. J. Oper. Res.21 (2013) 141–151. MR3016880
  2. [2] R.D. Banker, A. Charnes and W.W. Cooper, Some Methods for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science30 (1984) 1078–1092. Zbl0552.90055
  3. [3] A. Charnes, W.W. Cooper and E. Rhodes, Measuring the Efficiency of Decision Making Units. Eur. J. Oper. Res.2 (1978) 429–444. Zbl0416.90080MR525905
  4. [4] Y. Chen, W.D. Cook, N. Li and J. Zhu, Additive Efficiency Decomposition in Two-Stage DEA. Eur. J. Oper. Res. 196 (2009a) 1170–1176. Zbl1176.90393
  5. [5] Y. Chen and J. Zhu, Measuring Information Technology’s Indirect Impact on Firm Performance. Inf. Technol. Manag. J.5 (2004) 9–22. 
  6. [6] Y. Chen, L. Liang and J. Zhu, Equivalence in two-stage DEA approaches. Eur. J. Oper. Res. 193 (2009b) 600–604. Zbl1153.90502MR2463286
  7. [7] Y. Chen, J. Du, H.D. Sherman and J. Zhu, DEA model with shared resources and efficiency decomposition. Eur. J. Oper. Res.207 (2010) 339–349. Zbl1205.90146MR2659449
  8. [8] W.D. Cook, M. Hababou and H. Tuenter, Multi-component efficiency measurement and shared inputs in DEA: an application to sales and service performance in bank branches. J. Prod. Anal.14 (2000) 209–224. 
  9. [9] W.D. Cook, J. Zhu, G. Bi and F. Yang, Network DEA: Additive efficiency decomposition. Eur. J. Oper. Res. 207 (2010) 1122–1129. Zbl1206.90055
  10. [10] R. Färe and S. Grosskopf, Productivity and intermediate products: A frontier approach, Econ. Lett.50 (1996) 65–70. Zbl0900.90164
  11. [11] R. Färe and S. Grosskopf, Network DEA. Socio-Economic Planning Sciences34 (2000) 35–49. 
  12. [12] B. Golany, S.T. Hackman and U. Passy, An Efficiency measurement Framework for Multi-stage Production System. Ann. Oper. Res.145 (2006) 51–68. Zbl1106.90024MR2258638
  13. [13] B. Hoopes, K.P. Triantis and N. Partangel, The Relationship Between Process and Manufacturing Plant Performance: A Goal Programming Approach. Int. J. Oper. Quantit. Manag.6 (2000) 287–310. 
  14. [14] C. Kao, Efficiency Measurement for Parallel Production systems. Eur. J. Oper. Res.196 (2009) 1107–1112. Zbl1176.90168
  15. [15] M. Lothgren and M. Tambour, Productivity and customer satisfaction in Swedish pharmacies: A DEA network model. Eur. J. Oper. Res.115 (1999) 449–458. Zbl0947.90061
  16. [16] L.M. Seiford and J. Zhu, Profitability and Marketability of the Top 55 US Commercial Banks. Management Science45 (1999) 1270–1288. 
  17. [17] K. Tone and M. Tsutsui, Network DEA: A slack-based measure approach. Eur. J. Oper. Res.197 (2009) 243–252. Zbl1157.90465
  18. [18] J. Zhu, Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets. Kluwer Academic Publishers, Boston (2003). Zbl1140.90014

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