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Erschienen in:
2024 | OriginalPaper | Buchkapitel
Exploring the Potential of Genetic Algorithms for Optimizing Academic Schedules at the School of Mechatronic Engineering: Preliminary Results
verfasst von : Johan Alarcón, Samantha Buitrón, Alexis Carrillo, Mateo Chuquimarca, Alexis Ortiz, Robinson Guachi, D. H. Peluffo-Ordóñez, Lorena Guachi-Guachi
Erschienen in: Applied Informatics
Verlag: Springer Nature Switzerland
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Abstract
The generation of schedules is a complex challenge, particularly in academic institutions aiming for equitable scheduling. The goal is to achieve fair and balanced schedules that meet the requirements of all parties involved, such as workload, class distribution, shifts, and other relevant criteria. To address this challenge, a genetic algorithm specifically designed for optimal schedule generation has been proposed as a solution. Adjusting genetic algorithm parameters impacts performance, and employing parameter optimization techniques effectively tackles this issue. This work introduces a genetic algorithm for optimal schedule generation, utilizing suitable encoding and operators, and evaluating quality through fitness techniques. Optimization efforts led to reduced execution time, improved solution quality, and positive outcomes like faster execution, fewer generations, increased stability, and convergence to optimal solutions.