Next Article in Journal
Improved Performance in the Detection of ACO-OFDM Modulated Signals Using Deep Learning Modules
Next Article in Special Issue
Towards Predicting Student’s Dropout in University Courses Using Different Machine Learning Techniques
Previous Article in Journal
Sleep Quality in Older Women: Effects of a Vibration Training Program
Previous Article in Special Issue
Analysis and Prediction of Engineering Student Behavior and Their Relation to Academic Performance Using Data Analytics Techniques
Article

Table Organization Optimization in Schools for Preserving the Social Distance during the COVID-19 Pandemic

Department of Mechanical, Computer and Aerospace Engineering, Universidad de León, 24071 León, Spain
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(23), 8392; https://doi.org/10.3390/app10238392
Received: 8 November 2020 / Revised: 20 November 2020 / Accepted: 23 November 2020 / Published: 25 November 2020
(This article belongs to the Special Issue Data Analytics and Machine Learning in Education)
The COVID-19 pandemic has supposed a challenge for education. The school closures during the initial coronavirus outbreak for reducing the infections have promoted negative effects on children, such as the interruption of their normal social relationships or their necessary physical activity. Thus, most of the countries worldwide have considered as a priority the reopening of schools but imposing some rules for keeping safe places for the school lessons such as social distancing, wearing facemasks, hydroalcoholic gels or reducing the capacity in the indoor rooms. In Spain, the government has fixed a minimum distance of 1.5 m among the students’ desks for preserving the social distancing and schools have followed orthogonal and triangular mesh patterns for achieving valid table dispositions that meet the requirements. However, these patterns may not attain the best results for maximizing the distances among the tables. Therefore, in this paper, we introduce for the first time in the authors’ best knowledge a Genetic Algorithm (GA) for optimizing the disposition of the tables at schools during the coronavirus pandemic. We apply this GA in two real-application scenarios in which we find table dispositions that increase the distances among the tables by 19.33% and 10%, respectively, with regards to regular government patterns in these classrooms, thus fulfilling the main objectives of the paper. View Full-Text
Keywords: COVID-19; table distribution optimization; table location problem; Genetic Algorithms; genetic operators COVID-19; table distribution optimization; table location problem; Genetic Algorithms; genetic operators
Show Figures

Figure 1

MDPI and ACS Style

Ferrero-Guillén, R.; Díez-González, J.; Verde, P.; Álvarez, R.; Perez, H. Table Organization Optimization in Schools for Preserving the Social Distance during the COVID-19 Pandemic. Appl. Sci. 2020, 10, 8392. https://doi.org/10.3390/app10238392

AMA Style

Ferrero-Guillén R, Díez-González J, Verde P, Álvarez R, Perez H. Table Organization Optimization in Schools for Preserving the Social Distance during the COVID-19 Pandemic. Applied Sciences. 2020; 10(23):8392. https://doi.org/10.3390/app10238392

Chicago/Turabian Style

Ferrero-Guillén, Rubén, Javier Díez-González, Paula Verde, Rubén Álvarez, and Hilde Perez. 2020. "Table Organization Optimization in Schools for Preserving the Social Distance during the COVID-19 Pandemic" Applied Sciences 10, no. 23: 8392. https://doi.org/10.3390/app10238392

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop