Dynamic network data envelopment analysis model grounded on game theory

Document Type : Research Paper


1 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

3 Department of Industrial Engineering, KHATAM University, Tehran, Iran


Among the most significant management and policy-making elements is improving the efficiency of businesses and companies active in various parts of the industry, as well as management processes and systems. Various methods and models have been proposed to measure the efficiency of firms, processes and systems in a sector. One of the most important models used is network data envelopment analysis, the most widely utilized in measuring the efficiency and productivity of businesses, processes and systems. This is founded on mathematical programming and moreover is among the most powerful techniques for performance evaluation and optimization. In this study, while mathematically modeling and measuring the performance of the study sample, utilizing dynamic network data envelopment analysis, a suitable model for measuring performance, taking into account the stability of the conditions, was designed to be a framework for leading the system to higher objectives. Via utilization, it provides the basis for improving and reducing the adverse effects of the system. Therefore, the data envelopment analysis model in this research is designed as a framework for measuring, analyzing and promoting activities at the network level with the game theory approach as well as in uncertain conditions. Executives/managers will be able to understand the strengths and weaknesses of the system to strengthen and eliminate existing weaknesses plus undertake requisite planning and actions.


[1] W. Alfonso Pi˜na and C. Pardo Martnez, Development and urban sustainability: An analysis of efficiency using
data envelopment analysis, Sustain. 8 (2016), no. 2, 148.
[2] T. Badiezadeh, R.F. Saen and T. Samavati, Assessing sustainability of supply chains by double frontier network
DEA: A big data approach, Comput. Oper. Res. 98 (2018), 284–290.
[3] V. Bosetti, M. Cassinelli and A. Lanza, Benchmarking in tourism destinations; keeping in mind the sustainable
paradigm, Adv. Modern Tourism Res. Physica-Verlag HD, 165–180, 2007.
[4] M.E. Bruni, F. Guerriero and V. Patitucci, Benchmarking sustainable development via data envelopment analysis:
an Italian case study, Int. J. Envir. Res. 5 (2011), no. 1, 47–56.
[5] A. Charnes, W.W. Cooper, B. Golany, R. Halek, G. Klopp, E. Schmitz and D. Thomas, Two phase data envelopment analysis approaches to policy evaluation and management of army recruiting activities: Tradeoffs between
joint services and army advertising, Center for Cybernetic Studies, University of Texas-Austin Austin, Texas,
USA , 1986.
[6] Y. Choi, Y. Yu and H. Lee, A study on the sustainable performance of the steel industry in Korea based on
SBM-DEA, Sustain. 10 (2018), no. 1, 173.
[7] N. Dash and P. Balachandra, Benchmarking urban sustainable efficiency: A case of Indian cities, Transport. Res.
Proced. 14 (2016), 1809–1818.
[8] T.J. De Koeijer, G.A.A. Wossink, P.C. Struik and J.A. Renkema, Measuring agricultural sustainability in terms
of efficiency: the case of Dutch sugar beet growers, J. Envir. Manag.66 (2002), no. 1, 9–17.
[9] I. Dobos and G. Vrsmarty, Green supplier selection and evaluation using DEA-type composite indicators, Int. J.
Prod. Econ. 157 (2014), 273–278.
[10] R. Fare and S. Grosskopf, Network DEA, Socio-Econ. Plan. Sci. 34 (2000), no. 1, 35–49..
[11] Y. Gadanakis, R. Bennett, J. Park and F.J. Areal, Evaluating the sustainable intensification of arable farms, J.
Envir. Manag. 150 (2015), 288–298.
[12] S. Gattoufi, M. Oral and A. Reisman, A taxonomy for data envelopment analysis, Soc.-Econ. Plann. Sci. 38
(2004), no. 2-3, 141–158.
[13] J.C. Gerdessen and S. Pascucci, Data Envelopment Analysis of sustainability indicators of European agricultural
systems at regional level, Agricul. Syst. 118 (2013), 78–90.
[14] M. Haron and J.A. Arul Chellakumar, Efficiency performance of manufacturing companies in Kenya: Evaluation
and policies, Int. J. Manag. Bus. Res. 2 (2012), no. 3, 233–242.
[15] M.A. Hinojosa, S. Lozano and A.M. M´armol, Nash decomposition for process efficiency in multistage production
systems, Expert Syst. Appl. 55 (2016), 480–492.
[16] V.N. Hoang and M. Alauddin, Input-orientated data envelopment analysis framework for measuring and decomposing economic, environmental and ecological efficiency: an application to OECD agriculture, Envir. Resource
Econ. 51 (2012), no. 3, 431–452.
[17] L. Hou, D. Hoag, C.M. Keske and C. Lu, Sustainable value of degraded soils in China’s Loess Plateau: An updated
approach, Ecologic. Econ. 97 (2014), 20-27.
[18] M. Izadikhah and R.F. Saen, Assessing sustainability of supply chains by chance-constrained two-stage DEA model
in the presence of undesirable factors, Comput. Oper. Res. 100 (2018), 343–367.
Volume 13, Issue 2
July 2022
Pages 3271-3280
  • Receive Date: 13 November 2021
  • Revise Date: 29 January 2022
  • Accept Date: 23 February 2022