Automatic University Course Scheduling Using A Multi-Constraint Genetic Algorithm For Room and Time Optimization
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Abstract
University course scheduling required simultaneous handling of resource conflicts, room capacity, and schedule quality. This study developed a Multi-Constraint Genetic Algorithm (MCGA) for automatic course scheduling using institutional data from STMIK Widya Cipta Dharma. The final dataset contained 141 optimized course offerings, 12 candidate rooms, five scheduling days, and six nominal time slots; room-capacity compliance was evaluable for 129 offerings (91.49%). A Standard Genetic Algorithm and MCGA were evaluated using identical feasibility domains and hard-conflict repair mechanisms. One-factor-at-a-time screening selected a population of 100, 500 generations, a crossover rate of 0.80, and a mutation rate of 0.02. Across 20 paired runs, both algorithms achieved zero lecturer, room, and cohort conflicts, with zero capacity excess among course offerings for which validated class-size data were available. MCGA increased mean room utilization from 0.6085 to 0.7800 and reduced mean cohort-day time gaps from 11.85 to zero. Paired Wilcoxon tests confirmed significant improvements in room utilization and time-gap reduction. The results showed that hierarchical feasibility handling combined with weighted post-feasibility objectives improved the quality of feasible university schedules without a material runtime penalty.
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Copyright (c) 2026 Sapriansyah, Heny Pratiwi, Ahmad Fahrijal Pukeng
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