Article
Full entry |
PDF
(0.5 MB)
Feedback
PDF
(0.5 MB)
Feedback
Keywords:
data envelopment analysis (DEA); efficiency; closed-loop supply chain; proportional dual-role factor; input/output
data envelopment analysis (DEA); efficiency; closed-loop supply chain; proportional dual-role factor; input/output
Summary:
Data Envelopment Analysis (DEA) is a beneficial mathematical programming method to measure relative efficiencies. In conventional DEA models, Decision Making Units (DMUs) are usually considered as black boxes. Also, the efficiency of DMUs is evaluated in the presence of the specified inputs and outputs. Nevertheless, in real-world applications, there are situations in which the performance of multi-stage processes like supply chains with forward and reverse flows must be measured such that some of the intervening factors, called proportional dual-role factors, are presented that one part of each proportional dual-role factor plays the input role and the other plays the output role. To address this issue, the current study proposes radial and non-radial DEA models for evaluating the overall and stage efficiencies of the closed-loop supply chains when there are proportional dual-role factors. To illustrate, a proportional dual-role factor is divided into portions of the input of the first stage and the output of the second stage such that the optimal overall and stage efficiency scores of closed-loop supply chain are obtained. A case study is used to illustrate the proposed approach. The experimental results obtained from real world data show the convincing performance of our proposed method.
References:
[1] Amirteimoori, A.: A DEA two-stage decision processes with shared resources. Cent. Eur. J. Oper. Res. 21 (2013), 141-151. DOI 10.1007/s10100-011-0218-3 | MR 3016880
[2] Amirteimoori, A., Khoshandam, L.: A data envelopment analysis approach to supply chain efficiency. Adv. Decision Sci. 8 (2011), 1-8. DOI 10.1155/2011/608324 | MR 2862474
[3] Amirteimoori, A., Khoshandam, L., Kordrostami, S.: Recyclable outputs in production process: a data envelopment analysis approach. Int. J. Oper. Res. 18 (2013), 62-70. DOI 10.1504/ijor.2013.055529 | MR 3136122
[4] Amirteimoori, A., Kordrostami, S., Azizi, H.: Additive models for network data envelopment analysis in the presence of shared resources. Transport. Res. D-Tr. E. 48 (2016), 411-424. DOI 10.1016/j.trd.2015.12.016
[5] Amirteimoori, A., Toloie-Eshlaghi, A., Homayoonfar, M.: Efficiency measurement in Two-Stage network structures considering undesirable outputs. Int. J. Industr. Math. 6 (2014), 65-71.
[6] Amirteimoori, A., Yang, F.: A DEA model for two-stage parallel-series production processes. RAIRO-Oper. Res. 48 (2014), 123-134. DOI 10.1051/ro/2013057 | MR 3177881
[7] Atici, K. B., Podinovski, V. V.: Using data envelopment analysis for the assessment of technical efficiency of units with different specialisations: An application to agriculture. Omega 54 (2015), 72-83. DOI 10.1016/j.omega.2015.01.015
[8] Sacoto, S. Avilés, Castorena, D. G., Cook, W. D., Delgado, H. C.: Time-staged outputs in DEA. Omega 55 (2015), 1-9. DOI 10.1016/j.omega.2015.01.019
[9] Aviles-Sacoto, S., Cook, W. D., Imanirad, R., Zhu, J.: Two-stage network DEA: when intermediate measures can be treated as outputs from the second stage. J. Oper. Res. Soc. 66 (2015), 1868-1877. DOI 10.1057/jors.2015.14
[10] Avkiran, N. K.: An illustration of dynamic network DEA in commercial banking including robustness tests. Omega 55 (2015), 141-150. DOI 10.1016/j.omega.2014.07.002
[11] Balfaqih, H., Nopiah, Z. M., Saibani, N., Al-Nory, M. T.: Review of supply chain performance measurement systems: 1998-2015. Comput. Ind. 82 (2016), 135-150. DOI 10.1016/j.compind.2016.07.002
[12] Beasley, J. E.: Comparing university departments. Omega 18 (1990), 171-183. DOI 10.1016/0305-0483(90)90064-g
[13] Beasley, J. E.: Determining teaching and research efficiencies. J. Oper. Res. Soc. 46 (1995), 441-452. DOI 10.1057/jors.1995.63
[14] Bian, Y., Liang, N., Xu, H.: Efficiency evaluation of Chinese regional industrial systems with undesirable factors using a two-stage slacks-based measure approach. J. Clean. Prod. 87 (2015), 348-356. DOI 10.1016/j.jclepro.2014.10.055 | MR 3679962
[15] Chao, C. M., Yu, M. M., Wu, H. N.: An application of the dynamic network DEA model: The case of banks in Taiwan. Emerg. Mark. Financ. Tr. 51 (2015), S133- S151. DOI 10.1016/j.jclepro.2014.10.055
[16] Charnes, A., Cooper, W. W.: Programming with linear fractional functionals. Na. Res. Logist. Q. 9 (1962), 181-186. DOI 10.1002/nav.3800090303 | MR 0152370
[17] Charnes, A., Cooper, W. W., Rhodes, E.: Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2 (1978), 429-444. DOI 10.1016/0377-2217(78)90138-8 | MR 0525905 | Zbl 0425.90086
[18] Chen, W. C.: Revisiting dual-role factors in data envelopment analysis: derivation and implications. IIE. Trans. 7 (2014), 653-663. DOI 10.1080/0740817x.2012.721943
[19] Chen, Y., Du, J., Sherman, H. D., Zhu, J.: DEA model with shared resources and efficiency decomposition. Eur. J. Oper. Res. 207 (2010), 339-349. DOI 10.1016/j.ejor.2010.03.031 | MR 2659449
[20] Chen, C., Yan, H.: Network DEA model for supply chain performance evaluation. Eur. J. Oper. Res. 213 (2011), 147-155. DOI 10.1016/j.ejor.2011.03.010 | MR 2795818
[21] Cook, W. D., Green, R. H., Zhu, J.: Dual-role factors in data envelopment analysis. IIE. Trans. 38 (2006), 105-115. DOI 10.1080/07408170500245570
[22] Cook, W. D., Zhu, J.: Classifying inputs and outputs in data envelopment analysis. Eur. J. Oper. Res. 180 (2007), 692-699. DOI 10.1016/j.ejor.2006.03.048
[23] Despotis, D. K., Sotiros, D., Koronakos, G.: A network DEA approach for series multi-stage processes. Omega 61 (2016), 35-48. DOI 10.1016/j.omega.2015.07.005
[24] Du, J., Chen, Y., Huo, J.: DEA for non-homogenous parallel networks. Omega 56 (2015), 122-132. DOI 10.1016/j.omega.2014.10.001
[25] Fang, L.: Optimal budget for system design series network DEA model. J. Oper. Res. Soc. 65 (2014), 1781-1787. DOI 10.1057/jors.2013.153
[26] Färe, R., Grosskopf, S.: Network DEA. Socio. Econom. Plan. Sci. 34 (2000), 35-49. DOI 10.1016/s0038-0121(99)00012-9
[27] Färe, R., Grosskopf, S.: Productivity and intermediate products: A frontier approach. Econom. Lett. 50 (1996), 65-70. DOI 10.1016/0165-1765(95)00729-6
[28] Färe, R., Lovell, C. A. Knox: Measuring the technical efficiency of production. J. Econom. Theory. 19 (1978), 150-162. DOI 10.1016/0022-0531(78)90060-1 | MR 0520432
[29] Färe, R., Primont, D.: Efficiency measures for multiplant firms. Oper. Res. Lett. 3 (1984), 257-260. DOI 10.1016/0167-6377(84)90057-9 | MR 0777641
[30] Fukuyama, H., Weber, W. L.: Measuring Japanese bank performance: a dynamic network DEA approach. J. Prod. Anal. 44 (2015), 249-264. DOI 10.1007/s11123-014-0403-1
[31] Heidari, M. D., Omid, M., Akram, A.: Optimization of energy consumption of broiler production farms using data envelopment analysis approach. Modern Appl. Sci. 5 (2011), 69-78. DOI 10.5539/mas.v5n3p69
[32] Johnes, J.: Data envelopment analysis and its application to the measurement of efficiency in higher education. Econom. Educ. Rev. 25 (2006), 273-288. DOI 10.1016/j.econedurev.2005.02.005
[33] Kao, C.: Efficiency decomposition in network data envelopment analysis: A relational model. Eur. J. Oper. Res. 192 (2009), 949-962. DOI 10.1016/j.ejor.2007.10.008 | MR 3225350
[34] Kao, C.: Efficiency measurement for parallel production systems. Eur. J. Oper. Res. 196 (2009), 1107-1112. DOI 10.1016/j.ejor.2008.04.020
[35] Kao, C., Hwang, S. N.: Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. Eur. J. Oper. Res. 185 (2008), 418-429. DOI 10.1016/j.ejor.2006.11.041
[36] Kao, C., Hwang, S. N.: Efficiency measurement for network systems: IT impact on firm performance. Decision Support Syst. 48 (2010), 437-446. DOI 10.1016/j.dss.2009.06.002
[37] Kordrostami, S., Noveiri, M. Jahani Sayyad: Evaluating the efficiency of decision making units in the presence of flexible and negative data. Indian. J. Sci. Technol. 5 (2012), 3776-3782. DOI 10.17485/ijst/2012/v5i12.20
[38] Liang, L., Li, Z. Q., Cook, W. D., Zhu, J.: Data envelopment analysis efficiency in two-stage networks with feedback. IIE Trans. 43 (2011), 309-322. DOI 10.1080/0740817x.2010.509307
[39] Liang, L., Yang, F., Cook, W. D., Zhu, J.: DEA models for supply chain efficiency evaluation. Ann. Oper. Res. 145 (2006), 35-49. DOI 10.1007/s10479-006-0026-7 | MR 2258637 | Zbl 1106.90045
[40] Liu, W., Zhou, Z., Ma, C., Liu, D., Shen, W.: Two-stage DEA models with undesirable input-intermediate-outputs. Omega 56 (2015), 74-87. DOI 10.1016/j.omega.2015.03.009 | MR 3362977
[41] Lozano, S.: Alternative SBM Model for Network DEA. Comput. Ind. Eng. 82 (2015), 33-40. DOI 10.1016/j.cie.2015.01.008
[42] Lozano, S.: Slacks-based inefficiency approach for general networks with bad outputs: An application to the banking sector. Omega 60 (2016), 73-84. DOI 10.1016/j.omega.2015.02.012
[43] Lozano, S., Gutiérrez, E., Moreno, P.: Network DEA approach to airports performance assessment considering undesirable outputs. Appl. Math. Model. 37 (2013), 1665-1676. DOI 10.1016/j.apm.2012.04.041
[44] Ma, J.: A two-stage DEA model considering shared inputs and free intermediate measures. Expert. Syst. Appl. 42 (2015), 4339-4347. DOI 10.1016/j.eswa.2015.01.040
[45] Maghbouli, M., Amirteimoori, A., Kordrostami, S.: Two-stage network structures with undesirable outputs: A DEA based approach. Measurement 48 (2014), 109-118. DOI 10.1016/j.measurement.2013.10.032
[46] Momeni, E., Tavana, M., Mirzagoltabar, H., Mirhedayatian, S. M.: A new fuzzy network slacks-based DEA model for evaluating performance of supply chains with reverse logistics. J. Intell. Fuzzy Syst. 27 (2014), 793-804. DOI 10.3233/ifs-131037 | MR 3256936
[47] Omrani, H., Soltanzadeh, E.: Dynamic DEA models with network structure: An application for Iranian airlines. J. Air. Transp. Manag. 57 (2016), 52-61. DOI 10.1016/j.jairtraman.2016.07.014
[48] Pastor, J. T., Ruiz, J. L., Sirvent, I.: An enhanced DEA Russell graph efficiency measure. Eur. J. Oper. Res. 115 (1999), 596-607. DOI 10.1016/s0377-2217(98)00098-8
[49] Rezaee, A., Esmaielzadeh, A.: Application of data envelopment analysis to evaluation energy efficiency in broiler production farms (case study: Maku free zone). Animal. Sci. J. 30 (2018), 27-40.
[50] Seiford, L. M., Zhu, J.: Profitability and marketability of the top 55 US commercial banks. Management Sci. 45 (1999), 1270-1288. DOI 10.1287/mnsc.45.9.1270
[51] Shabani, A., Saen, R. Farzipoor: Developing a novel data envelopment analysis model to determine prospective benchmarks of green supply chain in the presence of dual-role factor. Benchmark. Int. J. 22 (2015), 711-730. DOI 10.1108/bij-12-2012-0087
[52] Tajbakhsh, A., Hassini, E.: A data envelopment analysis approach to evaluate sustainability in supply chain networks. J. Clean. Prod. 105 (2015), 74-85. DOI 10.1016/j.jclepro.2014.07.054
[53] Tavana, M., Mirzagoltabar, H., Mirhedayatian, S. M., Saen, R. Farzipoor, Azadi, M.: A new network epsilon-based DEA model for supply chain performance evaluation. Comput. Ind. Engrg. 66 (2013), 501-513. DOI 10.1016/j.cie.2013.07.016
[54] Toloo, M., Keshavarz, E., Hatami-Marbini, A.: Dual-role factors for imprecise data envelopment analysis. Omega 77 (2018), 15-31. DOI 10.1016/j.omega.2017.05.005
[55] Tone, K., Tsutsui, M.: Network DEA: A slacks-based measure approach. Eur. J. Oper. Res. 197 (2009), 243-252. DOI 10.1016/j.ejor.2008.05.027
[56] Tone, K., Tsutsui, M.: Dynamic DEA with network structure: A slacks-based measure approach. Omega 42 (2014), 124-131. DOI 10.1016/j.omega.2013.04.002
[57] Wei, Q. L., Chang, T. S: Optimal system design series-network DEA models. J. Oper. Res. Soc. 62 (2011), 1109-1119. DOI 10.1057/jors.2010.45
[58] Wu, J., Zhu, Q., Ji, X., Chu, J., Liang, L.: Two-stage network processes with shared resources and resources recovered from undesirable outputs. Eur. J. Oper. Res. 251 (2016), 182-197. DOI 10.1016/j.ejor.2015.10.049 | MR 3447062
[59] Yang, F., Wu, D., Liang, L., Bi, G., Wu, D. D.: Supply chain DEA: production possibility set and performance evaluation model. Ann. Oper. Res. 185 (2011), 195-211. DOI 10.1007/s10479-008-0511-2 | MR 2788794
[60] Zhu, J.: Models for Evaluating Supply Chains and Network Structures. In: Quantitative Models for Performance Evaluation And Benchmarking: Data Envelopment Analysis with Spreadsheets. Springer International Publishing: Cham. 2014, pp. 311-344. DOI 10.1007/978-3-319-06647-9_15 | MR 3308434
Search
Partner of
