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. 2020 Dec:97:106790.
doi: 10.1016/j.asoc.2020.106790. Epub 2020 Oct 14.

Real-time neural network scheduling of emergency medical mask production during COVID-19

Chen-Xin Wu  1 , Min-Hui Liao  1 , Mumtaz Karatas  2 , Sheng-Yong Chen  3 , Yu-Jun Zheng  1
Affiliations

Real-time neural network scheduling of emergency medical mask production during COVID-19

Chen-Xin Wu et al. Appl Soft Comput. 2020 Dec.

Abstract

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 real-time 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.

Keywords: Emergency production; Flow shop scheduling; Neural network; Public health emergencies; Reinforcement learning.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

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Graphical abstract
Fig. 1
Fig. 1
Basic flow to apply the machine learning approach to solve the production task scheduling problem.
Fig. 2
Fig. 2
Architecture of the neural network scheduler.
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Fig. 3
Snapshots of the codes of algorithmic programs.
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Fig. 4
Convergence curves of the four methods for training the neural network.
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Fig. 5
Numbers of epochs of the four methods to reach the best value within 1% error.
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Fig. 6
Distribution of number of tasks of the real-world instances.
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Fig. 7
Comparison of the results of the neural network scheduler, constructive heuristics, and metaheuristic algorithms on the instance of Feb 8.
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Fig. 8
Comparison of the results of the neural network scheduler, constructive heuristics, and metaheuristic algorithms on the instance of Feb 9.
Fig. 9
Fig. 9
Comparison of the results of the neural network scheduler, constructive heuristics, and metaheuristic algorithms on the instance of Feb 10.
Fig. 10
Fig. 10
Comparison of the results of the neural network scheduler, constructive heuristics, and metaheuristic algorithms on the instance of Feb 11.
Fig. 11
Fig. 11
Comparison of the results of the neural network scheduler, constructive heuristics, and metaheuristic algorithms on the instance of Feb 12.
Fig. 12
Fig. 12
Comparison of the results of the neural network scheduler, constructive heuristics, and metaheuristic algorithms on the instance of Feb 13.
Fig. 13
Fig. 13
Comparison of the results of the neural network scheduler, constructive heuristics, and metaheuristic algorithms on the instance of Feb 14.
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See this image and copyright information in PMC

References

    1. Pinedo M. second ed. Prentice Hall; New York: 2002. Scheduling Theory, Algorithms, and Systems.
    1. McMahon G.B., Burton P.G. Flow-shop scheduling with the branch-and-bound method. Oper. Res. 1967;15(3):473–481. doi: 10.1287/opre.15.3.473. - DOI
    1. Karlof J.K., Wang W. Bilevel programming applied to the flow shop scheduling problem. Comput. Oper. Res. 1996;23(5):443–451. doi: 10.1016/0305-0548(95)00034-8. - DOI
    1. Ziaee M., Sadjadi S. Mixed binary integer programming formulations for the flow shop scheduling problems, a case study: ISD projects scheduling. Appl. Math. Comput. 2007;185(1):218–228. doi: 10.1016/j.amc.2006.06.092. - DOI
    1. Gicquel C., Hege L., Minoux M., van Canneyt W. A discrete time exact solution approach for a complex hybrid flow-shop scheduling problem with limited-wait constraints. Comput. Oper. Res. 2012;39(3):629–636. doi: 10.1016/j.cor.2011.02.017. - DOI

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