Task Allocation in Human–Machine Manufacturing Systems Using Deep Reinforcement Learning
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- Digiesi, Salvatore & Kock, Ad A.A. & Mummolo, Giovanni & Rooda, Jacobus E., 2009. "The effect of dynamic worker behavior on flow line performance," International Journal of Production Economics, Elsevier, vol. 120(2), pages 368-377, August.
- Trentesaux, Damien & Moray, Neville & Tahon, Christian, 1998. "Integration of the human operator into responsive discrete production management systems," European Journal of Operational Research, Elsevier, vol. 109(2), pages 342-361, September.
- Alan S. Manne, 1960.
"On the Job-Shop Scheduling Problem,"
Operations Research, INFORMS, vol. 8(2), pages 219-223, April.
- Alan S. Manne, 1959. "On the Job Shop Scheduling Problem," Cowles Foundation Discussion Papers 73, Cowles Foundation for Research in Economics, Yale University.
- Xiong, Jian & Xing, Li-ning & Chen, Ying-wu, 2013. "Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns," International Journal of Production Economics, Elsevier, vol. 141(1), pages 112-126.
- David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
- Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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Keywords
deep learning; dynamic task allocation; human factors; intelligent manufacturing systems; manufacturing scheduling; reinforcement learning;All these keywords.
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