<![CDATA[Uneven distribution and assignment of public elementary school teachers in Magelang Regency lead to long daily commute distances from teachers' residences to schools. This condition negatively affects teachers' physical condition, psychological fatigue, and teaching effectiveness. This study aims to optimize the assignment of public elementary school teachers in Magelang Regency by minimizing the total daily commute distance using the Ant Colony Optimization (ACO) algorithm, specifically the Ant System variant with an ant-cycle pheromone update scheme. The dataset comprises 636 teachers and 106 public elementary schools (6 teachers assigned per school) with 67,416 distance pairs calculated using the Haversine formula based on geographic coordinates. Parameter tuning shows that α = 4, β = 5, and evaporation rate ρ = 0.1 yield the most consistent optimal search performance. In the ant quantity (M) and iteration tests, M = 50 with 1,000 iterations produced the shortest average total distance of 4,304.874 km with an execution time of 763 seconds. Meanwhile, M = 30 with 1,000 iterations provided high computational efficiency with an average total distance of 4,305.688 km and execution time of 460 seconds (~39.7% faster). This study demonstrates that ACO is effective in solving large-scale teacher assignment optimization problems and serves as a decision-support framework for local education authorities. ]]>
Copyrights © 2026
