數據包絡分析
數據包絡分析（英語：Data envelopment analysis，DEA）是運籌學和經濟學中一種非參數化的評估生產前沿的方法。[1] 這一分析方法廣泛應用於多個領域，包括國際銀行業、經濟可持續性研究、警務運營，以及物流管理。[2][3][4] DEA還被用於評估自然語言處理模型的性能，在機器學習領域也展現出諸多應用前景。[5][6][7]
簡述
[編輯]數據包絡分析（DEA）是一種實證測量決策單元生產效率（DMU）的方法。儘管起源於經濟學的生產理論，但如今已廣泛應用於運營管理的標杆評估。[8] 在標杆分析中，研究者選擇一系列指標，對製造業和服務業的運營績效進行比較。[1][9]:243–285
與必須預先設定生產或成本函數的參數化方法不同，非參數化方法僅根據可獲得的數據比較可行的投入產出組合。[10] 作為最常用的非參數化方法之一，數據包絡分析得名於其對數據集中高效決策單元的「包絡」特性。在這一方法中，經驗上最高效的決策單元構成了「最佳實踐前沿」，其他所有決策單元都與之對標。
數據包絡分析之所以廣受歡迎，主要有三個原因：
- 假設條件相對較少
- 能夠對多維度的投入和產出進行標杆比較
- 計算簡便，可以通過線性規劃方法直接求解效率比率
不同於傳統方法，DEA並不追求構建嚴格的「生產前沿」，而是試圖找出實踐中最為高效的運營模式。[11]
歷史
[編輯]DEA最早可追溯到1978年。在Farrell的研究基礎上，[12] 查恩斯、庫珀和羅德斯[1]運用線性規劃，首次實證性地估算了生產技術前沿。在德國，類似方法此前已被用於估算研發及其他生產要素的邊際生產率。此後，DEA迅速成為學術界研究的熱點，大量專著和期刊論文湧現。
從最初的CCR模型（以創始人查恩斯、庫珀、羅德斯命名）開始，[1] 學者們不斷拓展DEA的應用。這些擴展包括多個方面：調整模型隱含假設、區分技術效率與配置效率[13]、引入輸入輸出的有限替代性[14]、考慮規模收益變化[15]，以及開發更複雜的分析技術，如隨機DEA[16]和交叉效率分析等。[17]
腳註
[編輯]- ^ 1.0 1.1 1.2 1.3 Charnes et al (1978)
- ^ Charnes et al (1995)
- ^ Emrouznejad et al (2016)
- ^ Thanassoulis (1995)
- ^ Koronakos and Sotiropoulos (2020)
- ^ Zhou et al (2022)
- ^ Guerrero et al (2022)
- ^ Mahmoudi et al (2021)
- ^ Sickles et al (2019)
- ^ Cooper et al (2007)
- ^ Cooper et al (2011)
- ^ Farrell (1957)
- ^ Fried et al (2008)
- ^ Cooper et al (2000)
- ^ Banker et al (1984)
- ^ Olesen (2016)
- ^ 引用錯誤：沒有為名為
:2的參考文獻提供內容
參考文獻
[編輯]- Charnes, Abraham; Cooper, William Wager; Rhodes, E. Measuring the Efficiency of Decision Making Units (PDF). European Journal of Operational Research. 1978, 2 (6): 429–444 [27 January 2022]. doi:10.1016/0377-2217(78)90138-8.
- Charnes, Abraham; Cooper, William; Lewin, Arie; Seiford, Lawrence. Data Envelopment Analysis: Theory, Methodology, and Applications. Springer Science & Business Media. 1995. ISBN 9780792394808.
- Mahmoudi, Amin; Abbasi, Mehdi; Deng, Xiaopeng. Evaluating the Performance of the Suppliers Using Hybrid DEA-OPA Model: A Sustainable Development Perspective. Group Decision and Negotiation. 2021, 31 (2): 335–362. ISSN 0926-2644. S2CID 254498857. doi:10.1007/s10726-021-09770-x.
- Banker, R. D.; Charnes, A.; Cooper, William Wager. Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis (PDF). Management Science. September 1984, 30 (9): 1078–1092 [27 January 2022]. S2CID 51901687. doi:10.1287/mnsc.30.9.1078.
- Brockhoff K. On the Quantification of the Marginal Productivity of Industrial Research by Estimating a Production Function for a Single Firm. German Economic Review. 1970, 8: 202–229.
- Banker, R. D.; Charnes, A.; Cooper, William Wager. Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis (PDF). Management Science. September 1984, 30 (9): 1078–1092 [27 January 2022]. S2CID 51901687. doi:10.1287/mnsc.30.9.1078.
- Cook, Wade D.; Hababou, Moez; Tuenter, Hans J. H. Multicomponent Efficiency Measurement and Shared Inputs in Data Envelopment Analysis: An Application to Sales and Service Performance in Bank Branches. Journal of Productivity Analysis. November 2000, 14 (3): 209–224. JSTOR 41781515. doi:10.1023/A:1026598803764.
- Cook, Wade D.; Tone, Kaoru; Zhu, Joe. Data envelopment analysis: Prior to choosing a model. Omega. April 2014, 44 (C): 1–4. doi:10.1016/j.omega.2013.09.004.
- Cooper, William Wager; Seiford, Lawrence; Zhu, Joe. A unified additive model approach for evaluating inefficiency and congestion with associated measures in DEA. Socio-Economic Planning Sciences. 2000, 34 (1): 1–25. doi:10.1016/S0038-0121(99)00010-5.
- Cooper, William Wager; Seiford, Lawrence; Zhu, Joe. A unified additive model approach for evaluating inefficiency and congestion with associated measures in DEA. Socio-Economic Planning Sciences. 2000, 34 (1): 1–25. doi:10.1016/S0038-0121(99)00010-5.
- Cooper, William Wager; Seiford, Lawrence M.; Tone, Kaoru. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software 2. Springer Publishing. 2007 （英語）.
- Cooper, William Wager; Seiford, Lawrence M.; Zhu, Joe (編). Handbook on Data Envelopment Analysis. International Series in Operations Research & Management Science 164 2. Springer Publishing. 2011. ISBN 978-1441961501 （英語）.
- Dyson, R. G.; Allen, R.; Camanho, A. S.; Podinovski, V. V.; Sarrico, C. S.; Shale, E. A. Pitfalls and protocols in DEA. European Journal of Operational Research. Data Envelopment Analysis. 2001-07-16, 132 (2): 245–259. doi:10.1016/S0377-2217(00)00149-1.
- Doyle, John; Green, Rodney. Efficiency and Cross-efficiency in DEA: Derivations, Meanings and Uses. Journal of the Operational Research Society. 1994, 45 (5): 567–578. ISSN 0160-5682. S2CID 122161456. doi:10.1057/jors.1994.84 （英語）.
- Emrouznejad, Ali; Banker, Rajiv; Ray, Subhash; Chen, Lei. Recent Applications of Data Envelopment Analysis. Proceedings of the 14th International Conference on Data Envelopment Analysis. 2016.
- Farrell, Michael James. The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. 1957, 120 (3): 253–290. JSTOR 2343100. doi:10.2307/2343100.
- Fried, Harold O.; Lovell, C. A. Knox; Schmidt, Shelton S. The Measurement of Productive Efficiency and Productivity Growth. Oxford University Press. 2008. ISBN 978-0-19-804050-7 （英語）.
- Guerrero, Nadia; Aparicio, Juan; Valero-Carreras, Daniel. Combining data envelopment analysis and machine learning. Mathematics. 2022, 10 (6): 909. doi:10.3390/math10060909
. - Lovell, C.A.L., & P. Schmidt (1988) "A Comparison of Alternative Approaches to the Measurement of Productive Efficiency, in Dogramaci, A., & R. Färe (eds.) Applications of Modern Production Theory: Efficiency and Productivity, Kluwer: Boston.
- Olesen, Ole B.; Petersen, Niels Christian. Stochastic Data Envelopment Analysis—A review. European Journal of Operational Research. 2016, 251 (1): 2–21. ISSN 0377-2217. doi:10.1016/j.ejor.2015.07.058 （英語）.
- Ramanathan, R. An Introduction to Data Envelopment Analysis: A tool for Performance Measurement. N.Delhi: SAGE Publishing. 2003 （英語）.
- Sexton, Thomas R. Data envelopment analysis: Critique and extension. New Directions for Program Evaluation. 1986, 1986 (32): 73–105. doi:10.1002/ev.1441.
- Sickles, Robin; Zelenyuk, Valentin. Measurement of Productivity and Efficiency - Theory and Practice (PDF). Cambridge University Press. 2019 [27 January 2022]. ISBN 978-1-107-68765-3 （英語）.
- Thanassoulis, Emmanuel. Assessing police forces in England and Wales using data envelopment analysis. European Journal of Operational Research. 1995, 87 (3): 641–657. doi:10.1016/0377-2217(95)00236-7.
- Zhou, Zachary; Zachariah, Alisha; Conathan, Devin; Kline, Jeffery. Assessing Resource-Performance Trade-off of Natural Language Models using Data Envelopment Analysis. Proceedings of the 3rd Workshop on Evaluation and Comparison of NLP Systems (Association for Computational Linguistics). 2022: 11–20. arXiv:2211.01486 .
- Koronakos, Gregory; Sotiropoulos, Dionysios. A Neural Network approach for Non-parametric Performance Assessment. 2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA. IEEE. 2020: 1–8. ISBN 978-1-6654-2228-4. S2CID 228097834. doi:10.1109/IISA50023.2020.9284346.
