Eliminating congestion of decision-making units using inverse data envelopment analysis

[1] Amin, G. R., Emrouznejad, A., Gattou , S., (2017). Minor and major consolidations in inverse dea: De nition and determination. Computers Industrial Engineering 103, 193-200. https://doi.org/10.1016/j.cie.2016.11.029

[2] Amin, G. R., Emrouznejad, A., Gattou , S., (2017). Modelling generalized  rms' restructuring using inverse dea. Journal of Productivity Analysis 48, 51-61. https://doi.org/10.1007/s11123-017-0501-y

[3] Amin, G. R., Qukil, A., (2019). Flexible target setting in mergers using inverse data envelopment analysis. International Journal of Operational Research 35, 301-317. https://doi.org/10.1504/ijor.2019.10022710

[4] Asgharian, M., Khodabakhshi, M., Neralic, L., (2010). Congestion in stochastic data envelopment analysis: an input relaxation approach. International Journal of Statistics and Management System 5 (1), 84-106.

[5] Avkiran, N., (1999). The evidence on eciency gains: the role of mergers and the bene ts to the public. Journal of Banking & Finance 23 (7), 991-1013. https://doi.org/10.1016/S0378-4266(98)00129-0

[6] Banker, R. D., Charnes, A., Cooper, W. W., (1984). Some models for estimating technical and scale eciencies in data envelopment analysis. Management Science 30, 1078-1092. http://www.jstor.org/stable/2631725

[7] Bogetoft, P., Strange, N., Thorsen, B., (2003). Eciency and merger gains in the danish forestry extension service. Forest Science 49 (4), 585-595. https://doi.org/10.1093/forestscience/49.4.585

[8] Bogetoft, P., Wang, D., (2005). Estimating the potential gains from mergers. Journal of Productivity Analysis 23, 145-171. https://doi.10.22004/ag.econ.24213

[9] Brockett, P. L., Cooper, W. W., Shin, H. C.,Wang, Y., (1998). Ineciency and congestion in chinese production before and after the 1978 economic reforms. Socio- Economic planning sciences 32, 1-20. https://doi.org/10.1016/S0038-0121(97)00020-7

[10] Charnes, A., Cooper, W. W., Rhodes, E., (1978). Measuring the eciency of decision making units. European Journal of Operational Research 2, 429-444. https://doi.org/10.1016/0377-2217(78)90138-8

[11] Chen, L., Wang, Y., Lai, F., Feng, F., (2017). An investment analysis for china's sustainable development based on inverse data envelopment analysis. Journal of Cleaner Production 142 (4), 1638-1649. https://doi.org/10.1016/j.jclepro.2016.11.129

[12] Cooper, W. W., Deng, H., Huang, Z. M., Li, S. X., (2002). One model approach to congestion in data envelopment analysis. Socio-Economic planning sciences 36, 231-238. https://doi.org/10.1016/S0038-0121(02)00008-3

[13] Cooper, W. W., Thompson, R. G., Thrall, R. M., (1996). Introduction: extensions and new developments in dea. Annals of operations research 66, 3-45. https://doi.org/10.1007/BF02125451

[14] De Witte, K., Dijkgraaf, E., (2010). Mean and bold? on separating merger economies from structural eciency gains in the drinking water sector. Journal of the Operational Research Society 61 (2), 222-234. https://doi.org/10.1057/jors.2008.129

[15] Ebrahimzadeh Adimi, M., Rostamy-Malkhalifeh, M., Hossienzadeh lot , F., Mehrjoo, R., (2019). A new linear method to  nd the congestion hyperplane in dea. Mathematical Sciences 13, 43-52. https://doi.org/10.1007/s40096-019-0277-5

[16] Ehrgott, M., (2005). Multicriteria optimization. Springer Berlin.

[17] Emrouznejad, A., Amin, G. R., Ghiyasi, M., Michali, M., (2023). A review of inverse data envelopment analysis: origins, development and future directions. IMA Journal of Management Mathematics 34 (3), 421-440. https://doi.org/10.1093/imaman/dpad006

[18] Fang, L., (2015). Congestion measurement in nonparametric analysis under the weakly disposable technology. European Journal of Operational Research 245, 203-208. https://doi.org/10.1016/j.ejor.2015.03.001

[19] Fare, R., Grosskopf, S., (1983). Measuring congestion in production. Zeitschr. f. Nationalokonomie 43 (3), 257-271. https://doi.org/10.1007/BF01283574

[20] Fare, R., Grosskopf, S., Lovel, C. A. K., (1985). The measurement of eciency of production. Kluwer-Nijho  Publishing, Boston.

[21] Fare, R., Svensson, L., (1980). Congestion of production factors. The Econometric Society 48, 1745-1752.

[22] Fuentes, R., Bellver-Domingo, A., Hernanndez-Chover, V., Hernanndez- Sancho, F., (2020). Identi cation and correction of congestion in wastewater treatment plants in the community of valencia, spain. Zeitschr. f. Nationalokonomie 27, 15729-15742. https://doi.org/10.1007/s11356-020-08063-1

[23] Gattou , S., Amin, G. R., Emrouznejad, A., (2014). A new inverse dea method for merging banks. IMA Journal of Management Mathematics 25, 73-87. https://doi.org/10.1093/imaman/dps027

[24] Ghobadi, S., (2021). Merging decision-making units with interval data. RAIROOperations Research 55, 1605-1630. https://doi.org/10.1051/ro/2020029

[25] Gholoom, S., Zervopoulos, P., (2023). The e ect of mergers and acquisitions on eciency: evidence from the pharmaceutical industry. Advances in Science and Technology 129, 77-95. https://doi.org/10.4028/p-qLBAB6

[26] Hadi-Vencheh, A., Foroughi, A. A., (2006). A generalized dea model for inputs/outputs estimation. Mathematical and Computer Modelling 43 (5-6), 447-457. https://doi.org/10.1016/j.mcm.2005.08.005

[27] Hajaji, H., Youse , S., Sean, R. F., Hassanzadeh, A., (2021). Recommending investment opportunities given congestion by adaptive network data envelopment analysis model: assessing sustainability of supply chains. RAIRO - Operations Research 55, 21-49. https://doi.org/10.1051/ro/2019059

[28] Halkos, G., Tzeremes, N., (2013). Estimating the degree of operating eciency gains from a potential bank merger and acquisition: a dea bootstrapped approach. Journal of Banking & Finance 37 (5), 1658-1668. https://doi.org/10.1016/j.jbank n.2012.12.009

[29] Jahanshahloo, G. R., khodabakhshi, M., (2004). Suitable combination of input for improving outputs in dea with determining input congestion: considering textile industry of china. Applied Mathematics and Computation 151 (1), 263-273. https://doi.org/10.1016/S0096-3003(03)00337-0

[30] Jahanshahloo, G. R., Lot , F. H., Shoja, N., Tohidi, G., Razavyan, S., (2004). Input estimation and identi cation of extra inputs in inverse dea models. Applied Mathematics and Computation 156 (2), 427-437. https://doi.org/10.1016/j.amc.2003.08.001

[31] Jahanshahloo, G. R., Soleimani-damaneh, M., Ghobadi, S., (2015). Inverse dea under inter-temporal dependence using multiple-objective programming. European Journal of Operational Research 240, 447-456. https://doi.org/10.1016/j.ejor.2014.07.002

[32] Kao, C., (2010). Congestion measurement and elimination under the framework of data envelopment analysis. International Journal of Production Economics 123 (2), 257-265. https://doi.org/10.1016/j.ijpe.2009.06.044

[33] Kheirollahi, H., Hessari, P., (2017). An input relaxation model for evaluating congestion in fuzzy dea. Croatian Operational Research Review 8 (2), 391-408. DOI: 10.17535/crorr.2017.0025

[34] Khezri, S., Dehnokhalaji, A., Hosseinzadeh Lot , F., (2021). A full investigation of the directional congestion in data envelopment analysis. RAIRO-Operations Research 55, 571-591. https://doi.org/10.1051/ro/2019092

[35] Khoveyni, M., Eslami, R., Yang, G., (2017). Negative data in dea: Recognizing congestion and specifying the least and the most congested decision making units. Computers & Operations Research 79, 39-48. https://doi.org/10.1016/j.cor.2016.09.002

[36] Khoveyni, M., Eslami, R., khodabakhshi, M., Jahanshahloo, G. R., Hosseinzadeh Lot , F., (2013). Recognizing strong and weak congestion slack based in data envelopment analysis. Computers & Industrial Engineering 64 (2), 731-738. DOI:
10.1016/j.cie.2012.11.014

[37] Kong, C., Chow, W., Ka, M., Fung, Y., (2012). Measuring the e ects of china's airline mergers on the productivity of state-owned carriers. Journal of Air Transport Management 25, 1-4. DOI: 10.1016/j.jairtraman.2011.08.006

[38] Mehdiloozad, M., Zhu, J., Sahoo, B., (2018). Identi cation 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 265 (2), 644-654. DOI: 10.1016/j.ejor.2017.07.065

[39] Oukil, A., (2022). Investigating prospective gains from mergers in the agricultural sector through inverse dea. IMA Journal of Management Mathematics 34 (3), 465-490. DOI:10.1093/imaman/dpac004

[40] Ren, X. T., Fukuyama, H., Yang, G., (2022). Eliminating congestion by increasing inputs in r&d activities of chinese universities. Omega 110, 102618. https://doi.org/10.1016/j.omega.2022.102618

[41] Shadab, M., Saati, S., Farzipoor Saen, R., Khoveyni, M., Mostafaee, A., (2021). Measuring congestion in sustainable supply chain based on data envelopment analysis. Neural Computing and Applications 33, 12477-12491. https://doi.org/10.1007/s00521-021-05889-9

[42] Shahsavan, T., Sanei, M., Tohidi, G., Lot , F. H., Ghobadi, S., (2022). A new method of determining decision-making unit congestion under inter-temporal dependence. Soft Comput 26, 2063-2073. https://doi.org/10.1007/s00500-021-06566-8

[43] Shahsavan, T., Sanei, M., Tohidi, G., Lot , F. H., Ghobadi, S., (2023). Determining the amount of the excess input and the output shortage of the congested decision-making units with negative data. Mathematical Sciences 17, https://doi.org/10.1007/s40096-023-00511-6.

[44] Sharma, M. J., Song Jin, Y., (2013). Multi-stage data envelopment analysis congestion model. Operational Research 13, 399-413. https://doi.org/10.1007/s12351-012-0128-8

[45] Shiri Daryani, Z., Tohidi, G., Daneshian, B., Razavyan, S., Hosseinzadeh Lot , F., (2023). Inverse data envelopment analysis for merging two-stage network systems. International Journal of Applied Operational Research 11 (4), 39-49. http://ijorlu.liau.ac.ir/article-1-647-en

[46] Soltanifar, M., Ghiyasi, M., Shara , H., (2023). Inverse dea-r models for merger analysis with negative data. IMA Journal of Management Mathematics 34 (3), 491-510. https://doi.org/10.1093/imaman/dpac001

[47] Sueyoshi, T., Sekitani, K., (2009). Dea congestion and returns to scale under an occurrence of multiple optimal projections. European Journal of Operational Research 194 (2), 592-607. https://ideas.repec.org/a/eee/ejores/v194y2009i2p592-607

[48] Tone, K., Sahoo, B. K., (2004). Degree of scale economies and congestion: A uni ed dea approach. European Journal of Operational Research 158 (3), 755-772. https://doi.org/10.1016/S0377-2217(03)00370-9

[49] Wang, Z., Wu, X., Lo, K., Mi, J., (2021). Assessing the management eciency of shipping company from a congestion perspective: A case study of hapag-lloyd. Ocean Coast Manag 209, 4-23. DOI: 10.1016/j.ocecoaman.2021.105617

[50] Wei, Q. L., Yan, H., (2004). Congestion and returns to scale in data envelopment analysis. European Journal of Operational Research 153 (3), 641-660. DOI: 10.1016/S0377-2217(02)00799-3

[51] Wei, Q. L., Zhang, J. Z., Zhang, X. S., (2000). An inverse dea model for inputs/outputs estimate. European Journal of Operational Research 121 (1), 151-163. https://doi.org/10.1016/S0377-2217(99)00007-7

[52] Wu, D., Birge, J.,(2011). Serial chain merger evaluation model and application to mortgage banking. Decision Sciences 43 (1), 5-360. https://doi.org/10.1111/j.1540-5915.2011.00340.x

[53] Yang, G., Ren, X., Khoveyni, M., Eslami, R., (2020). Directional congestion in the framework of data envelopment analysis. Journal of Management Science and Engineering 5 (1), 57-75. https://doi.org/10.1016/j.jmse.2020.02.001

[54] Yang, Z., Shi, Y., Yan, H., (2017). Analysis on pure e-commerce congestion e ect, productivity e ect and pro tability in China. Socio-Economic Planning Sciences 57, 35-49. DOI: 10.1016/j.seps.2016.08.002

[55] Zenodin, E., Ghobadi, S., (2020). Merging decision-making units under intertemporal dependence. IMA Journal of Management Mathematics 31 (2), 139-16. https://doi.org/10.1093/imaman/dpz005

[56] Zhang, Y. J., Liu, J. Y., Su, B., (2020). Carbon congestion e ects in china's industry: Evidence from provincial and sectoral levels. Energy Economics 86, 104635. DOI:10.1016/j.eneco.2019.104635