Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078–1092.
Article
Google Scholar
Banker, R. D., & Slaughter, S. A. (1997). A field study of scale economies in software maintenance. Management Science, 43, 1709–1725.
Article
Google Scholar
Berkelaar, M., et al. (2007). lpSolve: Interface to Lp solve v. 5.5 to solve linear or integer programs. R Package Version 5.
Bogetoft, P., & Otto, L. (2011). Benchmarking with DEA, SFA, and R. New York: Springer.
Book
Google Scholar
Borchers, S. T., & H.W. (2014). CRAN task view: Optimization and mathematical programming.
Canty, A., & Ripley, B. (2012). boot: Bootstrap R (S-Plus) functions. R Package Version 1.
Charnes, A., Cooper, W. W., Golany, B., Seiford, L., & Stutz, J. (1985). Foundations of data envelopment analysis for Pareto–Koopmans efficient empirical production functions. Journal of Economics, 30, 91–107.
Article
Google Scholar
Chidamber, S. R., & Kemerer, C. F. (1994). A metrics suite for object oriented design. IEEE Transactions on Software Engineering, 20, 476–493. https://doi.org/10.1109/32.295895.
Article
Google Scholar
Emrouznejad, A., Rostami-Tabar, B., & Petridis, K. (2016). A novel ranking procedure for forecasting approaches using data envelopment analysis. Technological Forecasting and Social Change, 111, 235–243.
Article
Google Scholar
Emrouznejad, A., & Thanassoulis, E. (2010). Measurement of productivity index with dynamic DEA. International Journal of Operational Research, 8, 247–260.
Article
Google Scholar
Färe, R., & Grosskopf, S. (1996). Dynamic production models. In Intertemporal production frontiers: With dynamic DEA (pp. 151–188). New York: Springer.
Geyer, C. J., & Meeden, G. D. (2008). R package rcdd (C double description for R), version 1.1. Incorporates code from (4). http://www.stat.umn.edu/geyer/rcdd.
Grigoroudis, E., Petridis, K., & Arabatzis, G. (2014). RDEA: A recursive DEA based algorithm for the optimal design of biomass supply chain networks. Renewable Energy, 71, 113–122.
Article
Google Scholar
Hayfield, T., & Racine, J. S. (2008). Nonparametric econometrics: The np package. Journal of Statistical Software, 27, 1–32.
Article
Google Scholar
Henningsen, A. (2010). linprog: Linear programming. Optim. R Package Version 09-0.
Hornik, K., Meyer, D., & Theussl, S. (2013). ROI: R optimization infrastructure.
Inman, O. L., Anderson, T. R., & Harmon, R. R. (2006). Predicting US jet fighter aircraft introductions from 1944 to 1982: A dogfight between regression and TFDEA. Technological Forecasting and Social Change, 73, 1178–1187. https://doi.org/10.1016/j.techfore.2006.05.013.
Article
Google Scholar
Karatzoglou, A., Smola, A., Hornik, K., & Zeileis, A. (2005). kernlab—Kernel methods. R Package Version 06-2 URL HttpCRAN R-Proj. Org.
Konis, K., et al. (2011). lpSolveAPI: R Interface for lp_solve Version 5.5. 2.0. R package version 5.5. 2.0-5.
Lang, M. L., & D.T. (2014). RGtk2: R bindings for Gtk 2.8.0 and above.
Nemoto, J., & Goto, M. (2003). Measurement of dynamic efficiency in production: An application of data envelopment analysis to Japanese electric utilities. Journal of Product Analysis, 19, 191–210.
Article
Google Scholar
Oh, D., & Oh, M. D. (2013). Package ‘nonparaeff’.
Ouellette, P., & Vierstraete, V. (2004). Technological change and efficiency in the presence of quasi-fixed inputs: A DEA application to the hospital sector. European Journal of Operational Research, 154, 755–763. https://doi.org/10.1016/S0377-2217(02)00712-9.
Article
Google Scholar
Petridis, K., Chatzigeorgiou, A., & Stiakakis, E. (2016). A spatiotemporal Data Envelopment Analysis (ST DEA) approach: The need to assess evolving units. Annals of Operations Research, 238(1–2), 475–496.
Article
Google Scholar
Petridis, K., Dey, P. K., & Emrouznejad, A. (2017). A branch and efficiency algorithm for the optimal design of supply chain networks. Annals of Operations Research, 253, 545–571.
Article
Google Scholar
Prior, D. (2006). Efficiency and total quality management in health care organizations: A dynamic frontier approach. Annals of Operations Research, 145, 281–299.
Article
Google Scholar
Rudy, J., Rudy, M. J., Liu, S., & Matrix, D., n.d. Package ‘CLSOCP’.
Shott, T., & Lim, D.-J. (2015). TFDEA: Technology forecasting using DEA.
Simm, J., Besstremyannaya, G., & Simm, M. J. (2014). Package ‘rDEA’.
Soetaert, K., Van den Meersche, K., & van Oevelen, D. (2009). limSolve: Solving linear inverse models. R Package Version 1.
Team, R. C., et al. (2012). R: A language and environment for statistical computing.
The R Project for Statistical Computing (WWW Document), n.d. http://www.r-project.org/. Accessed 26 February 15.
Turlach, B. A., & Weingessel, A. (2010). quadprog: Functions to solve quadratic programming problems. R Package Version 1.5-3.
Varadhan, R., & Gilbert, P. (2009). BB: An R package for solving a large system of nonlinear equations and for optimizing a high-dimensional nonlinear objective function. Journal of Statistical Software, 32, 1–26.
Article
Google Scholar
Wilson, P. W. (2008). FEAR: A software package for frontier efficiency analysis with R. Socio-Economic Planning Sciences, 42, 247–254.
Article
Google Scholar