A Review of Computational Solution Strategies for the Balanced Academic Curriculum Problem Optimization
Mohd Fadzil Faisae Ab. Rashid and Muhamad Zuhairi Sulaiman
Universiti Malaysia Pahang Al-Sultan Abdullah, Malaysia
Volume 19: 2025, pp. 179-198; ABSTRACT
The balanced academic curriculum problem (BACP) involves scheduling courses
over academic periods to balance workloads and credits. This paper reviews existing research
on optimization approaches for BACP. The review focuses on examining problem formulations
and solution methods applied to balance curriculum plans. The aim is to summarize the progress
in BACP optimization research. Different modeling techniques like integer programming and
constraint programming have been employed for BACP optimization. On the other hand, a
variety of metaheuristics, including genetic algorithms, tabu search, and ant colony optimization
have been proposed to handle the curriculum balancing problem. One of the popular approaches
to optimize BACP is hybrid algorithms that combine metaheuristics with local search methods.
Based on the review, future BACP research should emphasize developing more flexible models
that can accommodate individual student needs and choices. There is also scope for exploring
the potential of new metaheuristics for handling the complexity of personalized curriculum
optimization. In summary, this review synthesizes key findings on optimization approaches for
BACP and identifies promising directions for further research.
Keywords: balanced academic load, academic load, student academic load, curriculum optimization.
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