Data driven approach for stochastic DEA in machine learning and artificial intelligence to improve the accuracy, stability, and interpretability of the model
DOI:
https://doi.org/10.35335/idea.v1i1.1Keywords:
DEA models, dynamic stochastic DEA models, accuracyAbstract
The novelty of this research lies in the integration of machine learning and artificial intelligence techniques into stochastic DEA models. While traditional DEA models have been widely used to measure the efficiency of decision-making units, they may not be able to capture complex and nonlinear relationships between inputs and outputs. By integrating advanced machine learning and AI techniques, this research aims to improve the accuracy, stability, and interpretability of stochastic DEA models, providing decision-makers with more reliable and actionable insights. Moreover, this research explores several novel approaches, including the integration of deep learning techniques, ensemble learning, dynamic stochastic DEA models, and explainable AI, to improve the performance of stochastic DEA models. These approaches have the potential to enhance the accuracy of efficiency scores, increase the stability of the model, provide more actionable insights, and improve the model's interpretability. By integrating these approaches into stochastic DEA models, this research aims to provide a comprehensive and effective solution to the problem of measuring the efficiency of decision-making units. This approach has not been explored extensively in the literature, and thus represents a novel and innovative approach to addressing this important research problem.
Downloads
References
Dawson, C. W., & Wilby, R. (1998). An artificial neural network approach to rainfall-runoff modelling. Hydrological Sciences Journal, 43(1), 47–66. https://doi.org/https://doi.org/10.1080/02626669809492102
Dehaghani, A. H. S., Soleimani, R., & Mohammadi, A. H. (2022). Screening of important parameters in optimal design of compressed air energy storage system using an ensemble learning method. Journal of Energy Storage, 48(104023), 1–9. https://doi.org/https://doi.org/10.1016/j.est.2022.104023
Heng, Y. S., & Subramanian, P. (2022). A Systematic Review of Machine Learning and Explainable Artificial Intelligence (XAI) in Credit Risk Modelling. Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1, 596–614. https://doi.org/10.1007/978-3-031-18461-1_39
Izadikhah, M. (2022). A fuzzy stochastic slacks-based data envelopment analysis model with application to healthcare efficiency. Healthcare Analytics, 2(November), 100038. https://doi.org/https://doi.org/10.1016/j.health.2022.100038
Keshteli, H. B., & Rostamy-Malkhalifeh, M. (2022). A combined machine learning algorithms and Interval DEA method for measuring predicting the efficiency. International Journal of Data Envelopment Analysis, 10(3), 57–64. https://doi.org/10.30495/IJDEA.2022.69371.1183
Lin, S., Zheng, H., Han, B., Li, Y., Han, C., & Li, W. (2022). Comparative performance of eight ensemble learning approaches for the development of models of slope stability prediction. Acta Geotechnica, 17(4), 1477–1502. https://doi.org/https://doi.org/10.1007/s11440-021-01440-1
Luo, Q., Miao, C., Sun, L., Meng, X., & Duan, M. (2019). Efficiency evaluation of green technology innovation of China’s strategic emerging industries: An empirical analysis based on Malmquist-data envelopment analysis index. Journal of Cleaner Production, 238(117782), 1–10. https://doi.org/https://doi.org/10.1016/j.jclepro.2019.117782
Saen, R. F., Memariani, A., & Lotfi, F. H. (2005). Determining relative efficiency of slightly non-homogeneous decision making units by data envelopment analysis: a case study in IROST. Applied Mathematics and Computation, 165(2), 313–328. https://doi.org/https://doi.org/10.1016/j.amc.2004.04.050
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. https://doi.org/https://doi.org/10.1111/coin.12251
Umenweke, G. C., Afolabi, I. C., Epelle, E. I., & Okolie, J. A. (2022). Machine learning methods for modeling conventional and hydrothermal gasification of waste biomass: A review. Bioresource Technology Reports, 17(February), 100976. https://doi.org/https://doi.org/10.1016/j.biteb.2022.100976
Wanke, P., Ostovan, S., Mozaffari, M. R., Gerami, J., & Tan, Y. (2023). Stochastic network DEA-R models for two-stage systems. Journal of Modelling in Management, 18(3), 842–875. https://doi.org/https://doi.org/10.1108/JM2-10-2021-0256
Yang, Z., Roth, J., & Jain, R. K. (2018). DUE-B: Data-driven urban energy benchmarking of buildings using recursive partitioning and stochastic frontier analysis. Energy and Buildings, 163(15), 58–69. https://doi.org/https://doi.org/10.1016/j.enbuild.2017.12.040
Yousefi, S., Shabanpour, H., Ghods, K., & Saen, R. F. (2023). How to improve the future efficiency of Covid-19 treatment centers? A hybrid framework combining artificial neural network and congestion approach of data envelopment analysis. Computers & Industrial Engineering, 176(108933), 1–15. https://doi.org/https://doi.org/10.1016/j.cie.2022.108933
Zhang, Q., Yuan, Q., Zeng, C., Li, X., & Wei, Y. (2018). Missing data reconstruction in remote sensing image with a unified spatial–temporal–spectral deep convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 56(8), 4274–4288. https://doi.org/10.1109/TGRS.2018.2810208
Zhang, X., Xia, Q., Wei, F., & Ang, S. (2023). Efficiency evaluation for decision making units with fixed-sum outputs using data envelopment analysis and stochastic multicriteria acceptability analysis. INFOR: Information Systems and Operational Research, 03155986(2191533), 1–26. https://doi.org/https://doi.org/10.1080/03155986.2023.2191533
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Dr. Hengki Tamando Sihotang, M.Kom, Aisyah Alesha
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
