• Home
  • Designing a Dynamic two-stage data envelopment analysis model to calculate partial and periodic efficiency

Share To

Article Url


Manuscript ID : JNRM-2005-1891 (R1) Visit : 127 Page: 141 - 156

Article Type: Original Research

Designing a Dynamic two-stage data envelopment analysis model to calculate partial and periodic efficiency

Subject Areas : Statistics

Reza Soleymani-Damaneh 1

1 - Department of Management, Faculty of Administrative Sciences and Economics, Vali Asr University (AJ) Rafsanjan

Received: 2020-05-06 Accepted : 2020-07-20 Published : 2022-01-21

Keywords: متغیرهای بین‌زمانی, کارایی, ساختار دومرحله‌ای پویا, متغیرهای میانی, تحلیل پوششی داده‌ها,

Abstract :

The measurement of organizational efficiency has always been discussed by various researchers. Data envelopment analysis, taking the inputs and outputs of the decision-making units into account, makes it possible to calculate the relative efficiency for each unit. Many organizations have a two-stage structure, and their performance in successive periods depends on each other. In evaluating such a structure, partial and periodic efficiency must be calculated. Early models and network and dynamic models are not able to calculate this performance alone. Models of existing dynamic networks are also unable to provide a projection for inefficient units or solve all challenges. In this study, by defining the PPS, an input-oriented dynamic two-stage DEA was developed. In this model, the optimal value of the intermediate and carryover variables is determined by the next stage and period, and the stages and periods become efficient backward from the last stage of the last period. In addition to the total structure, the model makes efficient all stages and periods and only becomes an efficient unit if it is efficient in all stages and periods. It was also proved that the projection of the unit to be evaluated is partial, periodic, and total efficient. How to use the model to calculate efficiency was expressed by a practical three periodical example.

References:


  1. Farrell M. J. (1957). The measurement of productive efficiency, Journal of the Royal Statistical Society, 120(3), 253-290.
    2.

 

  1. Charnes, A., Cooper, W. W., & Rhodes, E. (1978), “Measuring the efficiency of decision-making units”. European Journal of Operational Research, 2, 429-444.
    2.

 

  1. Chen, C., Zhu, J., Yu, Y., Noori, H. (2012). A new methodology for evaluating Sustainable product design performance with two-stage Network Data Envelopment Analysis, European Journal of Operational Research, 221(1).
    2.

 

  1. Wang, Q., Wu, Z., Chen, X. (2019). Decomposition weights and overall efficiency in a two-stage DEA model with shared resources, Computers & Industrial Engineering, 14, 7.
    2.

 

  1. Fare, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34, 35-49.
    2.

 

  1. Nemoto, J., & Goto, M. (1999). Dynamic data envelopment analysis: Modeling intertemporal behavior of a firm in the presence of productive inefficiencies. Economics Letters, 64, 51-56.
    2.

 

  1. Nemoto, J., & Goto, M. (2003). Measurement of dynamic efficiency in production: An application of data envelopment analysis to Japanese electric utilities. Journal of Productivity Analysis, 19, 191-210.
    2.

 

  1. Banker, R.D., Chanes, A., U cooper, W. W. (1984). Some models for estimating technical and scale efficiencies in data envelopment analysis. Management Science, 30, 1078-1092.
    2.

 

  1. Halkos. G. E., Tzeremes, N. G., Kourtzidis, S. A. (2014). A unified classification of two-stage DEA models. Surveys in Operations Research and Management Science, 19: 1-16.
    2.

 

  1. Zhang, L. (2017). Dynamic network data envelopment analysis based upon technology changes, Information Systems and Operational Research, 139. 37.
    2.

 

  1. An, Q., Meng, F., Xiong, B., Wang, Z., Chen, X. (2018). Assessing the relative efficiency of Chinese high-tech industries: a dynamic network data envelopment analysis approach, Ann. Oper. Res., 479, 18.
    2.

 

  1. Kao, C., & Hwang, S. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185, 418-429.
    2.

 

  1. Chen, Y., Cook, W. D., Li, N., & Zhu, J. (2009). Additive efficiency decomposition in two-stage DEA. European Journal of Operational Reaearch, 196, 1170-1176.
    2.

 

  1. Despotis, K., D., Koronakos, G., Sotiros, D. (2016). Composition versus decomposition in two-stage network DEA: a reverse approach. Journal of Productivity Analysis, 45, 71-87.
    2.

 

  1. Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197, 243-252.
    2.

 

  1. Cook, W. D., Zhu, J., Bi, G. B., & Yang, F. (2010). Network DEA: Additive efficiency decomposition. European Journal of Operational Research, 207, 1122-1129.
    2.

 

  1. Nikfarjam, H., Rostamy-Malkhalifeh, M. & Mamizadeh-Chatghayeh, S. (2015). Measuring supply chain efficiency based on a hybrid approach. Transportation Research Part D, 39, 141-150.
    2.

 

  1. Lee, H. S. (2020). Efficiency decomposition of the network DEA in variable returns to scale: an additive dissection in losses, Omega, doi: https://doi.org/10.1016/j.omega.2020.102212.
    2.

 

  1. Fukuyama, H. & Mirdehghan, S.M. (2012). Identifying the efficiency status in Network DEA. European Journal of Operational Research, 220, 85-92.
    2.

 

  1. Rostamy-Malkhalifeh, M. & Mollaeian, E. (2012). Evaluating performance supply chain by a new non-radial network DEA model with fuzzy data. Journal of Data Envelopment Analysis and Decision Science, 9.
    2.

 

  1. Rostamy-Malkhalifeh, M., Mollaeian, E. & Mamizadeh-Chatghayeh, S. (2013). A New Non-radial Network DEA Model for Evaluating Performance Supply Chain. Indian Journal of Science and Technology, 6, 3.
    2.

 

  1. Yang, G. L., Fukuyama, H., Song, Y. (2018). Measuring the inefficiency of Chinese research universities based on a two-stage network DEA model, Journal of Informetrics, 12, 10-30.
    2.

 

  1. Tone, k., & Tsutsui, M. (2010). Dynamic DEA: A slacks-based measure approach. Omega, 38, 3-4.
    2.

 

  1. Chen, Chien M., & van Dalen, J. (2010). Measuring dynamic efficiency: theories and an integrated methodology. European Journal of Operational Research, 203, 749-760.
    2.

 

  1. Kao, C. (2013). Dynamic data envelopment analysis: A relational analysis. European Journal of Operational Research.
    2.

 

  1. Kao, C. (2014b). Network data envelopment analysis: A review. European Journal of Operational Research, 239, 1-16.
    2.

 

  1. Fukuyama, H., & Weber, W.L. (2015). Measuring Japanese bank performance: A dynamic network DEA approach. Journal of Productivity Analysis, 44(3), 249-264.
    2.

 

  1. Liu, D. (2017). Evaluating the multi-period efficiency of East Asia airport companies. Journal of Air Transport Management, 59: 71-82.
    2.

 

  1. A. Esmaeilzadeh, R. K. Matin. (2018). Multi-period efficiency measurement of network production systems. Measurement, doi: https://doi.org/10.1016/j.measurement.2018.12.024.
    2.

 

  1. Hosseini, K., Stefaniec, A. (2019). Efficiency assessment of irans petroleum efining industry in the presence of unprofitable output: a dynamic two-stage slacks-based measure. Energy, 189.
    2.

 

  1. Tavana, M., Izadikhah, M., Di Caprio, D., Farzipoor Saen, R. (2017). A new dynamic range directional measure for two-stage data envelopment analysis models with negative data. Computers & industrial Engineering, doi: https://doi.org/10.1016/j.cie.2017.11.024.
    2.

 

  1. Ching-Cheng Lu, Wu Dan, Xiang Chen, Chih-Kuo Tseng & Kuowei Chou. (2019). Evaluation of the operating performance of Taiwanese mchine tool industry with the dynamic network DEA model, Enterprise information systems, 17, 96.
    2.

 

  1. Kao, C. (2017). Dynamic Systems. Network Data Envelopment Analysis, International Series in Operations Research & Management Science, 240, chapter 17, pp. 409-431.
    2.

 

  1. Xiong, X., Yang, G. Zhong, C. G. (2018). Assessing R&D efficiency using a two-stage dynamic DEA model: A case study of research institutes in the Chinese Academy of Sciences, Journal of Informetrics, 12, 784-805.
    2.

 

  1. Seyedboveir, S., Kordrostami, S., Daneshian, B., Amirteioori, A. (2017). Cost Efficiency Measurement in Data Envelopment Analysis with Dynamic Network Structures: A Relational Model, Asia-Pacific Journal of Operational Research, 34, 5.
    2.

 

  1. Wanke, P., Barros, C.P. (2016). Efficiency in latin American airlines: a two-stage approach combining virtual frontier dynamic DEA and Simplex Regression, Journal of Air Transport Management, 54, 93-103.
    2.

 

  1. Kalantary, M., Farzipoor Saen, R. (2018). Assessing Sustainability of supply chains: an inverse network dynamic DEA model, Computers & Industrial Engineering,doi: https://doi.org/10.1016/j.cie.2018.11.009.
    2.

 

  1. Tavana, M., Khalili-Damghani, K., Arteaga, F., J., Hosseini, A. (2019). Computers & Industrial Engineering, 135, 143-155.
    2.

 

  1. Hosseinzadeh Lotfi, F., Navabakhs, M, Tehranian, A., Rostamy-Malkhalifeh, M. & Shahverdi, R. (2007). Ranking Bank Branches with Interval Data The Application of DEA. International Mathematical Forum, 2, 9, 429-440.
    2.

Sanad

Sanad is a platform for managing Azad University publications

Links

Related Centers

Technical Support

Official pages

The rights to this website are owned by the Raimag Press Management System.
Copyright © 2021-2024

Home| Login| About Us| Contact Us|
[فارسی] [العربية] [fa] [ar]