| تعداد نشریات | 6 |
| تعداد شمارهها | 121 |
| تعداد مقالات | 1,448 |
| تعداد مشاهده مقاله | 1,555,685 |
| تعداد دریافت فایل اصل مقاله | 1,459,768 |
Uncertainty in inverse data envelopment analysis: A novel approach for $CO_2$ emission efficiency | ||
| Communications in Combinatorics and Optimization | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 10 مهر 1404 اصل مقاله (465.82 K) | ||
| نوع مقاله: Original paper | ||
| شناسه دیجیتال (DOI): 10.22049/cco.2025.30515.2510 | ||
| نویسندگان | ||
| Jafar Pourmahmoud* ؛ Sima Aliabadi؛ Reza farzipour Saen؛ Alireza Ghaffari-Hadigheh | ||
| Department of Applied Mathematics, Azarbaijan Shahid Madani University, Tabriz, Iran | ||
| چکیده | ||
| Industries are increasingly relying on analytical approaches for performance evaluation and decision-making. Consequently, they must invest suitable resources at the right time for the appropriate engagements. Inverse Data Envelopment Analysis is a post-DEA sensitivity analysis method designed to tackle resource allocation. The primary objective of Inverse DEA is to determine the optimal input and/or output levels for each decision-making unit under varying conditions to achieve a specified efficiency target. Traditional inverse DEA models require precise data on the inputs and outputs of Decision-Making Units. However, in many scenarios, such as system flexibility, social and cultural contexts information may be indeterminate. In these cases, experts' opinions are used to model uncertainty. Uncertainty theory, a branch of mathematics, logically deals with degrees of belief. This paper aims to develop an InvDEA model incorporating uncertainty theory. We assume that inputs and outputs of decision-making units are based on experts' belief degrees. An input-oriented model is developed, and several properties are proven. To demonstrate the model is performance, we employ a case study involving CO$_2$ emission data from OPEC countries. | ||
| کلیدواژهها | ||
| Inverse Data Envelopment Analysis؛ Uncertainty؛ Multi Objective Programming؛ Efficiency | ||
| مراجع | ||
|
[1] Our world in data, https://ourworldindata.org/.
[2] M. Ahmadkhanlou Gharakhanlo, G. Tohidi, N. Azarmir Shotorbani, S. Razavyan, and R. Abbasi, Cost, Revenue and profit efficiency in multi-period network system: A DEA-R based approach, Commun. Comb. Optim. 9 (2024), no. 4, 725–746. https://doi.org/10.22049/cco.2023.28179.1474 [3] G.R. Amin and M.I. Boamah, Modeling business partnerships: A data envelopment analysis approach, Eur. J. Oper. Res. 305 (2023), no. 1, 329–337. https://doi.org/10.1016/j.ejor.2022.05.036
[4] R.D. Banker, A. Charnes, and W.W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis, Manag. Sci. 30 (1984), no. 9, 1078–1092.
[5] A. Charnes, W.W. Cooper, and E. Rhodes, Measuring the efficiency of decision making units, European journal of operational research 2 (1978), no. 6, 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
[6] L. Chen, Y. Gao, M.J. Li, Y.M. Wang, and L.H. Liao, A new inverse data envelopment analysis approach to achieve China’s road transportation safety objectives, Saf. Sci. 142 (2021), 105362. https://doi.org/10.1016/j.ssci.2021.105362
[7] Q. Cui and Y. Li, An empirical study on the influencing factors of transportation carbon efficiency: Evidences from fifteen countries, Appl. Energy 141 (2015), 209–217. https://doi.org/10.1016/j.apenergy.2014.12.040
[8] A. Emrouznejad, G.R. Amin, M. Ghiyasi, and M. Michali, A review of inverse data envelopment analysis: origins, development and future directions, IMA J. Manag. Math. 34 (2023), no. 3, 421–440. https://doi.org/10.1093/imaman/dpad006
[9] A. Emrouznejad, M. Tavana, and A. Hatami-Marbini, The state of the art in fuzzy data envelopment analysis, Performance Measurement with Fuzzy Data Envelopment Analysis (A. Emrouznejad and M. Tavana, eds.), Springer Berlin Heidelberg, Berlin, Heidelberg, 2014, pp. 1–45. [10] A. Emrouznejad and G.l. Yang, A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016, Socio-Econ. Plan. Sci. 61 (2018), 4–8. https://doi.org/10.1016/j.seps.2017.01.008
[11] A. Emrouznejad, G.l. Yang, and G.R. Amin, A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in chinese manufacturing industries, J. Oper. Res. Soc. 70 (2019), no. 7, 1079–1090. https://doi.org/10.1080/01605682.2018.1489344 [12] M. Ghiyasi, On inverse DEA model: The case of variable returns to scale, Comput. Ind. Eng. 87 (2015), 407–409. https://doi.org/10.1016/j.cie.2015.05.018 [13] A. Ghomi, S. Ghobadi, M.H. Behzadi, and M. Rostamy-Malkhalifeh, Inverse data envelopment analysis with stochastic data, RAIRO Oper. Res. 55 (2021), no. 5, 2739–2762. https://doi.org/10.1051/ro/2021135
[14] P. Guo and H. Tanaka, Decision making based on fuzzy data envelopment analysis, Intelligent Decision and Policy Making Support Systems (Da Ruan, F. Hardeman, and K. van der Meer, eds.), Springer Berlin Heidelberg, Berlin, Heidelberg, 2008, pp. 39–54.
[15] A. Hadi-Vencheh, A.A. Foroughi, and M. Soleimani-damaneh, A DEA model for resource allocation, Econ. Model. 25 (2008), no. 5, 983–993. https://doi.org/10.1016/j.econmod.2008.01.003
[16] A. Hatami-Marbini, A. Emrouznejad, and M. Tavana, A taxonomy and review of the fuzzy data envelopment analysis literature: two decades in the making, Eur. J. Oper. Res. 214 (2011), no. 3, 457–472. https://doi.org/10.1016/j.ejor.2011.02.001
[17] D. Hou, Y. Song, J. Zhang, M. Hou, D. O’Connor, and M. Harclerode, Climate change mitigation potential of contaminated land redevelopment: A city-level assessment method, J. Clean. Prod. 171 (2018), 1396–1406. https://doi.org/10.1016/j.jclepro.2017.10.071 [18] Y. Iftikhar, Z. Wang, B. Zhang, and B. Wang, Energy and CO2 emissions efficiency of major economies: A network DEA approach, Energy 147 (2018), 197–207. https://doi.org/10.1016/j.energy.2018.01.012
[19] D.S. Kwon, J.H. Cho, and S.Y. Sohn, Comparison of technology efficiency for CO2 emissions reduction among European countries based on DEA with decomposed factors, J. Clean. Prod. 151 (2017), 109–120. https://doi.org/10.1016/j.jclepro.2017.03.065
[20] S. Lertworasirikul, P. Charnsethikul, and S.C. Fang, Inverse data envelopment analysis model to preserve relative efficiency values: The case of variable returns to scale, Comput. Ind. Eng. 61 (2011), no. 4, 1017–1023. https://doi.org/10.1016/j.cie.2011.06.014
[21] S. Li, H. Diao, L. Wang, and L. Li, A complete total-factor CO2 emissions efficiency measure and “2030• 60 CO2 emissions targets” for shandong province, china, J. Clean. Prod. 360 (2022), 132230. https://doi.org/10.1016/j.jclepro.2022.132230
[22] Y. Li, Y. Wei, X. Zhang, and Y. Tao, Regional and provincial CO2 emission reduction task decomposition of China’s 2030 carbon emission peak based on the efficiency, equity and synthesizing principles, Struct. Change Econ. Dyn. 53 (2020), 237–256. https://doi.org/10.1016/j.strueco.2020.02.007
[23] W. Lio and B. Liu, Uncertain data envelopment analysis with imprecisely observed inputs and outputs, Fuzzy Optim. Decis. Mak. 17 (2018), no. 3, 357–373. https://doi.org/10.1007/s10700-017-9276-x
[24] B. Liu, Uncertainty Theory, 2007.
[25] B. Liu, Uncertainty Theory.Berlin:springer, 2017.
[26] Z. Moghaddas, B.M. Tosarkani, and S. Yousefi, Resource reallocation for improving sustainable supply chain performance: an inverse data envelopment analysis, Int. J. Prod. Econ. 252 (2022), 108560. https://doi.org/10.1016/j.ijpe.2022.108560 [27] O.B. Olesen and N.C. Petersen, Stochastic data envelopment analysis–A review, Eur. J. Oper. Res. 251 (2016), no. 1, 2–21. https://doi.org/10.1016/j.ejor.2015.07.058
[28] J. Pourmahmoud and N. Bagheri, Providing an uncertain model for evaluating the performance of a basic two-stage system, Soft Comput. 25 (2021), no. 6, 4739–4748. https://doi.org/10.1007/s00500–020-05481-8
[29] J. Pourmahmoud and N. Bagheri, Uncertain malmquist productivity index: An application to evaluate healthcare systems during COVID-19 pandemic, Socio-Econ. Plan. Sci. 87 (2023), 101522. https://doi.org/10.1016/j.seps.2023.101522
[30] J. Pourmahmoud and D. Norouzi Bene, Global Malmquist productivity index for evaluation of multistage series systems with undesirable and non-discretionary data, Commun. Comb. Optim. 10 (2025), no. 4, 905–931. https://doi.org/10.22049/cco.2024.29063.1831
[31] M. Soleimani-Damaneh, G.R. Jahanshahloo, and S. Abbasbandy, Computational and theoretical pitfalls in some current performance measurement techniques; and a new approach, Appl. Math. Comput. 181 (2006), no. 2, 1199–1207. https://doi.org/10.1016/j.amc.2006.01.085 [32] M. Wegener and G.R. Amin, Minimizing greenhouse gas emissions using inverse DEA with an application in oil and gas, Expert Syst. Appl. 122 (2019), 369–375. https://doi.org/10.1016/j.eswa.2018.12.058
[33] Q. Wei, J. Zhang, and X. Zhang, An inverse DEA model for inputs/outputs estimate, Eur. J. Oper. Res. 121, no. 1, 151–163. https://doi.org/10.1016/S0377-2217(99)00007-7
[34] H. Xiao, Y. Zhou, N. Zhang, D. Wang, Y. Shan, and J. Ren, co2 emission reduction potential in China from combined effects of structural adjustment of economy and efficiency improvement, Resour. conserv. recycl. 174 (2021), 105760. https://doi.org/10.1016/j.resconrec.2021.105760 [35] H. Yan, Q. Wei, and G. Hao, DEA models for resource reallocation and production input/output estimation, Eur. J. Oper. Res. 136 (2002), no. 1, 19–31. https://doi.org/10.1016/S0377-2217(01)00046-7
[36] M. Yang, Y. Hou, Q. Ji, and D. Zhang, Assessment and optimization of provincial $CO_2$ emission reduction scheme in China: an improved ZSG-DEA approach, Energy Econ. 91 (2020), 104931. https://doi.org/10.1016/j.eneco.2020.104931
[37] A. Younesi, F.H. Lotfi, and M. Arana-Jim´enez, Using slacks-based model to solve inverse DEA with integer intervals for input estimation, Fuzzy Optim. Decis. Mak. 22 (2023), no. 4, 587–609. https://doi.org/10.1007/s10700-022-09403-1
[38] L.A. Zadeh, Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets Syst. 100 (1978), no. 1, 3–28. https://doi.org/10.1016/S0165-0114(99)80004-9
[39] L.A. Zadeh, G.J. Klir, and B. Yuan, Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by lotfi a zadeh.united states, World scientific, 1996.
[40] J. Zhang, W. Jin, G.l. Yang, H. Li, Y. Ke, and S.P. Philbin, Optimizing regional allocation of CO2 emissions considering output under overall efficiency, SocioEcon. Plan. Sci. 77 (2021), 101012. https://doi.org/10.1016/j.seps.2021.101012
[41] J. Zhang, H. Li, B. Xia, and M. Skitmore, Impact of environment regulation on the efficiency of regional construction industry: A 3-stage data envelopment analysis (DEA), J. Clean. Prod. 200 (2018), 770–780. https://doi.org/10.1016/j.jclepro.2018.07.189 | ||
|
آمار تعداد مشاهده مقاله: 90 تعداد دریافت فایل اصل مقاله: 119 |
||