This study introduces a hybrid meta-heuristic for generating feasible course
timetables in large-scale scenarios. We conducted tests using our university's
instances. The current commercial software often struggles to meet constraints
and takes hours to find satisfactory solutions. Our methodology combines
adaptive large neighbourhood search, guided local search, variable
neighbourhood search, and an innovative instance decomposition technique.
Constraint violations from various groups are treated as objective functions to
minimize. The search focuses on time slots with the most violations, and if no
improvements are observed after a certain number of iterations, the most
challenging constraint groups receive new weights to guide the search towards
non-dominated solutions, even if the total sum of violations increases. In
cases where this approach fails, a shaking phase is employed. The decomposition
mechanism works by iteratively introducing curricula to the problem and finding
new feasible solutions while considering an expanding set of lectures.
Assignments from each iteration can be adjusted in subsequent iterations. Our
methodology is tested on real-world instances from our university and random
subdivisions. For subdivisions with 400 curricula timetables, decomposition
reduced solution times by up to 27%. In real-world instances with 1,288
curricula timetables, the reduction was 18%. Clustering curricula with more
common lectures and professors during increments improved solution times by 18%
compared to random increments. Using our methodology, viable solutions for
real-world instances are found in an average of 21 minutes, whereas the
commercial software takes several hours