An inverse model for evaluating the power industry’s supply chain
*Mojgan PouralizadehCorresponding authorpouralizadehmojgan@yahoo.comDepartment of Applied MathematicsLahijan BranchIslamic Azad UniversityLahijan, IranView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 06 Sep 2022
- Published Online:
- 27 Feb 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1419
- Pages:
- 1313–1334
Abstract
Keywords
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References
[1] R. D. Banker, A. Charnes, and W. W. Cooper, “Some models for estimating technical and scale inefficiencies in data envelopment analysis,” Management Science, vol. 30, pp. 1078–1092 (1984).
[2] A. Charnes, W. W. Cooper, and E. Rhodes, “Measuring the efficiency of decision-making units,” European Journal of Operational Research, vol. 2, pp. 429–444 (1978).
[3] F. Colver and T. Sueyoshi, “Contribution of Professor William W. Cooper in operation research and management science,” European Journal of Operational Research, vol. 196, pp. 1–16 (2009).
[4] R. Farzipoor Saen, “Developing a new data envelopment analysis methodology for supplier selection in the presence of both undesirable outputs and imprecise data,” Journal of Intelligent Manufacturing, vol. 51, pp. 1243–1250 (2010).
[5] S. R. Ghyeghrn, A. Amirteimoori, A. Malek, and S. Kordrostami, “Cost efficiency analysis in data envelopment analysis framework: An application in sugar industries,” Journal of Information and Optimization Science, vol. 42, no. 5, pp. 1137–1161 (2021), doi: 10.1080/02522667.2021.1885749.
[6] A. Hadi-Vancheh and A. A. Foroughi, “A generalized model for inputs/outputs estimation,” Mathematics and Computer Modelling, vol. 43, no. 5, pp. 447–457 (2006).
[7] S. Han, “An inverse DEA model with stochastic factors,” Mathematics Practice and Theory, vol. 33, no. 3, pp. 23–29 (2013).
[8] A. Hassenzadeh, S. Yousefi, R. Farzipoor Saen, and S. S. Seyede Hosseininia, “How to assess sustainability of countries via inverse data envelopment analysis?” Clean Technologies and Environmental Policy (2017), doi: 10.1007/s10098-017-1450-x.
[9] Iran Power Generation and Transmission Company, “Detailed statistics of Iran’s power industry electricity transmission” (in Persian), Iran Power Generation and Transmission Company (2015). [Online]. Available: https://web.archive.org/web/20210514014836/http://amar.tavanir.org.ir/
[10] G. R. Jahanshahloo, F. H. Lotfi, N. Shoja, J. Tohid, and S. Razavyn, “Input estimation and identification of extra inputs in inverse DEA models,” Applied Mathematics and Computation, vol. 156, no. 2, pp. 427–437 (2014).
[11] M. Khodakaremi, A. Shabani, R. Farzipoor Saen, M. Zolfaghari, and M. Tavana, “Developing distinctive two-stage data envelopment analysis models: An application evaluating the sustainability of supply chain management,” Measurement vol. 70, pp. 62–74 (2015).
[12] S. Lertworasirikul, P. Chamsthikul, and S. C. Fang, “Inverse data envelopment analysis model to preserve relative efficiency values: The case of variable returns to scale,” Computers & Industrial Engineering, vol. 61, no. 4, pp. 1017–1021 (2011).
[13] M. Pouralizadeh, “A DEA model to sustainability improvement of the electricity supply chain in presence dual-role factors and undesirable outputs: A case on power industry,” AIMS Energy, vol. 8, no. 4, pp. 580–614 (2020).
[14] M. Pouralizadeh, A. Amirteimoori, R. Riccardi, F. G. Z. de Moura, M. G. M. Udupa, and S. P. S. P. G. S. Swamy, “Supply chain performance evaluation in the presence of undesirable outputs: A case on power industry,” AIMS Energy, vol. 8, pp. 48–80 (2020).
[15] M. Pouralizadeh, “Sustainability assessment of electricity supply chain via resource waste reduction and pollution emissions management: A case study of the power industry,” Smart and Sustainable Manufacturing Systems, vol. 5, no. 1, pp. 290–309 (2021).
[16] M. Pouralizadeh, “Marginal profit maximization estimation of supply chains by waste energy: A case study of the power industry,” RIARO Operation Research, vol. 58, pp. 3143-1369 (2024). doi: 10.1051/ro/2024103.
[17] A. M. Rad, R. Dehghan, and F. H. Lotfi, “Inverse DEA with fuzzy data for output estimation,” Iranian Journal of Optimization, vol. 2, no. 2, pp. 388–411 (2011).
[18] T. Sueyoshi and M. Goto, “Measurement of a linkage among environmental, operational and financial performance in Japanese manufacturing firms: A use of data envelopment analysis with strong complements slackness condition,” European Journal of Operational Research, vol. 207, pp. 1742–1753 (2010).
[19] T. Sueyoshi and M. Goto, “Should the US clean air act include CO2 emission control? Examination by data envelopment analysis,” Journal of Energy Policy, vol. 38, pp. 5902–5911 (2010).
[20] T. Sueyoshi and M. Goto, “Environmental assessment for corporate sustainability by resource utilization and technology innovation: DEA radial measurement on Japanese industrial sectors,” Energy Economics, vol. 46, pp. 295–307 (2014).
[21] M. Tavana, H. Mirzagoltabar, S. M. Mirhedayatian, M. Zolfaghari, and R. Saen, “A new network Epsilon-based DEA model for supply chain performance evaluation,” Journal of Computational and Industrial Engineering, vol. 66, pp. 501–513 (2013).
[22] K. Tone and M. Tsutsui, “An epsilon-based measure of efficiency in DEA-A third pole of technical efficiency,” European Journal of Operational Research, vol. 207, pp. 1554–1566 (2010).
[23] Q. Wei, J. Zhang, and X. Zhang, “An inverse DEA for inputs/outputs estimate,” European Journal of Operational Research, vol. 121, no. 1, pp. 151–163 (2000).
[24] G. Yu, P. Wei, and A. Brocket, “A generalized data envelopment analysis model: A unification and extension of existing methods units,” Annals of Operations Research, vol. 66, pp. 47–89 (1996).
[25] M. Zhang and J. S. Cui, “The extension and integration of the inverse DEA method,” Journal of Operational Research Society, pp. 1–9 (2016).
[26] X. S. Zhang and J. C. Cui, “A project evaluation system in the state economic information system of China: An operation research practice in public sectors,” International Transactions in Operational Research, vol. 6, no. 5, pp. 441–452 (1999).
