Zhu, Nan, Zhu, Chuanjin and Emrouznejad, Ali (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 ,
Abstract
Data Envelopment Analysis (DEA) is a linear programming methodology for measuring the efficiency of Decision Making Units (DMUs) to improve organizational performance in the private and public sectors. However, if a new DMU needs to be known its efficiency score, the DEA analysis would have to be re-conducted, especially nowadays, datasets from many fields have been growing rapidly in the real world, which will need a huge amount of computation. Following the previous studies, this paper aims to establish a linkage between the DEA method and machine learning (ML) algorithms, and proposes an alternative way that combines DEA with ML (ML-DEA) algorithms to measure and predict the DEA efficiency of DMUs. Four ML-DEA algorithms are discussed, namely DEA-CCR model combined with back-propagation neural network (BPNN-DEA), with genetic algorithm (GA) integrated with back-propagation neural network (GANN-DEA), with support vector machines (SVM-DEA), and with improved support vector machines (ISVM-DEA), respectively. To illustrate the applicability of above models, the performance of Chinese manufacturing listed companies in 2016 is measured, predicted and compared with the DEA efficiency scores obtained by the DEA-CCR model. The empirical results show that the average accuracy of the predicted efficiency of DMUs is about 94%, and the comprehensive performance order of four ML-DEA algorithms ranked from good to poor is GANN-DEA, BPNN-DEA, ISVM-DEA, and SVM-DEA.
Publication DOI: | https://doi.org/10.1016/j.jmse.2020.10.001 |
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Divisions: | College of Business and Social Sciences > Aston Business School > Operations & Information Management |
Additional Information: | Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) |
Publication ISSN: | 2096-2320 |
Last Modified: | 08 Nov 2024 08:14 |
Date Deposited: | 14 Oct 2020 10:25 |
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Related URLs: |
https://www.sci ... 0469?via%3Dihub
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PURE Output Type: | Article |
Published Date: | 2020-10-10 |
Published Online Date: | 2020-10-10 |
Accepted Date: | 2020-10-01 |
Authors: |
Zhu, Nan
Zhu, Chuanjin Emrouznejad, Ali ( 0000-0001-8094-4244) |
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