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A model for sector restructuring through genetic algorithm and inverse DEA

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A model for sector restructuring through genetic algorithm and inverse DEA

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dc.contributor.author Guijarro, Francisco es_ES
dc.contributor.author Martínez-Gómez, Mónica es_ES
dc.contributor.author Visbal-Cadavid, Delimiro es_ES
dc.date.accessioned 2021-05-25T03:32:50Z
dc.date.available 2021-05-25T03:32:50Z
dc.date.issued 2020-09-15 es_ES
dc.identifier.issn 0957-4174 es_ES
dc.identifier.uri http://hdl.handle.net/10251/166752
dc.description.abstract [EN] The aim of this study is to devise a sector restructuring model in which all the decision making units (DMUs) satisfy a predefined global efficiency level. The proposal makes several realistic assumptions regarding the merging of DMUs under specific circumstances. The model computes the global efficiency target by giving preference to merging DMUs over saving inputs, hence considering that the affected stakeholders may be resistant to restructuring, and this resistance may have overall negative effects on the image and reputation of the companies and organizations. In addition, the number of constituents in the new entities can be limited by the decision maker after the restructuring process, so that the model also considers a constraint on cardinality. The proposal combines the inverse data envelopment analysis (InvDEA), which computes the merger's input savings, and the genetic algorithm (GA), which solves the combinatorial problem of identifying the merging units. The proposal is illustrated by two examples from banking and higher education. (C) 2020 Elsevier Ltd. All rights reserved. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Expert Systems with Applications es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Mergers es_ES
dc.subject Restructuring es_ES
dc.subject Inverse data envelopment analysis es_ES
dc.subject Genetic algorithm es_ES
dc.subject Cardinality constraint es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.subject.classification ECONOMIA FINANCIERA Y CONTABILIDAD es_ES
dc.title A model for sector restructuring through genetic algorithm and inverse DEA es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.eswa.2020.113422 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Economía y Ciencias Sociales - Departament d'Economia i Ciències Socials es_ES
dc.description.bibliographicCitation Guijarro, F.; Martínez-Gómez, M.; Visbal-Cadavid, D. (2020). A model for sector restructuring through genetic algorithm and inverse DEA. Expert Systems with Applications. 154:1-13. https://doi.org/10.1016/j.eswa.2020.113422 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.eswa.2020.113422 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 13 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 154 es_ES
dc.relation.pasarela S\406612 es_ES
dc.description.references Abbott, M., & Doucouliagos, C. (2003). The efficiency of Australian universities: a data envelopment analysis. Economics of Education Review, 22(1), 89-97. doi:10.1016/s0272-7757(01)00068-1 es_ES
dc.description.references Ahuja, R. K., & Orlin, J. B. (2001). Inverse Optimization. Operations Research, 49(5), 771-783. doi:10.1287/opre.49.5.771.10607 es_ES
dc.description.references Amin, G. R., Al-Muharrami, S., & Toloo, M. (2019). A combined goal programming and inverse DEA method for target setting in mergers. Expert Systems with Applications, 115, 412-417. doi:10.1016/j.eswa.2018.08.018 es_ES
dc.description.references Amin, G. R., Emrouznejad, A., & Gattoufi, S. (2017). Minor and major consolidations in inverse DEA: Definition and determination. Computers & Industrial Engineering, 103, 193-200. doi:10.1016/j.cie.2016.11.029 es_ES
dc.description.references Amin, G. R., Emrouznejad, A., & Gattoufi, S. (2017). Modelling generalized firms’ restructuring using inverse DEA. Journal of Productivity Analysis, 48(1), 51-61. doi:10.1007/s11123-017-0501-y es_ES
dc.description.references Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078-1092. doi:10.1287/mnsc.30.9.1078 es_ES
dc.description.references Beckmann, T., & Forbes, W. (2004). An Examination of Takeovers, Job Loss and the Wage Decline within UK Industry. European Financial Management, 10(1), 141-165. doi:10.1111/j.1468-036x.2004.00243.x es_ES
dc.description.references Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. doi:10.1016/0377-2217(78)90138-8 es_ES
dc.description.references Dentchev, N. A., & Heene, A. (2004). Managing the reputation of restructuring corporations: send the right signal to the right stakeholder. Journal of Public Affairs, 4(1), 56-72. doi:10.1002/pa.171 es_ES
dc.description.references Emrouznejad, A., Yang, G., & Amin, G. R. (2018). A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries. Journal of the Operational Research Society, 70(7), 1079-1090. doi:10.1080/01605682.2018.1489344 es_ES
dc.description.references Fallahpour, A., Olugu, E. U., Musa, S. N., Khezrimotlagh, D., & Wong, K. Y. (2015). An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach. Neural Computing and Applications, 27(3), 707-725. doi:10.1007/s00521-015-1890-3 es_ES
dc.description.references García, F., Guijarro, F., & Moya, I. (2010). Ranking Spanish savings banks: A multicriteria approach. Mathematical and Computer Modelling, 52(7-8), 1058-1065. doi:10.1016/j.mcm.2010.02.015 es_ES
dc.description.references Gattoufi, S., Amin, G. R., & Emrouznejad, A. (2012). A new inverse DEA method for merging banks. IMA Journal of Management Mathematics, 25(1), 73-87. doi:10.1093/imaman/dps027 es_ES
dc.description.references González, M., López-Espín, J. J., Aparicio, J., Giménez, D., & Pastor, J. T. (2015). Using Genetic Algorithms for Maximizing Technical Efficiency in Data Envelopment Analysis. Procedia Computer Science, 51, 374-383. doi:10.1016/j.procs.2015.05.257 es_ES
dc.description.references Gugler, K., & Yurtoglu, B. B. (2004). The effects of mergers on company employment in the USA and Europe. International Journal of Industrial Organization, 22(4), 481-502. doi:10.1016/j.ijindorg.2003.12.003 es_ES
dc.description.references Halkos, G. E., Matousek, R., & Tzeremes, N. G. (2014). Pre-evaluating technical efficiency gains from possible mergers and acquisitions: evidence from Japanese regional banks. Review of Quantitative Finance and Accounting, 46(1), 47-77. doi:10.1007/s11156-014-0461-5 es_ES
dc.description.references Halkos, G. E., & Tzeremes, N. G. (2013). Estimating the degree of operating efficiency gains from a potential bank merger and acquisition: A DEA bootstrapped approach. Journal of Banking & Finance, 37(5), 1658-1668. doi:10.1016/j.jbankfin.2012.12.009 es_ES
dc.description.references Hsu, C.-M. (2013). An integrated portfolio optimisation procedure based on data envelopment analysis, artificial bee colony algorithm and genetic programming. International Journal of Systems Science, 45(12), 2645-2664. doi:10.1080/00207721.2013.775388 es_ES
dc.description.references Jain, V., Kumar, A., Kumar, S., & Chandra, C. (2015). Weight restrictions in Data Envelopment Analysis: A comprehensive Genetic Algorithm based approach for incorporating value judgments. Expert Systems with Applications, 42(3), 1503-1512. doi:10.1016/j.eswa.2014.09.034 es_ES
dc.description.references Kao, H.-Y., Chan, C.-Y., & Wu, D.-J. (2014). A multi-objective programming method for solving network DEA. Applied Soft Computing, 24, 406-413. doi:10.1016/j.asoc.2014.06.057 es_ES
dc.description.references Kohers, T., Huang, M., & Kohers, N. (2000). Market perception of efficiency in bank holding company mergers: the roles of the DEA and SFA models in capturing merger potential. Review of Financial Economics, 9(2), 101-120. doi:10.1016/s1058-3300(00)00019-7 es_ES
dc.description.references Kuah, C. T., Wong, K. Y., & Wong, W. P. (2012). Monte Carlo Data Envelopment Analysis with Genetic Algorithm for Knowledge Management performance measurement. Expert Systems with Applications, 39(10), 9348-9358. doi:10.1016/j.eswa.2012.02.140 es_ES
dc.description.references Kubo, K., & Saito, T. (2012). The effect of mergers on employment and wages: Evidence from Japan. Journal of the Japanese and International Economies, 26(2), 263-284. doi:10.1016/j.jjie.2011.04.001 es_ES
dc.description.references Lin, R.-C., Sir, M. Y., & Pasupathy, K. S. (2013). Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: Specific application to determining optimal resource levels in surgical services. Omega, 41(5), 881-892. doi:10.1016/j.omega.2012.11.003 es_ES
dc.description.references Lozano, S., & Villa, G. (2010). DEA-based pre-merger planning tool. Journal of the Operational Research Society, 61(10), 1485-1497. doi:10.1057/jors.2009.106 es_ES
dc.description.references Nazarko, J., & Šaparauskas, J. (2014). APPLICATION OF DEA METHOD IN EFFICIENCY EVALUATION OF PUBLIC HIGHER EDUCATION INSTITUTIONS. Technological and Economic Development of Economy, 20(1), 25-44. doi:10.3846/20294913.2014.837116 es_ES
dc.description.references Pendharkar, P. C. (2002). A potential use of data envelopment analysis for the inverse classification problem. Omega, 30(3), 243-248. doi:10.1016/s0305-0483(02)00030-0 es_ES
dc.description.references Pendharkar, P. C. (2017). A hybrid genetic algorithm and DEA approach for multi-criteria fixed cost allocation. Soft Computing, 22(22), 7315-7324. doi:10.1007/s00500-017-2605-8 es_ES
dc.description.references Radojicic, M., Savic, G., & Jeremic, V. (2018). MEASURING THE EFFICIENCY OF BANKS: THE BOOTSTRAPPED I-DISTANCE GAR DEA APPROACH. Technological and Economic Development of Economy, 24(4), 1581-1605. doi:10.3846/tede.2018.3699 es_ES
dc.description.references Toloo, M. (2014). An epsilon-free approach for finding the most efficient unit in DEA. Applied Mathematical Modelling, 38(13), 3182-3192. doi:10.1016/j.apm.2013.11.028 es_ES
dc.description.references Toloo, M. (2014). The role of non-Archimedean epsilon in finding the most efficient unit: With an application of professional tennis players. Applied Mathematical Modelling, 38(21-22), 5334-5346. doi:10.1016/j.apm.2014.04.010 es_ES
dc.description.references Toloo, M., & Mensah, E. K. (2019). Robust optimization with nonnegative decision variables: A DEA approach. Computers & Industrial Engineering, 127, 313-325. doi:10.1016/j.cie.2018.10.006 es_ES
dc.description.references Tsolas, I. E., & Charles, V. (2015). Incorporating risk into bank efficiency: A satisficing DEA approach to assess the Greek banking crisis. Expert Systems with Applications, 42(7), 3491-3500. doi:10.1016/j.eswa.2014.12.033 es_ES
dc.description.references Udhayakumar, A., Charles, V., & Kumar, M. (2011). Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems. Omega, 39(4), 387-397. doi:10.1016/j.omega.2010.09.002 es_ES
dc.description.references Visbal-Cadavid, D., Martínez-Gómez, M., & Guijarro, F. (2017). Assessing the Efficiency of Public Universities through DEA. A Case Study. Sustainability, 9(8), 1416. doi:10.3390/su9081416 es_ES
dc.description.references Wanke, P., & Barros, C. (2014). Two-stage DEA: An application to major Brazilian banks. Expert Systems with Applications, 41(5), 2337-2344. doi:10.1016/j.eswa.2013.09.031 es_ES
dc.description.references Wei, Q., Zhang, J., & Zhang, X. (2000). An inverse DEA model for inputs/outputs estimate. European Journal of Operational Research, 121(1), 151-163. doi:10.1016/s0377-2217(99)00007-7 es_ES
dc.description.references Zhang, G., Gao, L., & Shi, Y. (2011). An effective genetic algorithm for the flexible job-shop scheduling problem. Expert Systems with Applications, 38(4), 3563-3573. doi:10.1016/j.eswa.2010.08.145 es_ES


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