A technique for identifying congestion in Data Envelopment Analysis
Subject Areas : International Journal of Data Envelopment AnalysisAmin Jabbari 1 , Farhad Hosseinzadeh Lotfi 2 , Mohsen Rostamy Malkhalifeh 3
1 - Department of Mathematics, 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 Mathematics, Science and Research Branch, Islamic Azad University,Tehran, Iran
Keywords: DEA, Congestion, Efficiency, Inefficiency,
Abstract :
Data Envelopment Analysis (DEA) is a non-parametric mathematical programming method used to assess performance and measure the efficiency of Decision-making Units (DMUs) that operate with multiple concurrent inputs and outputs. The performance of these units is influenced by the utilization of input resources. While an increase in input utilization typically leads to higher production levels, there are scenarios where increased input usage results in decreased outputs. This phenomenon is termed congestion. Given that alleviating congestion can reduce costs and enhance production, it holds significant importance in economics. This paper introduces a method for identifying congestion based on a defined modeling framework. A DMU is considered congested when reducing inputs in at least one component leads to increased outputs in at least one component, and increasing inputs in at least one component can be achieved by reducing outputs in at least one component, without improvement in other indicators. The paper explores congestion in DMUs with both increasing and decreasing inputs.
References:
1) Charnes, A., & Cooper, W. W. (1962). Programming with linear fractional functionals. Naval Research Logistics Quarterly, 9(3‐4), 181-186.
2) Grosskopf, S., & Far, G. (2002). The productivity of U.S. states: The role of shocks and congestion. The Review of Economics and Statistics, 84(1), 127-140.
3) Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Data envelopment analysis: History, models, and interpretations. In Handbook on data envelopment analysis (pp. 1-39). Springer.
4) Cooper, W. W., & Park, K. S. (1998). Technical and allocative efficiencies in the US airline industry: 1978-1995. Transportation Research Part E: Logistics and Transportation Review, 34(4), 229-245.
5) Cooper, W. W., Seiford, L. M., & Zhu, J. (2004). A unified additive model approach for evaluating inefficiency and congestion with associated measures in DEA. Socio-Economic Planning Sciences, 38(2-3), 103-124.
6) Brockett, P. L., Charnes, A., Cooper, W. W., & Huang, Z. M. (1996). Congestion in Chinese industries: A DEA model. Socio-Economic Planning Sciences, 30(4), 249-259.
7) Jahanshahloo, G. R., & Khodabakhshi, M. (2005). Input aggregation in DEA and improvement on the firms performances. Applied Mathematics and Computation, 160(3), 623-629.
8) Asgharian, M., Jahanshahloo, G. R., & Khodabakhshi, M. (2006). A new approach for finding targets DMUs in DEA. Applied Mathematics and Computation, 175(1), 68-78.
9) Tone, K., & Sahoo, B. K. (2003). Scale, indivisibilities, and production function in data envelopment analysis. European Journal of Operational Research, 145(1), 85-114.
10) Noura, A., et al., A new method for measuring congestion in data envelopment analysis. SocioEconomic Planning Sciences, 2010. 44(4): p. 240-246.
11) Khoveyni, M., et al., Recognizing strong and weak congestion slack based in data envelopment analysis. Computers & Industrial Engineering, 2013. 64(2): p. 731-738.
12) Khoveyni, M., R. Eslami, and G.-l. Yang, Negative data in DEA: Recognizing congestion and specifying the least and the most congested decision-making units. Computers & Operations Research, 2017. 79: p. 39-48.
13) Sueyoshi, T. and M. Goto, Undesirable congestion under natural disposability and desirable congestion under managerial disposability in US electric power industry measured by DEA environmental assessment. Energy Economics, 2016. 55: p. 173-188.
14) Meng, F., et al., Inefficiency and congestion assessment of mix energy consumption in 16 APEC countries by using DEA window analysis. Energy Procedia, 2014. 61: p. 2518-2523.
15) Mehdiloozad, M., J. Zhu, and B.K. Sahoo, Identification of congestion in data envelopment analysis under the occurrence of multiple projections: A reliable method capable of dealing with negative data. European Journal of Operational Research, 2018. 265(2): p. 644-654.
16) Chen, Z., et al., Undesirable and desirable energy congestion measurements for regional coalfired power generation industry in China. Energy Policy, 2019. 125: p. 122-134.
17) Chen, L., Y.-M. Wang, and L. Wang, Congestion measurement under different policy objectives: an analysis of Chinese industry. Journal of Cleaner Production, 2016. 112: p. 2943-2952.
18) Saati, S., & Shadab, M. (2023). Exploring congestion in intermediate products by DEA: an application on Iranian cement supply chain. Operational Research, 23(4), 60.
19) Shadab, M., Saati, S., Farzipoor Saen, R., Khoveyni, M., & Mostafaee, A. (2021). Detecting congestion in DEA by solving one model. Operations Research and Decisions, 31(1).
