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Solving the Vietnamese EdTech Timetabling Problem with Optimized Multi-Objectives

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Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15431))

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Abstract

Online teaching has become increasingly popular in recent years, particularly after Covid-19, in response to educational difficulties such as teacher shortages and remote learning. This paper proposes Adjusted Non-dominated Sorting Genetic Algorithm III (aNSGA-III) to overcome the poor convergence of Non-dominated Sorting Genetic Algorithm III (NSGA-III) in optimizing teaching timetables based on practical constraints such as class demand and teacher availability for an EdTech company in Vietnam (VET). By improving on mutation parameters, termination conditions, and the application of parallel processing has increased the algorithm's efficiency and accuracy. Furthermore, our empirical experiments demonstrate significant practical and academic contributions by cutting scheduling time up to 40% and operational costs in half. The findings highlight the potential of the automated management information system in order to enhance the efficiency and quality of timetable management in the EdTech sector.

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Acknowledgments

We acknowledge Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study.

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet, District 10, Ho Chi Minh City, Vietnam

    Cong Canh Le, Thi Ai Thao Nguyen, Minh Quang Tran & Trong Nhan Phan

  2. Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam

    Cong Canh Le, Thi Ai Thao Nguyen, Minh Quang Tran & Trong Nhan Phan

Authors
  1. Cong Canh Le
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  2. Thi Ai Thao Nguyen
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  3. Minh Quang Tran
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  4. Trong Nhan Phan
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Corresponding author

Correspondence to Trong Nhan Phan .

Editor information

Editors and Affiliations

  1. Mahasarakham University, Mahasarakham, Thailand

    Chattrakul Sombattheera

  2. Duke Kunshan University, Kunshan, China

    Paul Weng

  3. University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Jun Pang

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Le, C.C., Nguyen, T.A.T., Tran, M.Q., Phan, T.N. (2025). Solving the Vietnamese EdTech Timetabling Problem with Optimized Multi-Objectives. In: Sombattheera, C., Weng, P., Pang, J. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2024. Lecture Notes in Computer Science(), vol 15431. Springer, Singapore. https://doi.org/10.1007/978-981-96-0692-4_27

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  • DOI: https://doi.org/10.1007/978-981-96-0692-4_27

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-96-0691-7

  • Online ISBN: 978-981-96-0692-4

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