Vol.41, No.3, 2022, pp.933-944, doi:10.32604/csse.2022.021078
A Hybrid Heuristic Algorithm for Solving COVID-19’s Social Distancing at Universities Campus
  • Hassan Al-Tarawneh1, Khalid Al-Kaabneh1, Aysh Alhroob2, Hazem Migdady3, Issam Alhadid4,*
1 Department of Data sciences and Artificial Intelligence, Al-Ahlyyia Amman University, Amman, Jordan
2 Department of Software Engineering, Isra University, Amman, Jordan
3 Department of CSMIS, Oman college of management and technology, Muscat, Oman
4 Faculty of Information Technology and Systems, University of Jordan, Aqaba, Jordan
* Corresponding Author: Issam Alhadid. Email:
(This article belongs to this Special Issue: Advances in Computational Intelligence and its Applications)
Received 22 June 2021; Accepted 29 July 2021; Issue published 10 November 2021
Social distancing during COVID-19 has become one of the most important measures in reducing the risks of the spread of the virus. Implementing these measures at universities is crucial and directly related to the physical attendance of the populations of students, professors, employees, and other members on campus. This research proposes an automated scheduling approach that can help universities and schools comply with the social distancing regulations by providing assistance in avoiding huge assemblages of people. Furthermore, this paper proposes a novel course timetable-scheduling scheme based on four main constraints. First, a distance of two meters must be maintained between each student inside the classroom. Second, no classrooms should contain more than 20% of their regular capacity. Third, there would be no back-to-back classes. Lastly, no lectures should be held simultaneously in adjacent classrooms. The proposed approach was implemented using a variable neighborhood search (VNS) approach with an adaptive neighborhood structure (AD-NS) to resolve the problem of scheduling course timetables at Al-Ahlyyia Amman University. However, the experimental results show that the proposed techniques outperformed the standard VNS tested on university course timetabling benchmark dataset ITC2007-Track3. Meanwhile, the approach was tested using datasets collected from the faculty of information technology at Al-Ahlyyia Amman University (Jordan). Where the results showed that, the proposed technique could help educational institutes to resume their regular operations while complying with the social distancing guidelines.
COVID-19; social distance; variable neighborhood search; adaptive neighborhood structure; university course timetable
Cite This Article
H. Al-Tarawneh, K. Al-Kaabneh, A. Alhroob, H. Migdady and I. Alhadid, "A hybrid heuristic algorithm for solving covid-19’s social distancing at universities campus," Computer Systems Science and Engineering, vol. 41, no.3, pp. 933–944, 2022.
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