Reinforcement learning for joint pricing, lead-time and scheduling decisions in make-to-order systems
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DOI: 10.1016/j.ejor.2012.03.020
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Cited by:
- Olumide Emmanuel Oluyisola & Swapnil Bhalla & Fabio Sgarbossa & Jan Ola Strandhagen, 2022. "Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 311-332, January.
- Zhai, Yue & Cheng, T.C.E., 2022. "Lead-time quotation and hedging coordination in make-to-order supply chain," European Journal of Operational Research, Elsevier, vol. 300(2), pages 449-460.
- Sun, Yiqi & Wu, Zhengping & Zhu, Wanshan, 2022. "When do firms benefit from joint price and lead-time competition?," European Journal of Operational Research, Elsevier, vol. 302(2), pages 497-517.
- Yang, Hongbing & Li, Wenchao & Wang, Bin, 2021. "Joint optimization of preventive maintenance and production scheduling for multi-state production systems based on reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
- Juan Pablo Usuga Cadavid & Samir Lamouri & Bernard Grabot & Robert Pellerin & Arnaud Fortin, 2020. "Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1531-1558, August.
- 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).
- Tanja Mlinar & Philippe Chevalier, 2016.
"Pooling heterogeneous products for manufacturing environments,"
4OR, Springer, vol. 14(2), pages 173-200, June.
- MLINAR, Tanja & CHEVALIER, Philippe, 2016. "Pooling Heterogeneous Products for Manufacturing Environments," LIDAM Reprints CORE 2744, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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Keywords
Pricing; Reinforcement learning (RL); Scheduling; Q-learning; Simulation-based optimization; Semi-Markov Decision Problem (SMDP);All these keywords.
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