  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 are beyond its capability. Therefore, it is of critical
importance for the manufacturer to schedule mask production tasks as
efficiently as possible. However, existing scheduling methods typically require
a considerable amount of computational resources and, therefore, cannot
effectively cope with the surge of orders. In this paper, we propose an
end-to-end neural network for scheduling real-time production tasks. The neural
network takes a sequence of production tasks as inputs to predict a
distribution over different schedules, employs reinforcement learning to
optimize network parameters using the negative total tardiness as the reward
signal, and finally produces a high-quality solution to the scheduling problem.
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 (i.e., the total
weighted tardiness) produced by the neural network scheduler is significantly
better than those of existing constructive heuristics, and is very close to
those of the state-of-the-art metaheuristics whose computational time is
unaffordable in practice.
