지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.12
- 수록면
- 13 - 26 (14page)
- DOI
- 10.25052/KSCM.2021.12.21.3.13
이용수
초록· 키워드
오류제보하기
Data envelopment analysis (DEA) is a tool for identifying best-practices when multiple performance metrics or measures are present for decision-making units (DMUs). As big data issue becomes an important area of supply chain and operations management, DEA is evolving into a data-oriented data science tool for benchmarking, performance evaluation, composite index construction and others. As the number of DMUs increases, the running-time to solve the standard DEA model sharply rises. Such situations are appearing more frequently in the era of big data. This issue could be an important challenge particularly when real-time data stream in at extremely high rates and the DEA analysis needs to be performed very quickly. Therefore, there exist practical needs for developing an efficient way of solving large-scale DEA problems. In this paper, we propose a practical approach for speeding up the DEA efficiency estimation process based on machine learning. In this approach, a sample of DMUs is selected from the population as a training data set, based on which a machine is trained to predict the efficiency scores of unselected or newly streamed-in DMUs. We also suggest a data augmentation technique to enhance the learning process under severe data class imbalance. The superior performance of the proposed approach over the conventional one in terms of efficiency prediction power as well as model computation time is shown through a series of computational experiments using randomly generated data.
목차
1. 서론
2. 이론적 배경
3. 제안 프레임워크
4. 실증 실험
5. 결론
REFERENCES
참고문헌 (20)
참고문헌 신청
Barr, R. S., & Durchholz, M. L. (1997). Parallel and hierarchical decomposition approaches for solving large-scale data envelopment analysis models. Annals of Operations Research, 73, 339-372.
Charles, V., Aparicio, J., & Zhu, J. (2019). The curse of dimensionality of decision-making units: A simple approach to increase the discriminatory power of data envelopment analysis. European Journal of Operational Research, 279(3), 929-940.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
Chen, W. C., & Cho, W. J. (2009). A procedure for large-scale DEA computations. Computers & Operations Research, 36(6), 1813-1824.
Chen, W. C., & Lai, S. Y. (2017). Determining radial efficiency with a large data set by solving small-size linear programs. Annals of Operations Research, 250(1), 147-166.
Charles, V., Aparicio, J., & Zhu, J. (2019). The curse of dimensionality of decision-making units: A simple approach to increase the discriminatory power of data envelopment analysis. European Journal of Operational Research, 279(3), 929-940.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
Chen, W. C., & Cho, W. J. (2009). A procedure for large-scale DEA computations. Computers & Operations Research, 36(6), 1813-1824.
Chen, W. C., & Lai, S. Y. (2017). Determining radial efficiency with a large data set by solving small-size linear programs. Annals of Operations Research, 250(1), 147-166.
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UCI(KEPA) : I410-ECN-0101-2022-324-000121003