Space allocation for examination scheduling using Genetic Algorithm / Alya Kauthar Azman
Space allocation management for examinations at UiTM Cawangan Terengganu Kampus Kuala Terengganu (UiTMCTKKT) presents a complicated task that demands analyzing multiple limitations alongside various elements to ensure proper resource utilization and conflict reduction. This study applies Genetic Alg...
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| Format: | Thesis |
| Language: | en |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/114925/1/114925.pdf https://ir.uitm.edu.my/id/eprint/114925/ |
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| Summary: | Space allocation management for examinations at UiTM Cawangan Terengganu Kampus Kuala Terengganu (UiTMCTKKT) presents a complicated task that demands analyzing multiple limitations alongside various elements to ensure proper resource utilization and conflict reduction. This study applies Genetic Algorithms (GA) to optimize space distribution for test scheduling, addressing the challenge of managing multiple test sessions across distinct locations. The research adopts a Genetic Algorithm-based approach, where examination scheduling details such as date, time, course code, program code, student group, student numbers, and available spaces are encoded as chromosomes. The system generates and evaluates potential schedules using fitness functions, selection, crossover, and mutation operators to iteratively improve scheduling efficiency. Data for the study was collected from university records, and algorithm performance was tested against predefined scheduling criteria.
The proposed system successfully optimized examination space allocation by significantly reducing scheduling conflicts and improving resource utilization. Compared to manual scheduling methods, the automated system reduced scheduling time and errors while achieving better space management efficiency. The results demonstrated that the Genetic Algorithm approach effectively balances examination load and minimizes room underutilization. To further enhance the scheduling system, future work should focus on integrating additional optimization techniques such as Particle Swarm Optimization or Simulated Annealing to refine scheduling accuracy. Implementing real-time data updates through cloud-based platforms could further improve system scalability. Additionally, a user-friendly interface for administrative staff would enhance interaction and usability. Expanding the dataset for training and evaluation would strengthen the modeTs robustness, ensuring better adaptability to dynamic scheduling constraints. |
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