Improving Academic Plan Efficiency with Genetic Algorithm and Stochastic Simulation

Efficient course scheduling in higher education institutions is a complex problem that directly impacts student retention and terminal efficiency. This research explores an optimization model that integrates Genetic Algorithms (GA) and Monte Carlo Simulation to enhance academic planning. The proposed approach assigns courses dynamically, considering constraints such as faculty availability, student demand, and institutional policies. Markov Chains and the Metropolis-Hastings Algorithm are employed to model enrollment patterns and predict scheduling outcomes. By simulating multiple scenarios, the model identifies optimal schedules that maximize course availability while minimizing conflicts. The results demonstrate that our methodology improves student progression rates and reduces scheduling inefficiencies of the current assignment. Additionally, our approach provides a flexible framework that can be adapted to different institutional contexts. The findings contribute to the development of intelligent scheduling systems that support data-driven decision-making in higher education administration.