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

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.

Multi-Objective Optimization of Integrated Civilian-Military Scheduling of Medical Supplies for Epidemic Prevention and Control

“…Therefore, our ongoing study is to incorporate mathematical models (e.g., [ 36 , 37 ]) in order to predict the spread of the epidemic and consequent supply demands in a more accurate manner. Our future work will also integrate the scheduling of vehicle for supply delivery [ 38 ] and the scheduling of supply production [ 2 ] into our approach in order to provide a more comprehensive decision support for epidemic prevention and control.…”

“…A large-scale epidemic outbreak often causes a huge shortage of medical supplies, such as medical masks, protecting clothing, ventilators, sickbeds, and computed tomography (CT), to name just a few [ 1 , 2 ]. For example, during the peak period of the novel coronavirus pneumonia (COVID-19) in Wuhan, China, the satisfaction rates of medical N95 masks, protecting clothing, and goggles are 52.57%, 30.88%, and 14.67%, respectively, as shown in Figure 1 .…”

Differential evolution and particle swarm optimization against COVID-19

“…No detailed discussion on historical applications is given. No detailed results of various population sizes and numbers of function calls that are said to be tested Mask production real-time scheduling Wu et al ( 2020 ) Large size scheduling instances ? 1 1.…”

“…Nowakova et al ( 2020 ) used the classical DE (Storn and Price 1997 ) for selection of subsets of matrix columns to analyze COVID-19 radiographs; again—no comparison against other metaheuristics was provided. Wu et al ( 2020 ) found that among 5 competitors, the algebraic DE variant (Santucci et al 2016 ) is the best method for mask-production real-time scheduling task. Discrete hybridization of PSO and DE has also been compared against three other metaheuristics for goods assignment maximization during COVID-19 pandemic and the risk of infection minimization (Zou et al 2020 ); the hybrid approach was praised, but it seems to perform better for the infection minimization criterion than for goods assignment problem.…”

“…Apart from DE and PSO, a number of other metaheuristics were used against COVID-19. Some of them were mentioned previously, as they were compared against DE or PSO variants in various papers (Lobato et al 2020 ; Saif et al 2021 ; Abdel-Basset et al 2020c ; Abd Elaziz et al 2020a , b ; Wu et al 2020 ; Zheng et al 2020a , b ; Al-qaness et al 2020a , b , 2021a ; Ardabili et al 2020 ; Sazvar et al 2020 ; Zhan et al 2020 ; Canayaz 2020 ; El-Kenawy et al 2020 ; Goel et al 2020 ; Sahlol et al 2020 ; Issa and Abd Elaziz 2020 ). Various other studies, in which applications of metaheuristics not related to DE or PSO for COVID-19 research are presented, are summarized in Table 4 .…”