期刊
APPLIED SOFT COMPUTING
卷 97, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.asoc.2020.106790
关键词
Emergency production; Flow shop scheduling; Neural network; Reinforcement learning; Public health emergencies
资金
- National Natural Science Foundation of China [61872123]
- Zhejiang Provincial Natural Science Foundation, China [LR20F030002, LQY20F030001]
- Zhejiang Provincial Emergency Project for Prevention & Treatment of New Coronavirus Pneumonia, China [2020C03126]
During the outbreak of the novel coronavirus pneumonia (COVID-19), there is a huge demand for medical masks. A mask manufacturer often receives a large amount of orders that must be processed within a short response time. It is of critical importance for the manufacturer to schedule and reschedule mask production tasks as efficiently as possible. However, when the number of tasks is large, most existing scheduling algorithms require very long computational time and, therefore, cannot meet the needs of emergency response. In this paper, we propose an end-to-end neural network, which takes a sequence of production tasks as inputs and produces a schedule of tasks in a realtime manner. The network is trained by reinforcement learning using the negative total tardiness as the reward signal. We applied the proposed approach to schedule emergency production tasks for a medical mask manufacturer during the peak of COVID-19 in China. Computational results show that the neural network scheduler can solve problem instances with hundreds of tasks within seconds. The objective function value obtained by the neural network scheduler is significantly better than those of existing constructive heuristics, and is close to those of the state-of-the-art metaheuristics whose computational time is unaffordable in practice. (C) 2020 Elsevier B.V. All rights reserved.
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