Optimising Planned Academic Workload Distribution: A Multiobjective Approach for the Balanced Academic Curriculum Problem

Designing academic curricula in universities is crucial for optimising student workload distribution. Excessive academic workload could negatively affect students' performance. The Balanced Academic Curriculum Problem (BACP) involves distributing the academic workload evenly across periods while prerequisites are satisfied according to an optimisation criterion. Traditionally, curriculum managers have manually balanced workloads, a time-consuming process often resulting in suboptimal distributions. Since different conflicting criteria have been proposed in the literature, this work considers a multi-objective approach to the BACP. Specifically, we designed and implemented three multi-objective metaheuristics: Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-objective Simulated Annealing (MOSA) and Multi-objective Greedy Algorithm (MOGA), utilising datasets from literature and real-engineering cases in Chile. The results show the superior effectiveness of the NSGA-II algorithm, highlighting the potential of a resolution method to support curriculum design and optimisation in higher education. This approach enhances the student experience by creating manageable and balanced academic workloads.