Document Type : Research Paper


1 Department of Mathematics, Rasht Branch, Islamic Azad University.

2 Mathematical Department, Rasht Branch, Islamic Azad University, Rasht, Iran


Data envelopment analysis is auseful method to measurement unified rational and operational and environmental efficiency a supply chain. Supply chain management divisions produce two types outputs based on economic activities and inputs separate into two categories under natural and managerial disposability. The current paper propose a new Data Envelopment Analysis based model to efficiency assessment a supply chain under investment on certain types of inputs to new technologic innovation. In hence, dual-role factors controls cleanup costs of flaring gas and the amount electricity consumptions of power plants also dual-role indices improve expertise in transmission entities. A real case study on Iran power industry is presented to demonstrate the applicability of the proposed model. To demonstrate the capability of the proposed approach this framework is implemented for the performance evaluation of a supply chain identified by oil and gas companies, power plants, transmissions companies, dispatching companies and final consumers in Iran.


 [1] Cook, W D., Green, R H., & Zhu, J. (2006). Dual- role factors envelopment analysis. IIE    Transaction, 38(2), 105-115.
 [2] Cook, W D., Zhu, J. (2007).  Classifying inputs and outputs in data envelopment analysis.  European Journal of Operational Research, 180 (2), 692-699.
 [3] Kao, C.,  Hwang, S N.  (2007). Efficiency decomposition in two-stage data envelopment Analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418-429.
 [4] Fare, R., Grosskopf, S., & Lovell, K. Pasurka, C. (1989). Multilateral productivity comparison When some outputs are undesirable. A nonparametric approach: The Review of Economics and statistical, 71(1), 90- 98.
[5] Farzipoor, Saen R. (2010a). Technology selection in the presence of dual- role factors.  .International Journal of Advance Operation Management  2 (Nos, ¾), 249-269.
[6] Farzipoor,   Saen R. (2010b) .Developing a new data envelopment analysis methodology for supplier selection in the presence of both undesirable outputs and imprecise data.  International Journal of advance Manufacturing Technologhy, 51 (9), 1 243-1250.   
 [7] Farzipoor, Saen R. (2010c). Restricting weights in supplier selection decision in the presence of dual –role factors.  Applied mathematical modeling, 34 (10), 2820-2830.
[8] Farzipoor,   Saen, R.  (2011).  A decision model for selecting third – party reverse logistics provider in the presence of dual – role factors and imprecise data .Asia- pacific. Journal of Operation Research, 28 (9), 239-254.
[9] Farzipoor, Sean R., Mirhedayatian, SM., & Azadi, M. (2014). A novel network data envelopment analysis model for evaluation green supply chain Management .International Journal Production Economics, 147, 544-554.
[10] Hatefi, SM., Jolia, F. (2010). A new model for classifying inputs and outputs and evaluating the performance of DMUs based on trans log outputs distance function. Applied Mathematical Modeling, 34 (6), 1439-1449.
[11] Kao, C., Hwang, S N. (2007). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418-429.
[12] Sueyoshi, T., Goto, M. (2009). Can environment investment and expenditure enhance financial performance of US electric utility firm under the clean air act amendment of 1990?  Energy policy, 37, 4819-4826.
[13] Sueyoshi, T., Goto, M. (2010a) .Measurement of a linkage among environmental, operation al and financial performance in Japanese  manufacturing  firms : A use of data envelopment analysis with Strong complement slackness condition European Journal of Operational Research, 207, 1742-1753.
[14] Sueyoshi, T., Gotto, M. (2010b). Should the US clean air act include CO2 emission control? Examination by data envelopment analysis.  Energy policy ,38, 5902-5911.
[15] Sueyoshi, T., Gotto, M. (2011a) .DEA approach for unified efficiency measurement assessment of Japanese fossil fuel power generation .Energy Econ ,33,195 -208.
[16] Sueyoshi, T., Gotto, M. (2011b). DEA approach for unified efficiency measurement assessment of Japanese fossil fuel power generation. EnergyEcon, 33, 195-208.
[17] Sueyoshi, T., Gotto, M. (2011c). A combined use of DEA (data envelopment analysis) with strong complementary slackness condition and DEA-DA (discriminant analysis).Appl Math Lett, 24, 1051-1056.
[18] Sueyoshi, T., Gotto, M. (2012a). Data envelopment analysis of environmental assessments comparison between public and private ownership in petroleum industries.  European Journal of Operational Research, 216, 668-678.
[19] Sueyoshi, T.,  Gotto, M. (2012b). Methodological comparison between two unified (operational and environmental efficiency measurements for environmental assessment.  European Journal of Operational Research, 210, 684-693.
[20] Sueyoshi, T., Gotto, M. (2012c). Efficiency-based rank assessment for electric power industry :a combined use of data envelopment analysis(DEA)and DEA discriminant analysis( DA). Energy Econ,34,634-644.
[21] Sueyoshi, T., Gotto, M. (2012 e). DEA radial and non-radial for unified efficiency under natural and managerial disposability theoretical extension by Strong complement slackness condition. Energy Econ,34,700-713
[22] Sueyoshi, T., Gotto, M. (2012 g).  A Returns to scale  vs  , damages to scale under  Strong complement slackness condition in DEA assessment Japanese  corporate effort on environmental protection .Energy Econ ,34,1422-1434.
[23] Sueyoshi, T., Gotto, M. (2012 k). Efficiency –based rank assessment for electric power Industry. A combined use of data envelopment analysis DEA and DEA discriminant analysis DEA. Economic,  34, 634-644.
[24] Sueyoshi, T., Gotto, M.  (2013). A Returns to scale vs, damages to scale in data envelopments analysis: A impact of US clean air act on coal fired power plants. OMEGA ,41, 164-175.
 [25] Sueyoshi, T., Gotto, M (2014a). DEA radial measurement for environment assessment .A Comparative study between Japanese chemical and pharmaceutical firms, Applied Energy , 115,502- 513.
[26] Sueoshi, T. Gotto, M. (2014c). photovoltaic power station in Germany and the united states ,A comparative study by data envelopment analysis.  Energy Economic,42, 271-288.
[27] Toolo, M. (2009). On classifying inputs and outputs in DEA: a revised model. European journal of operational  research, 198 (1), 358-360.
[28] Tone, U., Tsutsui, M. (2009). Network DEA: A slack-based measure approach. European Journal of Operation research, 197(1), 243-252.
[29] Tone, K., Tsutsui, M. (2010). An epsilon-based measure of efficiency in DEA-A third pole of technical efficiency. European Journal of operational research, 207(3), 1554-156
[30] Tavana, M., Mirzagoltabar, H., Mirhedayatian, S m., & Farzipoor, Saen R. (2013). A new Network      epsilon-based DEA model for supply chain performanceevaluation. Computer an industrial Engineering 66, 501-513.
 [31] Yang, SL., Li, TF. (2002). Agility evaluation of mass customization product.Manufacturing. Journal of  materials  processing Technology, 129, 640-644.
  [32] Zhu, J.  (2003). Quantitative models for performance Evaluation and Benchmarking: Data    Envelopment analysis with spreadsheets. Boston; Xluwer academic publishers
  [33] Zhu, J., Cook, WD., & Liang,  L. (2006).  DEA models for supply chain efficiency evaluation  Data Envelopment analysis with spreadsheets. Boston: Xluwer Academic publishers,   145, 35-49.