Design, Implementation and Automation of University Timetable Using a Multi-Server Queuing Model

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@article{1723496,
      author = {Sanvee Gael Kuakuvi, Eru, Akwuma Nathaniel, Ahiaba, Solomon, Atoyebi, Temitope Olufunmi, Prema Kirubakaran},
      title = {Design, Implementation and Automation of University Timetable Using a Multi-Server Queuing Model},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {4072-4093},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723496.pdf},
      abstract = {Manual entry of courses in order to form a timetable can be stressful, time-consuming, inefficient and also leads to scheduling conflicts. So, this study focuses on how to automate and generate a university timetable using a Multi-server Queuing Model so as to make doing the task efficient. The methodology will be centered on applying a multi-server queuing model for visualizing timetable creation as a virtual queue. This theoretical model will be implemented via a dedicated web application built on a Python-based technology stack: Streamlit for the creation of an interactive user interface, Pandas for data management and constraint handling, SimPy to power the core discrete-event simulation responsible for processing the queue and allocating the resources and the database will be handled using supabase. Output from the system, including the final timetable and performance analytics, is visualized dynamically with Plotly. Expected findings during the testing phase should confirm the system's efficacy, showing it was able to generate a fully optimized, conflict-free timetable in less than one minute, thus increasing lecture room occupancy significantly and eliminating any administrative errors. This study will provide a powerful and scalable solution for complex academic scheduling. Key recommendations are for academic institutions to adopt such automated systems to enhance operational efficiency and resource utilization. The recommended work based on this is to expand the model to involve more types of constraints and adapt the framework for other scheduling challenges, such as university examinations.},
      keywords = {university timetable automation; multi-server queuing model (M/M/c); discrete event simulation; timetable scheduling; resource optimization; SimPy; Streamlit; Pandas; Supabase; Plotly},
      month = {September},
  }