En el libro Intelligent data analysis for COVID-19 pandemic
Springer, Singapur, Singapur
Resumen:
An improved scheduling the disinfection process of the new coronavirus (COVID-19) is introduced. The scheduling aims at achieving the best utilization of the available day time, which is calculated as the total disinfection time minus the total loss travelling time. In this regard, a new application problem is presented, which is called a travelling disinfection-man problem (TDP). The new problem (TDP) in network optimization resemble somehow the famous travelling salesman problems (TSP) but with basic distinct variations where a disinfection group is likely to select a route to reach a subset of predetermined places to be 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 the improving the scheduling of coronavirus disinfection process for five contaminated faculties in Ain Shams University in Cairo, and the case study is solved using a novel discrete binary gaining-sharing knowledge-based optimization algorithm (DBGSK).
Palabras clave: Coronavirus disinfection scheduling; Travelling disinfection-man problem (TSP); Nonlinear binary constrained optimization; Gaining-sharing knowledge-based algorithm; Discrete binary variables
ISBN: 978-981-16-1573-3
DOI: 
https://doi.org/10.1007/978-981-16-1574-0_13
DOI del Libro:
https://doi.org/10.1007/978-981-16-1574-0
Publicado: 2021
Cita:
S.A. Hassan, P. Agrawal, T. Ganesh, A.W. Mohamed, A Travelling Disinfection-Man Problem (TDP) for COVID-19: a nonlinear binary constrained gaining-sharing knowledge-based optimization algorithm, en Intelligent data analysis for COVID-19 pandemic. Ed. Springer. Singapur, Singapur, 2021.
