Improved efficiency assessment in network DEA through interval data analysis: An empirical study in agriculture

Open Access

Issue		RAIRO-Oper. Res. Volume 57, Number 6, November-December 2023


Page(s)		3007 - 3031
DOI		https://doi.org/10.1051/ro/2023154
Published online		30 November 2023

A. Charnes, W.W. Cooper and E. Rhodes, Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2 (1987) 429–444. [Google Scholar]
W.W. Cooper, K.S. Park and G. Yu, IDEA and AR-IDEA: models for dealing with imprecise data in DEA. Manage. Sci. 45 (1999) 597–607. [CrossRef] [Google Scholar]
S.H. Kim, C.K. Park and K.S. Park, An application of data envelopment analysis in telephone offices evaluation with partial data. Comput. Oper. Res. 26 (1999) 59–72. [CrossRef] [Google Scholar]
W.W. Cooper, K.S. Park and G. Yu, IDEA (imprecise data envelopment analysis) with CMDs (column maximum decision making units). J. Oper. Res. Soc. 52 (2001) 176–181. [CrossRef] [Google Scholar]
D.K. Despotis and Y.G. Smirlis, Data envelopment analysis with imprecise data. Eur. J. Oper. Res. 140 (2002) 24–36. [Google Scholar]
T. Entani, Y. Maeda and H. Tanaka, Dual models of interval DEA and its extension to interval data. Eur. J. Oper. Res. 136 (2002) 32–45. [Google Scholar]
Y.K. Lee, K.S. Park and S.H. Kim, Identification of inefficiencies in an additive model based IDEA (imprecise data envelopment analysis). Comput. Oper. Res. 29 (2002) 1661–1676. [CrossRef] [MathSciNet] [Google Scholar]
J. Zhu, Imprecise data envelopment analysis (IDEA): a review and improvement with an application. Eur. J. Oper. Res. 144 (2003) 513–529. [Google Scholar]
K.S. Park, Simplification of the transformations and redundancy of assurance regions in IDEA (imprecise DEA). J. Oper. Res. Soc. 55 (2004) 1363–1366. [CrossRef] [Google Scholar]
J. Zhu, Imprecise DEA via standard linear DEA models with a revisit to a Korean mobile telecommunication company. Oper. Res. 52 (2004) 323–329. [Google Scholar]
G.R. Jahanshahloo, R. Kazemi-Matin and A. Hadi-Vencheh, On FDH efficiency analysis with interval data. Appl. Math. Comput. 159 (2004) 47–55. [MathSciNet] [Google Scholar]
G.R. Jahanshahloo, R. Kazemi-Matin and A. Hadi-Vencheh, On return to scale of fully efficient DMUs in data envelopment analysis under interval data. Appl. Math. Comput. 154 (2004) 31–40. [MathSciNet] [Google Scholar]
G.R. Jahanshahloo, F. Hosseinzadeh-Lofti and M. Moradi, Sensitivity and stability analysis in DEA with interval data. Appl. Math. Comput. 156 (2004) 463–477. [MathSciNet] [Google Scholar]
A. Amirteimoori and S. Kordrostami, Multi-component efficiency measurement with imprecise data. Appl. Math. Comput. 162 (2005) 1265–1277. [MathSciNet] [Google Scholar]
Y.M. Wang, R. Greatbanks and J.B. Yang, Interval efficiency assessment using data envelopment analysis. Fuzzy Sets Syst. 153 (2005) 347–370. [Google Scholar]
M.S. Haghighat and E. Khorram, The maximum and minimum number of efficient units in DEA with interval data. Appl. Math. Comput. 163 (2005) 919–930. [MathSciNet] [Google Scholar]
C. Kao, Interval efficiency measures in data envelopment analysis with imprecise data. Eur. J. Oper. Res. 174 (2006) 1087–1099. [CrossRef] [Google Scholar]
Y.G. Smirlis, E.K. Maragos and D.K. Despotis, Data envelopment analysis with missing values: an interval DEA approach. Appl. Math. Comput. 177 (2006) 1–10. [MathSciNet] [Google Scholar]
R. Farzipoor-Saen, A decision model for selecting technology suppliers in the presence of nondiscretionary factors. Appl. Math. Comput. 181 (2006) 1609–1615. [Google Scholar]
R. Farzipoor-Saen, An algorithm for ranking technology suppliers in the presence of nondiscretionary factors. Appl. Math. Comput. 181 (2006) 1616–1623. [Google Scholar]
R. Farzipoor-Saen, Suppliers selection in the presence of both cardinal and ordinal data. Eur. J. Oper. Res. 183 (2007) 741–747. [CrossRef] [Google Scholar]
R. Kazemi-Matin, G.R. Jahanshahloo and A. Hadi-Vencheh, Inefficiency evaluation with an additive DEA model under imprecise data, an application on IAUK departments. J. Oper. Res. Soc. Jpn. 50 (2007) 163–177. [Google Scholar]
K.S. Park, Efficiency bounds and efficiency classifications in DEA with imprecise data. J. Oper. Res. Soc. 58 (2007) 533–540. [CrossRef] [Google Scholar]
G.R. Jahanshahloo, F. Hosseinzadeh-Lotfi, M. Rostamy-Malkhalifeh and M. Ahadzadeh-Namin, A generalized model for data envelopment analysis with interval data. Appl. Math. Model. 33 (2009) 3237–3244. [CrossRef] [MathSciNet] [Google Scholar]
R. Farzipoor-Saen, Supplier selection by the pair of nondiscretionary factors-imprecise data envelopment analysis models. J. Oper. Res. Soc. 60 (2009) 1575–1582. [CrossRef] [Google Scholar]
K.S. Park, Duality, efficiency computations and interpretations in imprecise DEA. Eur. J. Oper. Res. 200 (2010) 289–296. [CrossRef] [Google Scholar]
H. Azizi and H. Ganjeh-Ajirlu, Measurement of the worst practice of decisionmaking units in the presence of non-discretionary factors and imprecise data. Appl. Math. Model. 35 (2011) 4149–4156. [CrossRef] [MathSciNet] [Google Scholar]
R. Farzipoor-Saen, Media selection in the presence of flexible factors and imprecise data. J. Oper. Res. Soc. 62 (2011) 1695–1703. [CrossRef] [Google Scholar]
R. Farzipoor-Saen, International market selection using advanced data envelopment analysis. IMA J. Manage. Math. 22 (2011) 371–386. [Google Scholar]
C. Kao and S.T. Liu, Efficiencies of two-stage systems with fuzzy data. Fuzzy Set Syst. 176 (2011) 20–35. [CrossRef] [Google Scholar]
A. Emrouznejad, M. Rostamy-Malkhalifeh, A. Hatami-Marbini and M. Tavana, General and multiplicative non-parametric corporate performance models with interval ratio data. Appl. Math. Model. 36 (2012) 5506–5514. [CrossRef] [Google Scholar]
W. Zhu and Z. Zhou, Interval efficiency of two-stage network DEA model with imprecise data. INFOR 51 (2013) 142–150. [Google Scholar]
Y. Bouzembrak, H. Allaoui, G. Goncalves, H. Bouchriha and M. Baklouti, A possibilistic linear programming model for supply chain network design under uncertainty. IMA J. Manage. Math. 24 (2013) 209–229. [Google Scholar]
A. Hadi-Vencheh, A. Hatami-Marbini, Z. Ghelej-Beigi and K. Gholami, An inverse optimization model for imprecise data envelopment analysis. Optimization 64 (2015) 2441–2454. [CrossRef] [MathSciNet] [Google Scholar]
H. Azizi, S. Kordrostami and A. Amirteimoori, Slacks-based measures of efficiency in imprecise data envelopment analysis: an approach based on data envelopment analysis with double frontiers. Comput. Ind. Eng. 79 (2015) 42–51. [CrossRef] [Google Scholar]
F. He, X. Xu, R. Chen and L. Zhu, Interval efficiency improvement in DEA by using ideal points. Measurement 87 (2016) 138–145. [CrossRef] [Google Scholar]
G.H. Shirdel, S. Ramezani-Tarkhorani and Z. Jafari, A method for evaluating the performance of decision making units with imprecise data using common set of weights. Int. J. Appl. Comput. Math. 3 (2017) 411–423. [CrossRef] [MathSciNet] [Google Scholar]
G. Wei and K. Wang, A comparative study of robust efficiency analysis and data envelopment analysis with imprecise data. Expert Syst. Appl. 81 (2017) 28–38. [CrossRef] [Google Scholar]
M. Toloo, E. Keshavarz and A. Hatami-Marbini, Dual-role factors for imprecise data envelopment analysis. Omega 77 (2018) 15–31. [PubMed] [Google Scholar]
R. Mo, H. Huang and L. Yang, An interval efficiency measurement in DEA when considering undesirable outputs. Complexity 2020 (2020) 1–12. [CrossRef] [Google Scholar]
S. Ghobadi, Merging decision-making units with interval data. RAIRO: Oper. Res. 55 (2021) S1605–S1631. [CrossRef] [EDP Sciences] [Google Scholar]
R. Färe and S. Grosskopf, Network DEA. Soc.-Econ. Plann. Sci. 34 (2000) 35–49. [CrossRef] [Google Scholar]
C. Kao, Network data envelopment analysis: a review. Eur. J. Oper. Res. 239 (2014) 1–16. [Google Scholar]
C. Kao, Network Data Envelopment Analysis Foundations and Extensions, 2nd edition. Springer Cham (2023) 427. [Google Scholar]
R. Färe, R. Grabowski, S. Grosskopf and S. Kraft, Efficiency of a fixed but allocatable input: a non-parametric approach. Econ. Lett. 56 (1997) 187–193. [Google Scholar]
W.D. Cook, M. Hababou and H.J.H. Tuenter, Multicomponent efficiency measurement and shared inputs in data envelopment analysis: an application to sales and service performance in bank branches. J. Prod. Anal. 14 (2000) 209–224. [CrossRef] [Google Scholar]
W.D. Cook and M. Hababou, Sales performance measurement in bank branches. Omega 29 (2001) 299–307. [CrossRef] [Google Scholar]
G.R. Jahanshahloo, A. Amirteimoori and S. Kordrostami, Multicomponent performance, progress and regress measurement and shared inputs and outputs in DEA for panel data: an application in commercial bank branches. Appl. Math. Comput. 151 (2004) 1–16. [MathSciNet] [Google Scholar]
M.M. Yu and C.K. Fan, Measuring the cost effectiveness of multimode bus transit in the presence of accident risks. Transp. Plan. Techn. 129 (2006) 383–407. [Google Scholar]
C. Kao, Efficiency measurement for parallel production systems. Eur. J. Oper. Res. 196 (2009) 1107–1112. [Google Scholar]
C. Kao, Efficiency decomposition for parallel production systems. J. Oper. Res. Soc. 63 (2012) 64–71. [CrossRef] [Google Scholar]
M.D. Kremantzis, P. Beullens, L.S. Kyrgiakos and J. Klein, Measurement and evaluation of multi-function parallel network hierarchical DEA systems. Soc.-Econ. Plann. Sci. 84 (2022) 101428. [CrossRef] [Google Scholar]
M.D. Troutt, P.J. Ambrose and C.K. Chan, Optimal throughput for multistage input–output processes. Int. J. Oper. Prod. Manage. 21 (2001) 148–158. [CrossRef] [Google Scholar]
A. Amirteimoori and S. Kordrostami, DEA-like models for multi-component performance measurement. Appl. Math. Comput. 163 (2005) 735–743. [MathSciNet] [Google Scholar]
K.S. Park and K. Park, Measurement of multi period aggregative efficiency. Eur. J. Oper. Res. 193 (2009) 567–580. [CrossRef] [Google Scholar]
M. Tsutsui and M. Goto, A multi-division efficiency evaluation of U.S. electric power companies using a weighted slacks-based measure. Soc.-Econ. Plann. Sci. 43 (2009) 201–208. [CrossRef] [Google Scholar]
C. Kao, Efficiency decomposition in network data envelopment analysis: a relational model. Eur. J. Oper. Res. 192 (2009) 949–962. [CrossRef] [Google Scholar]
Q.L. Wei and T.S. Chang, Optimal system design series-network DEA models. J. Oper. Res. Soc. 52 (2011) 1109–1119. [CrossRef] [Google Scholar]
L. Fang, Optimal budget for system design series network DEA model. J. Oper. Res. Soc. 65 (2014) 1781–1787. [CrossRef] [Google Scholar]
R. Lin and Q. Liu, Directional distance based efficiency decomposition for series system in network data envelopment analysis. J. Oper. Res. Soc. 73 (2021) 1873–1888. [Google Scholar]
T.R. Sexton and H.F. Lewis, Two-stage DEA: an application to major league baseball. J. Prod. Anal. 19 (2003) 227–249. [CrossRef] [Google Scholar]
C. Kao and S.N. Hwang, Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan. Eur. J. Oper. Res. 85 (2008) 418–429. [CrossRef] [Google Scholar]
Y. Chen, W.D. Cook, N. Li and J. Zhu, Additive efficiency decomposition in two-stage DEA. Eur. J. Oper. Res. 196 (2009) 1170–1176. [Google Scholar]
L. Liang, Z.Q. Li and W.D. Cook, Data envelopment analysis efficiency in two-stage networks with feedback. IIE. Trans. 43 (2011) 309–322. [CrossRef] [Google Scholar]
J.S. Liu and W.M. Lu, Network-based method for ranking of efficient units in two-stage DEA models. J. Oper. Res. Soc. 63 (2012) 1153–1164. [CrossRef] [Google Scholar]
G. Halkos, N. Tzeremes and S. Kourtzidis, Weight assurance region in two-stage additive efficiency decomposition DEA model: an application to school data. J. Oper. Res. Soc. 66 (2015) 696–704. [CrossRef] [Google Scholar]
S. Aviles-Sacoto, W.D. Cook, R. Imanirad and J. Zhu, Two-stage network DEA: when intermediate measures can be treated as outputs from the second stage. J. Oper. Res. Soc. 66 (2015) 1868–1877. [Google Scholar]
M. Tavana, M.A. Kaviani, D. Di-Caprio and B. Rahpeyma, A two-stage data envelopment analysis model for measuring performance in three-level supply chains. Measurement 78 (2016) 322–333. [CrossRef] [Google Scholar]
R. Azizi and R. Kazemi-Matin, Ranking two-stage production units in data envelopment analysis. Asia Pac. J. Oper. Res. 33 (2016) 1–19. [Google Scholar]
G. Halkos, N. Tzeremes and S. Kourtzidis, A unified classification of two-stage DEA models. Surv. Oper. Res. Man. Sci. 19 (2014) 1–16. [Google Scholar]
M.S. Shahbazifar, R. Kazemi Matin, M. Khounsiavash and F. Koushki, Group ranking of two-stage production units in network data envelopment analysis. RAIRO: Oper. Res. 55 (2021) 1825–1840. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
H.F. Lewis and T.R. Sexton, Network DEA: efficiency analysis of organizations with complex internal structure. Comput. Oper. Res. 31 (2004) 1365–1410. [Google Scholar]
C. Kao and S.N. Hwang, Efficiency measurement for network systems: IT impact on firm performance. Decis. Support Syst. 48 (2010) 437–446. [CrossRef] [Google Scholar]
S. Lozano, Scale and cost efficiency analysis of networks of processes. Expert Syst. Appl. 38 (2011) 6612–6617. [CrossRef] [Google Scholar]
R. Kazemi-Matin and R. Azizi, A unified network-DEA model for performance measurement of production systems. Measurement 60 (2015) 186–193. [CrossRef] [Google Scholar]
F. Boloori, M. Afsharian and J. Pourmahmoud, Equivalent multiplier and envelopment DEA models for measuring efficiency under general network structures. Measurement 80 (2016) 259–269. [CrossRef] [Google Scholar]
A. Kalhor and R. Kazemi-Matin, Performance evaluation of general network production processes with undesirable outputs: a DEA approach. RAIRO: Oper Res. 52 (2018) 17–34. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
F. Yang, Y. Sun, D. Wang and S. Ang, Network data envelopment analysis with two-level maximin strategy. RAIRO: Oper. Res. 56 (2022) 2543–2556. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
L.S. Kyrgiakos, G. Kleftodimos, G. Vlontzos and P.M. Pardalos, A systematic literature review of data envelopment analysis implementation in agriculture under the prism of sustainability. Oper. Res. Int. J. 23 (2023) 7. [CrossRef] [Google Scholar]
V.H.L. Saputri, W. Sutopo, M. Hisjam and A. Ma’aram, Sustainable agri-food supply chain performance measurement model for GMO and Non-GMO using data envelopment analysis method. Appl. Sci. 9 (2019) 1199. [CrossRef] [Google Scholar]
A. Kord, A. Payan and S. Saati, Network DEA models with stochastic data to assess the sustainability performance of agricultural practices: an application for Sistan and Baluchestan Province in Iran. J. Math. 2022 (2022) 1–19. [CrossRef] [Google Scholar]
L.C. Lu, S.Y. Chiu, Y. Ho and T.H. Chang, Three-stage circular efficiency evaluation of agricultural food production, food consumption, and food waste recycling in EU countries. J. Clean. Prod. 343 (2022) 130870. [CrossRef] [Google Scholar]
A. Kord, A. Payan and S. Saati, Sustainability and optimal allocation of human resource of agricultural practices in Sistan and Baluchestan Province based on network DEA. J. Math. Extension 15 (2021) 1–44. [Google Scholar]
A. Abbas, C. Zhao, M. Waseem and A.K.R. Ahmad Khan, Analysis of energy input-output of farms and assessment of greenhouse gas emissions: a case study of cotton growers. Front. Environ. Sci. 9 (2022) 826838. [CrossRef] [Google Scholar]
Y. Yang, Q. Zhuang, G. Tian and S. Wei, A management and environmental performance evaluation of China’s family farms using an ultimate comprehensive cross-efficiency model (UCCE). Sustainability 11 (2019) 6. [Google Scholar]
A. Nandy, P.K. Singh and A.K. Singh, Systematic review and meta-regression analysis of technical efficiency of agricultural production systems. Global Bus. Rev. 22 (2021) 396–421. [CrossRef] [Google Scholar]
X. Li, X. Li and J. Jiang, Deep intelligence-driven efficient forecasting for the agriculture economy of computational social systems. Comput. Intell. Neurosci. 2022 (2022). DOI: 10.1155/2022/2854675. [Google Scholar]
K. Kočišová, Application of the DEA on the measurement of efficiency in the EU countries. Agric. Econ. 61 (2015) 51–62. [Google Scholar]

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