A deep learning-based approach to a newsvendor problem considering uncertainty and time-varying costs
Author:
Funder
Ministry of Education
Publisher
Springer Science and Business Media LLC
Subject
Control and Optimization,Business, Management and Accounting (miscellaneous)
Link
https://link.springer.com/content/pdf/10.1007/s11590-023-02028-2.pdf
Reference19 articles.
1. Al-Gabalawy, M., Hosny, N.S., Adly, A.R.: Probabilistic forecasting for energy time series considering uncertainties based on deep learning algorithms. Electric Power Syst. Res. 196, 107216 (2021)
2. Althoff, D., Rodrigues, L.N., Bazame, H.C.: Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble. Stoch. Env. Res. Risk Assess. 35, 1051–1067 (2021)
3. Azoury, K.S.: Bayes solution to dynamic inventory models under unknown demand distribution. Manage. Sci. 31(9), 1150–1160 (1985)
4. Ban, G.Y., Rudin, C.: The big data newsvendor: practical insights from machine learning. Oper. Res. 67(1), 90–108 (2019)
5. Barrow, D.K., Kourentzes, N.: Distributions of forecasting errors of forecast combinations: implications for inventory management. Int. J. Prod. Econ. 177, 24–33 (2016)
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