Abstract
In the present study, a basic two-stage system for uncertain conditions was evaluated. A key factor in DEA studies is the use of accurate measurement of all factors. However, in many cases, such as the release of carbon dioxide and the production system, inputs and outputs are very volatile. Volatile cases commonly were studied by using pre-known information in which fuzzy theory or probability theory was applied. In the absence of knowledge for probability and fuzzy theories, it is possible to use an expert’s belief degrees for modeling. For such cases, uncertainty theory can be applied which was introduced by Liu (Uncertainty theory, Springer, Berlin, 2007). In this study, a basic two-stage model was extended to consider uncertain conditions. Several theorems were presented to discuss some features of the introduced model. When the data had a linear distribution, the efficiencies of the system and sub-system were calculated. Finally, a numerical example was presented to illustrate the proposed model.
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References
Banker RD (1993) Maximum likelihood, consistency and data envelopment analysis: a statistical foundation. Manag Sci 39:1265–1273
Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30:1078–1092
Boloori F, Pourmahmoud J (2016) A modified SBM-NDEA approach for the efficiency measurement in bank branches. Oper Res Int J 16:301–326
Charnes A, Cooper W, Golany B, Halek R, Klopp G, Schmitz E, Thomas D (1986) 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, TX
Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444
Cooper WW, Deng H, Huang Z, Li SX (2002) Chance constrained programming approaches to technical efficiencies and inefficiencies in stochastic data envelopment analysis. J Oper Res Soc 53:1347–1356
Dyson R, Shale EA (2010) Data envelopment analysis, operational research and uncertainty. J Oper Res Soc 61:25–34
Färe R, Grosskopf S (1996) Productivity and intermediate products: a frontier approach. Econ Lett 50:65–70
Färe R, Whittaker G (1995) An intermediate input model of dairy production using complex survey data. J Agric Econ 46:201–213
Ghaffari-Hadigheh A (2019) Roman domination problem with uncertain positioning and deployment costs. Soft Comput 24:1–9
Hamidzadeh J, Ghadamyari R (2020) Clustering data stream with uncertainty using belief function theory and fading function. Soft Comput 24:8955–8974
Hatami-Marbini A, Emrouznejad A, Tavana M (2011) A taxonomy and review of the fuzzy data envelopment analysis literature: two decades in the making. Eur J Oper Res 214:457–472
Izadikhah M, Saen RF (2018) Assessing sustainability of supply chains by chance-constrained two-stage DEA model in the presence of undesirable factors. Comput Oper Res 100:343–367
Jahanshahloo GR, Vencheh AH, Foroughi AA, Matin RK (2004) Inputs/outputs estimation in DEA when some factors are undesirable. Appl Math Comput 156:19–32
Jiang B, Li Y, Lio W, Li J (2018a) Sustainability efficiency evaluation of seaports in China: an uncertain data envelopment analysis approach. Soft Comput 24:2503–2514
Jiang B, Lio W, Li X (2018b) An uncertain DEA model for scale efficiency evaluation. IEEE Trans Fuzzy Syst 27:1616–1624
Jiang B, Chen H, Li J, Lio W (2020a) The uncertain two-stage network DEA models. Soft Comput 1–9. https://doi.org/10.1007/s00500-020-05157-3
Jiang B, Zou Z, Lio W, Li J (2020b) The uncertain DEA models for specific scale efficiency identification. J Intell Fuzzy Syst 38:3403–3417
Kao C (2017) Network data envelopment analysis. Int Ser Oper Res Manag Sci. https://doi.org/10.1007/978-3-319-31718-2
Kao C, Hwang S-N (2008) Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan. Eur J Oper Res 185:418–429
Land KC, Lovell CK, Thore S (1993) Chance-constrained data envelopment analysis. Manag Decis Econ 14:541–554
Li Y, Yang Z (2019) Games with incomplete information and uncertain payoff: from the perspective of uncertainty theory. Soft Comput 23:13669–13678
Lio W, Liu B (2018) Uncertain data envelopment analysis with imprecisely observed inputs and outputs. Fuzzy Optim Decis Mak 17:357–373
Liu B (2007) Uncertainty theory. Springer, Berlin, pp 205–234
Liu B (2009) Some research problems in uncertainty theory. J Uncertain Syst 3:3–10
Liu B (2010) Uncertainty theory: a branch of mathematics for modeling human uncertainty, vol 300. Springer, Berlin, Heidelberg
Liu B (2015) Uncertainty theory. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44354-5
Loucks DP, Van Beek E (2017) Water resource systems planning and management: an introduction to methods, models, and applications. Springer, Berlin
Ma J, Qi L, Deng L (2017) Efficiency measurement and decomposition in hybrid two-stage DEA with additional inputs. Expert Syst Appl 79:348–357
Nejad ZM, Ghaffari-Hadigheh A (2018) A novel DEA model based on uncertainty theory. Ann Oper Res 264:367–389
Pourmahmoud J, Bafekr-Sharak N (2018) Measuring cost efficiency with new fuzzy DEA models. Int J Fuzzy Syst 20:155–162
Rao C, Lin H, Liu M (2019) Design of comprehensive evaluation index system for P2P credit risk of “three rural” borrowers. Soft Comput 24:1–17
Rolf F, Shawna G (2000) Network DEA. Socio-Econ Plan Sci 34:35–49
Rouyendegh BD, Yildizbasi A, Üstünyer P (2019) Intuitionistic Fuzzy TOPSIS method for green supplier selection problem. Soft Comput 24:1–14
Sengupta JK (1992) A fuzzy systems approach in data envelopment analysis. Comput Math Appl 24:259–266
Soleimani-Damaneh M, Jahanshahloo GR, Abbasbandy S (2006) Computational and theoretical pitfalls in some current performance measurement techniques; and a new approach. Appl Math Comput 181:1199–1207
Sotiros D, Koronakos G, Despotis DK (2019) Dominance at the divisional efficiencies level in network DEA: the case of two-stage processes. Omega 85:144–155
Tavana M, Khalili-Damghani K, Arteaga FJS, Mahmoudi R, Hafezalkotob A (2018) Efficiency decomposition and measurement in two-stage fuzzy DEA models using a bargaining game approach. Comput Ind Eng 118:394–408
Tone K, Tsutsui M (2009) Network DEA: a slacks-based measure approach. Eur J Oper Res 197:243–252
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Pourmahmoud, J., Bagheri, N. Providing an uncertain model for evaluating the performance of a basic two-stage system. Soft Comput 25, 4739–4748 (2021). https://doi.org/10.1007/s00500-020-05481-8
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DOI: https://doi.org/10.1007/s00500-020-05481-8