En el libro Artificial intelligence for COVID-19
Springer, Cham, Suiza
Resumen:
The aim of this paper is to introduce an improved strategy for controlling COVID-19 and other pandemic episodes as an environmental disinfection culture for public places. The scheduling aims at achieving the best utilization of the available working day-time hours, which is calculated as the total consumed disinfection times minus the total loosed transportation times. The proposed problem in network optimization identifies a disinfection group who is likely to select a route to reach a subset of predetermined public places to be regularly disinfected with the most utilization of the available day-working hours. A Nonlinear Binary Model is introduced with a detailed real application case study involving improving the scheduling of Coronavirus disinfection process for some Educational Institutions as an example of crowded public places in Cairo, Egypt. The case study mathematical model is solved using a novel Discrete Binary Gaining-Sharing knowledge-based optimization algorithm (DBGSK). The results of this study show that the novel optimization algorithm can efficiently solve the proposed Problem.
Palabras clave: Disinfection process scheduling; COVID-19; Public places; Nonlinear binary constrained optimization; Gaining sharing Knowledge-Based optimization algorithm
ISBN: 978-3-030-69743-3
DOI: 
https://doi.org/10.1007/978-3-030-69744-0_13
DOI del Libro:
https://doi.org/10.1007/978-3-030-69744-0
Publicado: 2021
Cita:
S.A. Hassan, P. Agrawal, T. Ganesh, A.W. Mohamed, Optimum scheduling of the disinfection process for covid-19 in public places with a case study from Egypt, a novel discrete binary gaining-sharing knowledge-based metaheuristic algorithm, en Artificial intelligence for COVID-19. Ed. Springer. Cham, Suiza, 2021.
