Abstract
This paper studies the application of a genetic algorithm (GA) for determining closest efficient targets in Data Envelopment Analysis. Traditionally, this problem has been solved in the literature through unsatisfactory methods since all of them are related in some sense to a combinatorial NP-hard problem. This paper presents and studies some algorithms to be used in the creation, crossover and mutation of chromosomes in a GA, in order to obtain an efficient metaheuristic which obtains better solutions.
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References
Amirteimoori, A., Kordrostami, S.: A Euclidean distance-based measure of efficiency in data envelopment analysis. Optimization 59, 985–996 (2010)
Aparicio, J., Pastor, J.T.: On how to properly calculate the Euclidean distance-based measure in DEA. Optimization (2012), doi:10.1080/02331934.2012.655692
Aparicio, J., Ruiz, J.L., Sirvent, I.: Closest targets and minimum distance to the Pareto-efficient frontier in DEA. J. Prod. Anal. 28, 209–218 (2007)
Burke, E., Hart, E., Kendall, G., Newall, J., Ross, P., Schulenburg, S.: Hyper-heuristics: An Emerging Direction in Modern Search Technology. In: Handbook of Metaheuristics, vol. 16, pp. 457–474 (2003)
Coelli, T., Rao, D.S.P., Battese, G.E.: An Introduction to Efficiency and Productivity Analysis. Kluwer Academic Publishers, Boston (1998)
Cooper, W.W., Seiford, L.M., Tone, K.: Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software. Kluwer Academic Publishers, Boston (2000)
Jahanshahloo, G.R., Hosseinzadeh Lotfi, F., Zohrehbandian, M.: Finding the piecewise linear frontier production function in data envelopment analysis. Appl. Math. Compt. 163, 483–488 (2005)
Jahanshahloo, G.R., Hosseinzadeh Lotfi, F., Zhiani Rezai, H., Rezai Balf, F.: Finding strong defining hyperplanes of Production Possibility Set. Eur. J. Oper. Res. 177, 42–54 (2007)
Jahanshahloo, G.R., Vakili, J., Mirdehghan, S.M.: Using the minimun distance of DMUs from the frontier of the PPS for evaluating group performance of DMUs in DEA. Asia Pac. J. Oper. Res. 29(2), 1250010-1–1250010-25 (2012a)
Jahanshahloo, G.R., Vakili, J., Zarepisheh, M.: A linear bilevel programming problem for obtaining the closest targets and minimum distance of a unit from the strong efficient frontier. Asia Pac. J. Oper. Res. 29(2), 1250011-1–1250011-19 (2012b)
Mitchell, M.: An Introduction to Genetic Algorithm. MIT Ps (1998)
Pastor, J.T., Ruiz, J.L., Sirvent, I.: An Enhanced DEA Russell Graph Efficiency Measure. Eur. J. Oper. Res. 115, 596–607 (1999)
Pastor, J.T., Aparicio, J.: The relevance of DEA benchmarking information and the Least-Distance Measure: Comment. Math. Comput. Model. 52, 397–399 (2010)
Portela, M.C.A.S., Borges, P.C., Thanassoulis, E.: Finding Closest Targets in Non-Oriented DEA Models: The Case of Convex and Non-Convex Technologies. J. Prod. Anal. 19, 251–269 (2003)
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Martinez-Moreno, R., Lopez-Espin, J.J., Aparicio, J., Pastor, J.T. (2013). Application of Genetic Algorithms to Determine Closest Targets in Data Envelopment Analysis. In: Omatu, S., Neves, J., Rodriguez, J., Paz Santana, J., Gonzalez, S. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-00551-5_14
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DOI: https://doi.org/10.1007/978-3-319-00551-5_14
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