Data driven approach for weight restricted data envelopment analysis models with single output

Year 2023, Volume: 7 Issue: 2, 1768 - 1779, 29.12.2023

Şenol Kurt Mustafa Kerem Yüksel Burcu Dinçergök

https://doi.org/10.56554/jtom.1333333

Abstract

This study aims to explore whether a machine learning algorithm can be used to make improvements in assessing unit efficiencies via a data envelopment analysis (DEA) model. In this study, a DEA model is used to calculate the efficiency scores of Desicion Making Units (DMUs). Then, an ML algorithm is trained that aims to predict the single output using inputs. Ranking of input features based on relative feature importance values obtained from the trained ML model is fed to the DEA model as weight restrictions. As a result, the two DEA models are compared with each other. ML-based insights (feature importance ranking) improve the DEA model in the direction of fewer zero weights. The additional weight restrictions are data depdendent, and hence realistic. As a novel approach, this study proposes the use of machine learning-based feature importance values to overcome a limitation of a DEA model.

Keywords

DEA, ML, ANN

References

• Appiahene, P., Missah, Y. M., & Najim, U. (2020). Predicting bank operational efficiency using machine learning algorithm: comparative study of decision tree, random forest, and neural networks. Advances in fuzzy systems, 2020, 1-12. doi: https://doi.org/10.1155/2020/8581202
• Adler, A. I., & Painsky, A. (2022). Feature importance in gradient boosting trees with cross-validation feature selection. Entropy, 24(5), 687. doi: https://doi.org/10.3390/e24050687
• Atici, K. B. (2012). Using data envelopment analysis for the efficiency and elasticity evaluation of agricultural farms (Doctoral dissertation, University of Warwick). Retrieved from: https://wrap.warwick.ac.uk/54354/2/WRAP_THESIS_Atici_2012.pdf
• Aydin, N., & Yurdakul, G. (2020). Assessing countries’ performances against COVID-19 via WSIDEA and machine learning algorithms. Applied Soft Computing, 97, 106792. doi: https://doi.org/10.1016/j.asoc.2020.106792
• 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: https://doi.org/10.1287/mnsc.30.9.1078
• 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: https://doi.org/10.1016/0377-2217(78)90138-8 Charnes, A., Cooper, W. W., & Rhodes, E. (1981). Evaluating program and managerial efficiency: an application of data envelopment analysis to program follow through. Management science, 27(6), 668-697. doi: https://doi.org/10.1287/mnsc.27.6.668
• Chen, Y., Tsionas, M. G., & Zelenyuk, V. (2021). LASSO + DEA for small and big wide data. Omega, 102, 102419. doi: https://doi.org/10.1016/j.omega.2021.102419
• Cooper, W. W., Seiford, L. M., & Zhu, J. (Eds.). (2011). Handbook on data envelopment analysis (2nd ed.). Springer.
• De La Hoz, E., Zuluaga, R., & Mendoza, A. (2021). Assessing and Classification of Academic Efficiency in Engineering Teaching Programs. Journal on Efficiency and Responsibility in Education and Science, 14(1), 41- 52. Retrieved from: https://hdl.handle.net/20.500.12834/880
• Emrouznejad, A., & Shale, E. (2009). A combined neural network and DEA for measuring efficiency of large scale datasets. Computers & Industrial Engineering, 56(1), 249-254. doi: https://doi.org/10.1016/j.cie.2008.05.012
• Farahmand, M., Desa, M. I., & Nilashi, M. (2014). A combined data envelopment analysis and support vector regression for efficiency evaluation of large decision making units. International journal of engineering and technology (IJET), 2310-2321. Retrieved from: https://www.researchgate.net/publication/288995583_A_Combined_Data_Envelopment_Analysis_and_Support _Vector_Regression_for_Efficiency_Evaluation_of_Large_Decision_Making_Units Førsund, F. R. (2013). Weight restrictions in DEA: misplaced emphasis?. Journal of Productivity Analysis, 40, 271-283. Retrieved from: https://link.springer.com/article/10.1007/s11123-012-0296-9
• Gupta, A., Kohli, M., & Malhotra, N. (2016, July). Classification based on Data Envelopment Analysis and supervised learning: A case study on energy performance of residential buildings. In 2016 IEEE 1st international conference on power electronics, intelligent control and energy systems (ICPEICES) (pp. 1-5). IEEE. doi: 10.1109/ICPEICES.2016.7853706
• Ghiyasi, M., Rouyendegh, B. D., & Özdemir, Y. S. (2021). Local and global energy efficiency analysis for energy production based on multi-plant generalized production technology. IEEE Access, 9, 58208-58215. doi: 10.1109/ACCESS.2021.3072493
• Hong, H. K., Ha, S. H., Shin, C. K., Park, S. C., & Kim, S. H. (1999). Evaluating the efficiency of system integration projects using data envelopment analysis (DEA) and machine learning. Expert Systems with Applications, 16(3), 283-296. doi: https://doi.org/10.1016/S0957-4174(98)00077-3
• Jomthanachai, S., Wong, W. P., & Lim, C. P. (2021). An Application of Data Envelopment Analysis and Machine Learning Approach to Risk Management. IEEE Access, 9, 85978-85994. doi: 10.1109/ACCESS.2021.3087623
• Kheirkhah, A., Azadeh, A., Saberi, M., Azaron, A., & Shakouri, H. (2013). Improved estimation of electricity demand function by using of artificial neural network, principal component analysis and data envelopment analysis. Computers & Industrial Engineering, 64(1), 425-441. doi: https://doi.org/10.1016/j.cie.2012.09.017
• Khezrimotlagh, D., Zhu, J., Cook, W. D., & Toloo, M. (2019). Data envelopment analysis and big data. European Journal of Operational Research, 274(3), 1047-1054. doi: https://doi.org/10.1016/j.ejor.2018.10.044
• Kongar, E., & Adebayo, O. (2021). Impact of Social Media Marketing on Business Performance: A Hybrid Performance Measurement Approach Using Data Analytics and Machine Learning. IEEE Engineering Management Review, 49(1), 133-147. doi: 10.1109/EMR.2021.3055036
• Koronakos, G., & Sotiropoulos, D. N. (2020, July). A Neural Network approach for Non-parametric Performance Assessment. In 2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA) (pp. 1-8). IEEE. doi: 10.1109/IISA50023.2020.9284346
• Kotsiantis, S. B. (2013). Decision trees: a recent overview. Artificial Intelligence Review, 39, 261-283. doi: Retrieved from: https://link.springer.com/article/10.1007/s10462-011-9272-4
• Kumar, A., Shrivastav, S. K., & Mukherjee, K. (2022). Performance evaluation of Indian banks using feature selection data envelopment analysis: A machine learning perspective. Journal of Public Affairs, e2686. doi: https://doi.org/10.1002/pa.2686
• Lin, S. J. (2021). Integrated artificial intelligence and visualization technique for enhanced management decision in today’s turbulent business environments. Cybernetics and Systems, 52(4), 274-292. doi: https://doi.org/10.1080/01969722.2021.1881244
• Mirmozaffari, M., Shadkam, E., Khalili, S. M., Kabirifar, K., Yazdani, R., & Gashteroodkhani, T. A. (2021). A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation. International Journal of Energy Sector Management, 15(3), 523-550. Retrieved from: https://www.emerald.com/insight/content/doi/10.1108/IJESM-02- 2020-0003/full/html
• Mirmozaffari, M., Yazdani, M., Boskabadi, A., Ahady Dolatsara, H., Kabirifar, K., & Amiri Golilarz, N. (2020). A novel machine learning approach combined with optimization models for eco-efficiency evaluation. Applied Sciences, 10(15), 5210. doi: https://doi.org/10.3390/app10155210
• Mousavi, M. M., Ouenniche, J., & Tone, K. (2019). A comparative analysis of two-stage distress prediction models. Expert Systems with Applications, 119, 322-341. doi: https://doi.org/10.1016/j.eswa.2018.10.053
• Nandy, A., & Singh, P. K. (2020). Application of fuzzy DEA and machine learning algorithms in efficiency estimation of paddy producers of rural Eastern India. Benchmarking: An International Journal, 28(1), 229-248. Retrieved from: https://www.emerald.com/insight/content/doi/10.1108/BIJ-01-2020-0012/full/html
• Nandy, A., & Singh, P. K. (2020). Farm efficiency estimation using a hybrid approach of machine-learning and data envelopment analysis: Evidence from rural eastern India. Journal of Cleaner Production, 267, 122106. doi: https://doi.org/10.1016/j.jclepro.2020.122106
• Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7, 21. doi: https://doi.org/10.3389/fnbot.2013.00021
• Özsoy, V. S., & Örkcü, H. H. (2021). Structural and operational management of Turkish airports: a bootstrap data envelopment analysis of efficiency. Utilities Policy, 69, 101180. doi: https://doi.org/10.1016/j.jup.2021.101180
• Rebai, S., Yahia, F. B., & Essid, H. (2020). A graphically based machine learning approach to predict secondary schools performance in Tunisia. Socio-Economic Planning Sciences, 70, 100724. doi: https://doi.org/10.1016/j.seps.2019.06.009
• Salehi, V., Veitch, B., & Musharraf, M. (2020). Measuring and improving adaptive capacity in resilient systems by means of an integrated DEA-Machine learning approach. Applied ergonomics, 82, 102975. doi: https://doi.org/10.1016/j.apergo.2019.102975
• Sarkis, J. (2007). Preparing your data for DEA. In Modeling data irregularities and structural complexities in data envelopment analysis (pp. 305-320). Springer, Boston, MA.
• Singpai, B., & Wu, D. (2020). Using a DEA–AutoML Approach to Track SDG Achievements. Sustainability, 12(23), 10124. doi: https://doi.org/10.3390/su122310124 Song, J., & Zhang, Z. (2009, January). Oil refining enterprise performance evaluation based on DEA and SVM. In 2009 Second International Workshop on Knowledge Discovery and Data Mining (pp. 401-404). IEEE. doi: 10.1109/WKDD.2009.43
• Tayal, A., Kose, U., Solanki, A., Nayyar, A., & Saucedo, J. A. M. (2020). Efficiency analysis for stochastic dynamic facility layout problem using meta‐heuristic, data envelopment analysis and machine learning. Computational Intelligence, 36(1), 172-202. doi: https://doi.org/10.1111/coin.12251
• Tayal, A., Solanki, A., & Singh, S. P. (2020). Integrated frame work for identifying sustainable manufacturing layouts based on big data, machine learning, meta-heuristic and data envelopment analysis. Sustainable Cities and Society, 62, 102383. doi: https://doi.org/10.1016/j.scs.2020.102383
• Thaker, K., Charles, V., Pant, A., & Gherman, T. (2021). A DEA and random forest regression approach to studying bank efficiency and corporate governance. Journal of the Operational Research Society, 1-28. doi: https://doi.org/10.1080/01605682.2021.1907239
• Thanassoulis, E. (2001). Introduction to the theory and application of data envelopment analysis. Dordrecht: Kluwer Academic Publishers.
• Xu, Y., Park, Y. S., & Park, J. D. (2021). Measuring the Response Performance of US States against COVID-19 Using an Integrated DEA, CART, and Logistic Regression Approach. In Healthcare (Vol. 9, No. 3, p. 268). MDPI. doi: https://doi.org/10.3390/healthcare9030268
• Zhang, Y., Yang, C., Yang, A., Xiong, C., Zhou, X., & Zhang, Z. (2015). Feature selection for classification with class-separability strategy and data envelopment analysis. Neurocomputing, 166, 172-184. doi: https://doi.org/10.1016/j.neucom.2015.03.081
• Zhou, Z., & Liu, W. (2015). DEA models with undesirable inputs, intermediates, and outputs. Data envelopment analysis: A handbook of models and methods, 415-446. Springer
• Zhu, N., Zhu, C., & Emrouznejad, A. (2020). A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies. Journal of Management Science and Engineering. doi: https://doi.org/10.1016/j.jmse.2020.10.001
• Zhu, J. (2009). Quantitative models for performance evaluation and benchmarking: data envelopment analysis with spreadsheets (Vol. 2). New York: Springer.