Hostname: page-component-7479d7b7d-q6k6v Total loading time: 0 Render date: 2024-07-12T08:08:31.913Z Has data issue: false hasContentIssue false

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

Published online by Cambridge University Press:  10 January 2014

Alireza Amirteimoori
Affiliation:
Department of Applied Mathematics, Islamic Azad University, Rasht-Iran.. aamirtemoori@gmail.com
Feng Yang
Affiliation:
School of Management, University of Science & Technology of China, He Fei, An Hui Province, 230026, P.R. China.; teimoori@guilan.ac.ir
Get access

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.

Type
Research Article
Copyright
© EDP Sciences, ROADEF, SMAI, 2014

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Amirteimoori, A., A DEA two-stage decision process with shared resources. Central Eur. J. Oper. Res. 21 (2013) 141151. Google Scholar
Banker, R.D., Charnes, A. and Cooper, W.W., Some Methods for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science 30 (1984) 10781092. Google Scholar
Charnes, A., Cooper, W.W. and Rhodes, E., Measuring the Efficiency of Decision Making Units. Eur. J. Oper. Res. 2 (1978) 429444. Google Scholar
Chen, Y., Cook, W.D., Li, N. and Zhu, J., Additive Efficiency Decomposition in Two-Stage DEA. Eur. J. Oper. Res. 196 (2009a) 11701176. Google Scholar
Chen, Y. and Zhu, J., Measuring Information Technology’s Indirect Impact on Firm Performance. Inf. Technol. Manag. J. 5 (2004) 922. Google Scholar
Chen, Y., Liang, L. and Zhu, J., Equivalence in two-stage DEA approaches. Eur. J. Oper. Res. 193 (2009b) 600604. Google Scholar
Chen, Y., Du, J., Sherman, H.D. and Zhu, J., DEA model with shared resources and efficiency decomposition. Eur. J. Oper. Res. 207 (2010) 339349. Google Scholar
Cook, W.D., Hababou, M. and Tuenter, H., Multi-component efficiency measurement and shared inputs in DEA: an application to sales and service performance in bank branches. J. Prod. Anal. 14 (2000) 209224. Google Scholar
Cook, W.D., Zhu, J., Bi, G. and Yang, F., Network DEA: Additive efficiency decomposition. Eur. J. Oper. Res. 207 (2010) 11221129. Google Scholar
Färe, R. and Grosskopf, S., Productivity and intermediate products: A frontier approach, Econ. Lett. 50 (1996) 6570. Google Scholar
Färe, R. and Grosskopf, S., Network DEA. Socio-Economic Planning Sciences 34 (2000) 3549. Google Scholar
Golany, B., Hackman, S.T. and Passy, U., An Efficiency measurement Framework for Multi-stage Production System. Ann. Oper. Res. 145 (2006) 5168. Google Scholar
Hoopes, B., Triantis, K.P. and Partangel, N., The Relationship Between Process and Manufacturing Plant Performance: A Goal Programming Approach. Int. J. Oper. Quantit. Manag. 6 (2000) 287310. Google Scholar
Kao, C., Efficiency Measurement for Parallel Production systems. Eur. J. Oper. Res. 196 (2009) 11071112. Google Scholar
Lothgren, M. and Tambour, M., Productivity and customer satisfaction in Swedish pharmacies: A DEA network model. Eur. J. Oper. Res. 115 (1999) 449458. Google Scholar
Seiford, L.M. and Zhu, J., Profitability and Marketability of the Top 55 US Commercial Banks. Management Science 45 (1999) 12701288. Google Scholar
Tone, K. and Tsutsui, M., Network DEA: A slack-based measure approach. Eur. J. Oper. Res. 197 (2009) 243252. Google Scholar
J. Zhu, Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets. Kluwer Academic Publishers, Boston (2003).