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10.1016/j.asoc.2020.106790 http://scihub22266oqcxt.onion/10.1016/j.asoc.2020.106790 33071685!7556290!33071685     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Appl+Soft+Comput 2020 ; 97 (ä): 106790 Nephropedia Template TP {{tp|p=33071685|t=2020. Real-time neural network scheduling of emergency medical mask production during COVID-19 |pdf=|usr=}}{{33071685}} gab.com Text Real-time neural network scheduling of emergency medical mask production during COVID-19 JO: Appl Soft Comput http://www.kidney.de/pget.php?&tw=1&pmid=33071685 #FOAvip 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. Twit Text FOAVip Real-time neural network scheduling of emergency medical mask production during COVID-19 ????Appl Soft Comput http://www.kidney.de/pget.php?&tw=1&pmid=33071685 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33071685 #FOAvip English Wikipedia <ref>{{Cite pmid|33071685}}</ref> Real-time neural network scheduling of emergency medical mask production during COVID-19 #MMPMID33071685 Wu CX; Liao MH; Karatas M; Chen SY; Zheng YJ Appl Soft Comput 2020[Dec]; 97 (ä): 106790 PMID33071685show ga 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. äReal-time neural network scheduling of emergency medical mask producti(epmc).pdf DeepDyve Pubget Overpricing