Measuring congestion in data envelopment analysis without solving any models

Document Type : Article

Authors

Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract

One of the important topics in Data Envelopment Analysis is congestion. Many scholars research in this field and represent their methods. In most of the represented methods, we have to solve lots of models or its used for a special aim like negative data, integer data, different Production Possibility Set and etc. Here we represent our method that measures the congestion without solving a model. It can be used for different Production Possibility Set (different technology) like T_{New}\ and \ FDH; different data like negative data and integer data. Also, we can distinguish strongly or weakly congestion of Decision Making Unit. Furthermore, each DMU has congestion, efficient and inefficient, we can measure it by this method. Finally, we represent some numerical example of our purpose method and compare our method with other methods then show the results in tables.

Keywords

References

References
[1] Abbasi M, Jahanshahloo GR, Rostamy-Malkhlifeh M, Hosseinzadeh Lotfi F, (2014). Estimation of Congestion in Free Disposal Hull Models Using Data Envelopment Analysis. The Scientific World Journal, Article ID 427673, 8 pages.
[2] Banker RD, Charnes A, Coopper WW, (1984). Some models for estimating technical and scale inefficiencies in Data Envelopment Analysis. Journal of Management Science, 30:1078-1092.
[3] Brockett PL, Cooper WW, Shin HC, Wang Y, (1998). Inefficiency and congestion in Chinese production before and after the 1978 economic reforms. Socio-Economic Planning Sciences, 32:1-20.
[4] Charnes A, Cooper W.W, Rhodes E, (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2:429–444.
[5] Cooper WW, Deng H, Huang ZM, Li SX (2002). A one-model approach to congestion in data envelopment analysis. Socio-Economic Planning Sciences, 36:231_8.
[6] Cooper WW, Deng H, Seiford LM, Zhu J, (2004). Congestion: its identification and management with DEA. In: Cooper WW, Seiford LM, Zhu J, editors. Handbook of data envelopment analysis. Boston: Kluwer; p. 177e201.
[7] Cooper WW, Seiford LM, Zhu J (2000). A unified additive model approach for evaluating inefficiency and congestion whit associated measures in DEA. Socio-Economic Planning Sciences, 34:1_25.
[8] Ebrahimzade Adimi M, Rostamy-Malkhalifeh M, Hosseinzadeh Lotfi F, Mehrjoo R, (2019). A new linear method to find the congestion hyperplane in DEA. Mathematical Sciences, 13:43_52.
 [9] Färe R, Grosskopf S, (1983). Measuring congestion in production. Journal of Economics, 43:257e71.
[10] Färe R, Grosskopf S, (2001). When can slacks be used to identify congestion. An answer to W. W. Cooper, L. Seiford, J. Zhu. Socio-Economic Planning Sciences, 35:1e10.
[11] Hu JL, Chang MC, Tsay HW, (2017). The congestion total-factor energy efficiency of regions in Taiwan. Energy Policy, 110: 710_718.
[12] Khodabakhshi M, (2009). A one-model approach based on relaxed combinations of inputs for evaluating input congestion in DEA. Journal of Computational and Applied Mathematics, 230:443_450.
[13] Khoveynia M, Eslamib R, Yang GL, (2017). Negative data in DEA: Recognizing congestion and specifying the least and the most congested decision making units. Computers & Operations Research, 79:39_48.
[14] Mollaeian E, Rostamy-Malkhalifeh M (2014). Detect and eliminate congestion of the intermediate products in supply chain. International Journal of Data Envelopment Analysis, 2(2): Article ID IJDEA-00225, 7 pages.
[15] Noura AA, Hosseinzadeh Lotfi F, Jahanshahloo GR, Fanati Rashidi S, Barnett R. Parker, (2010). A new method for measuring congestion in data envelopment analysis. Socio-Economic Planning Sciences, 44: 240_246.
[16] Sole-Ribalta A, Arenas A, Gomez S, (2019). Effect of shortest path multiplicity on congestion of multiplex networks. New journal of physics, 21: Open access.
[17] Sueyoshi T, Goto M, (2016). Undesirable congestion under natural disposability and desirable congestion under managerial disposability in U.S. electric power industry measured by DEA environmental assessment. Energy Economics, 55: 173_188.
[18] Sueyoshi T, Goto M, (2016). Returns to damage under undesirable congestion and damages to return under desirable congestion measured by DEA environmental assessment with multiplier restriction: Economic and energy planning for social sustainability in China. Energy Economics, 56: 288_309.
[19] Wang Q, Wan J, YuanY, (2018). Locality constraint distance metric learning for traffic congestion detection. Pattern Recognition, 75:272_281.
[20] Zare-Haghighi H, Rostamy-Malkhalifeh M, Jahanshahloo GR, (2014). Measurement of Congestion in the Simultaneous Presence of Desirable and Undesirable Outputs. Journal of Applied Mathematics, Article ID 512157, 9 pages.
[21] Zhou DQ, Meng FY, Bai Y, Cai SQ, (2017). Energy efficiency and congestion assessment with energy mix effect: The case of APEC countries. Journal of Cleaner Production, 142:819_828.
[22] Zhou P, Wu F, Zhou DQ, (2017). Total-factor energy efficiency with congestion. Annals of Operations Research, 255:241_256.
Scientia Iranica
Volume 28, Issue 5
Transactions on Industrial Engineering (E)
September and October 2021
Pages 2926-2932

Files

Share

How to cite

Statistics