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An approach based slack variables in network data envelopment analysis to incorporate dynamic effects | ||
| Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies) | ||
| دوره 17، شماره 3، مهر 2024، صفحه 817-838 اصل مقاله (1.44 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ijms.2023.340611.675017 | ||
| نویسندگان | ||
| Seyed Javad Salehzadeh* 1؛ Seyed Reza Hejazi1؛ Mohammad Taghi Rezvan2 | ||
| 1Department of Industrial Engineering, Isfahan University of Technology, Isfahan, Iran | ||
| 2Department of Industrial Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran | ||
| چکیده | ||
| Senior managers are the customers of organizational performance measurement methods to help them make better decisions at the firm level. One of the most applicable methods is Network Data Envelopment Analysis (DEA). Network DEA models consider systems that have a network structure in which system inputs, after passing several intermediate interactions, are transformed into intermediate productions and finally leave the system as output products. However, many real-world cases do not necessarily conform to this network structure, which is related to the system outputs during multiple time periods or the same “dynamic” impacts. These structures cannot handle dynamic impacts. Therefore, this paper presents a novel structure that can consider the dynamic impacts and influences of sub-units on each other at various time periods. Besides, two models based on slack variables are proposed which can consider dynamic effects and calculate the efficiency of such networks. Using these models, the overall efficiency of networks is calculated for the whole time period. Finally, these models are applied to two examples, and the results obtained are compared with other methods. | ||
| کلیدواژهها | ||
| Efficiency؛ Network Data Envelopment Analysis؛ Dynamic effects؛ Slack-based model | ||
| مراجع | ||
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[1] Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253-281.
[2] Charnes, A., Cooper, W.W. and Rhodes, E. (1978). Measurement the efficiency of decision making unit, European Journal of Operational Research, 2, 429-444.
[3] Fare, R., and Grosskopf, S. (2000). Network DEA, Socio-economic Plannig Science, 34, 35-49.
[4] Lothgren, M., and Tambour, M. (1999). Productivity and customer satisfaction in Swedish pharmacies: A DEA network model, European Journal of Operational Research, 115, 449–458.
[5] Lewis, H. F., and Sexton, T. R. (2004). Network DEA: efficiency analysis of organizations with complex internal structure, Computers & Operations Research, 31(9), 1365-1410.
[6] Liang, L., Yang, F., Cook, W. D., and Zhu, J. (2006). DEA models for supply chain efficiency evaluation, Annals of Operations Research, 145(1), 35-49.
[7] Prieto, A. M., and Zofío, J. L., (2007). Network DEA efficiency in input–output models: with an application to OECD countries, European Journal of Operational Research, 178(1), 292-304.
[8] Yu, M. M., and Lin, E. T., (2008). Efficiency and effectiveness in railway performance using a multi-activity network DEA model, Omega, 36(6), 1005-1017.
[9] Kao, C., 2009. Efficiency decomposition in network data envelopment analysis: A relational model, European Journal of Operational Research, 192(3), 949-962.
[10] Tone, K., and Tsutsui, M. (2014). Dynamic DEA with network structure: A slacks-based measure approach, Omega, 42(1), 124-131.
[11] Fukuyama, H., and Weber, W. L. (2010). A slacks-based inefficiency measure for a two-stage system with bad outputs, Omega, 38(5), 398-409.
[12] Hatami-Marbini, A., and Saati, S. (2020). Measuring performance with common weights: network DEA. Neural Computing & Application, 32, 3599–3617.
[13] Shadab, M., Saati, S., Farzipoor Saen, R., Khoveyni, M., and Mostafaee, A. (2021). Measuring congestion in sustainable supply chain based on data envelopment analysis, Neural Computing & Application, 33, 12477–12491.
[14] Michali, M., A. Emrouznejad A. Dehnokhalaji, and B. Clegg (2021) Noise-pollution efficiency analysis of European railways: A Network DEA model, Transportation Research Part D, 98: 102980.
[15] Färe, R., and S. Grosskopf. (1996). Intertemporal Production Frontiers: With Dynamic DEA. Norwell, MA: Kluwer, Boston, MA.
[16] Jaenicke, E. C. (2000). Testing for intermediate outputs in dynamic DEA models: Accounting for soil capital in rotational crop production and productivity measures. Journal of productivity analysis, 14(3), 247-266.
[17] Emrouznejad, A., and Thanassoulis, E. (2005). A mathematical model for dynamic efficiency using data envelopment analysis. Applied mathematics and computation, 160(2), 363-378.
[18] Tone, K., and Tsutsui, M. (2009). Network DEA: A slacks-based measure approach , European journal of operational research, 197(1), 243-252.
[19] Wang, K., Yu, S., and Zhang, W. (2013). China’s regional energy and environmental efficiency: A DEA window analysis based dynamic evaluation. Mathematical and Computer Modelling, 58(5-6), 1117-1127.
[20] Wang, Z., Feng, C., and Zhang, B. (2014). An empirical analysis of China's energy efficiency from both static and dynamic perspectives. Energy, 74, 322-330.
[21] Khalili-Damghani, K., Tavana, M., Santos-Arteaga, F. J., and Mohtasham, S. (2015). A dynamic multi-stage data envelopment analysis model with application to energy consumption in the cotton industry. Energy Economics, 51, 320-328.
[22] Chen, C. M. (2009). A network-DEA model with new efficiency measures to incorporate the dynamic effect in production networks, European Journal of Operational Research, 194(3), 687-699.
[23] Bogetoft, P., Fare, R., Grosskopf, S., Hayes, K., and Taylor, L. (2009). Dynamic Network: An Illustartion. Journal of the Operations Research Society of Japan, 52(2), 147-162.
[24] Chen, K., and Guan, J., 2012. Measuring the efficiency of China's regional innovation systems: application of network data envelopment analysis (DEA), Regional Studies, 46(3), 355-377.
[25] Fukuyama, H., and Weber, W. L. (2015). Measuring Japanese bank performance: a dynamic network DEA approach, Journal of Productivity Analysis, 44(3), 249-264.
[26] Omrani, H., & Soltanzadeh, E., (2016). Dynamic DEA models with network structure: An application for Iranian airlines, Journal of Air Transport Management, 57, 52-61.
[27] Zhang, B., Luo, Y., & Chiu, Y. H. (2019). Efficiency evaluation of China's high-tech industry with a multi-activity network data envelopment analysis approach. Socio-Economic Planning Sciences, 66, 2-9.
[28] Shao, L., Yu, X., & Feng, C. (2019). Evaluating the eco-efficiency of China's industrial sectors: A two-stage network data envelopment analysis. Journal of environmental management, 247, 551-560.
[29] Salehzadeh, S.J., Hoseini ,S.f., Zareian A. & Nookabadi A.Sh. (2020). A Hybrid Modeling Approach for Measuring Efficiency of Organizations Considering Dynamic Effects. International Journal of Industrial Engineering & Management Science, 7 (2), 36-44.
[30] Alizadeh, R., Beiragh, R. G., Soltanisehat, L., Soltanzadeh, E., & Lund, P. D. (2020). Performance evaluation of complex electricity generation systems: A dynamic network-based data envelopment analysis approach. Energy Economics, 91, 104894.
[31] Lahouel, B. B., Taleb, L., & Kossai, M. (2022). Nonlinearities between bank stability and income diversification: A dynamic network data envelopment analysis approach. Expert Systems With Applications, 207, 117776.
[32] Huang, X.-J., An, R., Yu, M.-M., & He, F.-F. (2022). Tourism efficiency decomposition and assessment of forest parks in China using dynamic network data envelopment analysis. Journal of Cleaner Production, 363, 132405.
[33] Fukuyama, H., Tsionas, M. & , Tan Y. (2023). Dynamic network data envelopment analysis with a sequential structure and behavioural-causal analysis: Application to the Chinese banking industry. European Journal of Operational Research, 307, 1360–1373.
[34] Salehzadeh, S.J., (2010). Using DEA Method for Network Structure Efficiency Measurement with Dynamic Effect. MSc thesis, Isfahan Uiniversity of Thechnology, Isfahan. | ||
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