Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor
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DOI: 10.1080/00207543.2020.1733125
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Cited by:
- Wu, Chia-Hung & Chou, Che-Wei & Chien, Chen-Fu & Lin, Yun-Siang, 2024. "Digital transformation in manufacturing industries: Effects of firm size, product innovation, and production type," Technological Forecasting and Social Change, Elsevier, vol. 207(C).
- Jeronymo Marcondes Pinto & Emerson Fernandes Marçal, 2023. "An artificial intelligence approach to forecasting when there are structural breaks: a reinforcement learning-based framework for fast switching," Empirical Economics, Springer, vol. 65(4), pages 1729-1759, October.
- Yan, Yimo & Chow, Andy H.F. & Ho, Chin Pang & Kuo, Yong-Hong & Wu, Qihao & Ying, Chengshuo, 2022. "Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 162(C).
- Thais de Castro Moraes & Xue‐Ming Yuan & Ek Peng Chew, 2024. "Hybrid convolutional long short‐term memory models for sales forecasting in retail," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1278-1293, August.
- Núñez-Merino, Miguel & Maqueira-Marín, Juan Manuel & Moyano-Fuentes, José & Castaño-Moraga, Carlos Alberto, 2022. "Industry 4.0 and supply chain. A Systematic Science Mapping analysis," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
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