A Mathematical Optimization Model for Allocating Semester Weeks to Students of Different Disciplines in Coronary-Living Conditions

Document Type : Research Paper

Authors

1 Prof., Faculty of Statistics, Mathematics & Computer, Allameh Tabataba’i University, Tehran, Iran.

2 Ph.D. Candidate, Department of Educational Technology, Faculty of Psychology & Educational Sciences, Allameh Tabataba’i University, Tehran, Iran.

Abstract

Objective: Many of the world's top universities have already decided to hold the next semester with e-teaching. In Iran, too, the forecasts show a red situation for some areas in terms of corona prevalence. Also, several university students live in these areas. In planning the next semester, therefore, more focus should be on e-teaching. According to the instructions of the Ministry of Science, Research and Technology in Iran, education should be implemented in two parts, including e-teaching and face-to-face training. In face-to-face education with the needs of the educational space, students are divided into disciplines so that the implementation of health protocols in the university and educational space is possible. For this purpose, in this research, a mathematical optimization model is presented to allocate semester weeks to students of different disciplines in coronary-living conditions.
Methods: In this paper, to determine specific weeks for students of different disciplines during the semester in face-to-face education, a mathematical optimization model is proposed in the form of nonlinear programming with integer variables. In the objective function of the model, the distribution of students in the educational space during consecutive weeks during the semester should be done in such a way that it has the maximum possible dispersion to prevent the spread of coronary heart disease.
Results: This model has been implemented to allocate semester weeks to students of different majors at universities, in general, and its use has brought positive results for decision-makers, particularly at Allameh Tabataba’i University.
Conclusion: The results obtained from the implementation and execution of the model will bring a clear and positive perspective for decision-makers in universities. To continue this research, another optimization model should be designed and implemented for each faculty, taking into account the limitations of each department

Keywords


Ashokka, B., Ong, S. Y., Tay, K. H., Loh, N. H. W., Gee, C. F., & Samarasekera, D. D. (2020). Coordinated responses of academic medical centres to pandemics: Sustaining medical education during COVID-19. Medical Teacher, 42(7), 762-771.
Babaei, H., Karimpour, J., & Hadidi, A. (2015). A survey of approaches for university course timetabling problem. Computers & Industrial Engineering, 86, 43-59.
Bellio, R., Ceschia, S., Di Gaspero, L., Schaerf, A., & Urli, T. (2016). Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem. Computers & Operations Research, 65, 83-92.
Eiselt, H. A., & Sandblom, C. L. (2019). Methods for nonlinearly constrained problems. In Nonlinear Optimization (pp. 243-278). Springer, Cham.
Esmaelian, M., Abdollahi, S. (2017). Proposing a two-phase integer linear programming for university-course timetabling. Industrial Management, 9(1), 19-42. (in Persian)
Hossain, S. I., Akhand, M. A. H., Shuvo, M. I. R., Siddique, N., & Adeli, H. (2019). Optimization of university course scheduling problem using particle swarm optimization with selective search. Expert Systems with Applications, 127, 9-24.
Khodabakhshi-koolaee, A. (2020). Living in home quarantine: Analyzing psychological experiences of college students during Covid-19 pandemic. Journal of Military Medicine, 22(2), 130-138.
Mesiono, M. (2020). Peer Review dan Hasil Turnitin E-Learning Management of State Islamic University of North Sumatera In Pandemic Covid-19.
Peyravi, M., Marzaleh, M. A., Shamspour, N., & Soltani, A. (2020). Public education and electronic awareness of the new Coronavirus (COVID-19): Experiences from Iran. Disaster Medicine and Public Health Preparedness, 14(3), e5-e6.
Rachmadtullah, R., Rasmitadila, R., Humaira, M. A., Aliyyah, R. R., & Samsudin, A. (2020). Use of blended learning with Moodle: study effectiveness in elementary school teacher education students during the COVID-19 pandemic use of blended learning with Moodle: Study effectiveness in elementary school teacher education students during the COV. Int. J. Adv. Sci. Technol, 29(7).
Raja Murugadoss, J., & Krishna Kishore, K. (2020). Effectiveness of E-learning in rural India and significance of self-directed learning. International Journal of Advanced Science and Technology, 29(6), 6015-6020.
Rashidi, H., & Hassanpour, M. (2020). A deep-belief network approach for course scheduling. Journal of Applied Research on Industrial Engineering, 7(3), 221-237.
Salimifard, K., Jamali, G., Babaeezadeh, S. (2013). University course timetabling using graph-based hyper heuristics. Industrial Management, 5(2), 49-70. (in Persian)
Salimifard, K., Nakhaei, M., Zare, Z., Moghdani, R. (2018). Developing a multi-objective meta-heuristic algorithm to select work model and staff scheduling in petrochemical companies. Industrial Management, 10(4), 551-574. (in Persian)
Schreck, J., Baretton, G., & Schirmacher, P. (2020). Situation of the German university pathologies under the constraints of the corona pandemic-evaluation of a first representative survey. Der Pathologe.
Seyyedi, S., khatami, M., Amiri, M., Taghavi Fard, M. (2019). Positioning and optimized allocation of transfer points, hospitals and emergency services centers to organize a crisis relief chain, assuming screening of injuries. Industrial Management, 11(1), 1-20. (in Persian)
Song, K., Kim, S., Park, M. and Lee, H.S. (2017). Energy efficiency-based course timetabling for university buildings. Energy. 139(1), 394-405.