Congestion-Based Benchmarking and Accessing Sustainability in Network DEA, Case study: Nine Iranian ‎T‎omato Paste Supply Chains‎

محورهای موضوعی : مجله بین المللی ریاضیات صنعتی

E. Mollaeian ¹ , Farhad Hosseinzade Lotfi ² , A. Toloie-Eshlaghy ³ , M. Rostamy Malkhalifeh ⁴ , M. A. Afshar Kazemi ⁵

1 - Department of Industrial Management,Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 - Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran.
3 - Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
4 - Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran.
5 - Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

تاریخ دریافت : 1398/09/20 تاریخ پذیرش : 1398/11/13 تاریخ انتشار : 1400/10/11

کلید واژه: Benchmark, Network DEA, Congestion, Sustainable ‎development, Inverse DEA,

چکیده مقاله :

This paper deals with benchmarking for a three-stage DMU. After investigating and eliminating congestion, adjustment of intermediate products and initial inputs have achieved by utilizing an Inverse DEA model, which dominates the current vectors and serve as a benchmark that is to the best of this paper knowledge. This process has two cases. First, the overall efficiency stays fixed, and the second case, is corresponded to the overall efficiency ‎improvement.‎

چکیده انگلیسی:

این مقاله در ارتباط با مبحث الگویابی برای واحد تصمیم گیرنده سه مرحله  می باشد. ابتدا تراکم در واحد شناسایی می شود و حذف می گردد، بدین ترتیب مقادیر تعدیل شده برای محصولات میانی و ورودی های اولیه سیستم بدست می آید. این مقادیر تعدیل شده، که منجر به مغلوب کردن مقادیر جاری و تعیین کردن نقطه الگو حاصل می شوند، با استفاده از مدل معکوس DEA حاصل می شود که یکی از مهم ترین بخش های این مقاله است. این فرآیند در دو حالت مجزا اتفاق می افتد: حالت اول اینکه کارایی کل سیستم ثابت می ماند و حالت دوم اینکه کارایی کلی سیستم درصدی بهبود دارد.

منابع و مأخذ:

[1] Gh R.Amin, S. Al-Muharrami, M. Toloo, A combined goal programming and inverse DEA method for target setting in mergers, Expert Systems with Applications 115 (2019) 412-417.
[2] J. Aparicio, J. M. Cordero, J. T. Pastor, The determination of the least distance to the strongly efficient frontier in Data Envelopment Analysis oriented models: Modelling and computational aspects, Omega 71 (2017) 1-10.
[3] R. D. Banker, A. Charnes, W. W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis, Management Science 30 (1984) 1078-1092.
[4] A. Charnes, W. W. Cooper, E. Rhodes, Measuring the efficiency of decision-making, European Journal Operation Research 2 (1978) 429-444.
[5] Y. Chen, W. Cook, N. Li, J. Zhu, Additive efficiency decomposition in two-stage DEA, European Journal of Operational Research 196 (2009) 1170-1176.
[6] C. Chen, H. Yan, Network DEA model for supply chain performance evaluation, European Journal Operation Research 213 (2011) 147-155.
[7] L. Chen, Y .Wang, F .Lai, F. Feng, An investment analysis for China’s sustainable development based on inverse data envelopment analysis, Journal of Cleaner Productivity 2 (2016) 1-12.
[8] W. W. Cooper, H. Deng, Z. M. Huang, S. X. Li, A one-model approach to congestion in DEA, Socio-Economic Planning Sciences 36 (2002) 231-238.
[9] W. D.Cook, J. L. Ruiz, I. Sirvent, J. Zhu, Within-group common benchmarking using DEA, European Journal of Operational Research 256 (2017)  901-910.
[10] W. D. Cook, N. Ramn, J. L. Ruiz, I. Sirvent, J. Zhu, DEA-based benchmarking for performance evaluation in pay-for-performance incentive plans, Omega, to appear (2018).

[11] R. Fare, L. Svensoon, Congestion on production factors, Econometrica 48 (1980) 1745-1753.
[12] R. Fare, S. Grosskopf, C. A. K. Lovell, The Measurement of Efficiency of Production, Kluwer Nijhoff, Boston, (1985).
[13] M. Ghiyasi, Inverse DEA based on cost and revenue efficiency, Computers and industrial Engineering 114 (2017) 258-263.
[14] M. Izadikhah, R.Farzipoor Saen, Evaluating sustainability of supply chains by two-stage range directional measure in the presence of negative data, Transportation Research Part D 49 (2016) 110-126.
[15] G. R. Jahanshahloo, M. Khodabakhshi, Suitable combination of improving output in DEA with determining input congestion considering textile industry of china, Applied Mathematics and computation 151 (2004) 263-273.
[16] G. R. Jahanshahloo, F. Hosseynzadeh Lotfi, N. Shoja, G. Tohidi, S. Razavyan, Input estimation and Identification of extra inputs in Invers DEA models, Applied Mathematics and Computation 156 (2004) 427-437.
[17] G. R. Jahanshahloo, M. Soleimani-damaneh, Inverse DEA under intertemporal dependence using multiple-objective programming, Eur. J. Operational Res. 240 (2015) 447-456.
[18] Ch. Kao, Sh. Hwang, Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan, European Journal of Operational Research 185 (2008) 418-429.
[19] M. Kalantary, R. Farzipoor Saen, Assessing sustainability of supply chains: An inverse network dynamic DEA model, Computers and industrial Engineering 135 (2019) 1224-1238.
[20] J.Krer, Direct targeting of efficient DMUs for benchmarking, International Journal of Production Economics 18 (2018) 50925-5273.
[21] R. N. Khalili, S. Duecker, From cleaner production to sustainable development: the role of academia, Journal Clean, Product. 96 (2015) 30-43.
[22] M. khodakarami, M. Shabani, R. FarzipoorSaen, M. Azadi, Developing distinctive twostage data envelopment analysis model: an application in evaluating the sustainability of supply chain management, Measurement 70 (2015) 62-74.
[23] M. Lothgren, M. Tambour, Productivity and customer satisfaction in Swedish pharmacies: A network model, European Journal of Operation Research 115 (1999) 449-458.
[24] J. S. Liu, L. Y. Y. Lu, W. M. Lu, Research fronts in data envelopment analysis, OmegaInt J Manag Sci 41 (2016) 3-15.
[25] J. D. Linton, R. Kelassen, V. Jayaraman, Sustainable supply chain : an introduction, Journal of Operation Management 25 (2007) 1075-1082.
[26] S. Lertworasirikul, P. Charnsethikul, Inverse data envelopment analysis model to preserve relative efficiency values: the case of variable returns to scale, Comput. Industrial Eng. 61 (2011) 1017-1023.
[27] S. M. Mirhedayatiyan, M. Azadi, R. Farzipoor-Saen, A novel network DEA model for evaluating green supply chain, International Journal Production Economic 147 (2014) 544-554.
[28] A. M. Prito, J. L. Zofio, Network DEA efficiency in input-output models: With an application to OECD countries, European Journal of Operation Research 178 (2007) 292-304.
[29] N. Ramn, J. L. Ruiz, I. Sirvent, On the use of DEA models with weigh restrictions for benchmarking and target setting, in Advances in efficiency and productivity (J. Aparicio, C. A. K. Lovell and J. T. Pastor, eds.), Springer (2016).
[30] N. Ram, J. L. Ruiz, I. Sirvent, Two-step benchmarking: setting more realistically achievable targets in DEA, Expert Systems with Applications 92 (2018) 124-131.
[31] J. L. Ruiz, I. Sirvent, Common benchmarking and ranking of units with DEA, Omega 65 (2016) 1-9.
[32] J. L. Ruiz, I. Sirvent, Performance evaluation through DEA benchmarking adjusted to goals, Omega 15 (2018) 305-483.
[33] M. Wegener, Gh. R. Amin, Minimizing greenhouse gas emissions using inverse DEA with an application in oil and gas, Expert Systems with Applications 122 (2019) 369-375.
[34] Q. L. Wei, J. Zhang, X. Zhang, An inverse DEA model for input/output estimate, Eur. J. Operation Res. 121 (2000) 151-163.
[35] M. M. Yu, Measuring the efficiency of multimode bus transit- evidence form Taiwans bus system, Applied Economics Letters 15 ( 2007) 647-653.