2025 Asian Conference on Communication and Networks (ASIANComNet 2025)

The creation of timetables is a complex combinatorial problem involving numerous hard and soft constraints. Traditional methods often prove ineffective when the number of subjects, teachers and student groups increases. This paper proposes a genetic algorithm with weighting coefficients for multi-criteria optimisation, which creates timetables by balancing preferences for time, days, halls and workload distribution. The approach uses a multi-criteria fitness function in which the weights are extracted through surveys, allowing for a balanced satisfaction of preferences for time, days, halls, workload distribution and minimisation of “free windows”. The experiments conducted show that the algorithm ensures a high percentage of satisfaction of the criteria, no violations of hard constraints, and practical applicability in medium-sized higher education institutions. A block diagram of the algorithm is presented, and the results are discussed in terms of its efficiency, flexibility, and potential for future extensions, including dynamic weight adjustment and integration with machine learning.

genetic algorithm,multi-criteria optimization,timetabling,weighted coefficients,constraint satisfaction