Adaptive Heuristic-Based Ant Colony Optimization for Multi-Constraint University Course Timetabling with Morning Slot Preference for Energy Efficiency

Imam  Muslem; Irvanizam Irvanizam; Almuzammil Almuzammil; Farhana  Johar

doi:10.52436/1.jutif.2025.6.6.5588

Authors

Imam Muslem Informatics, Universitas Almuslim Bireuen, Indonesia
Irvanizam Informatics, Universitas Syiah Kuala, Indonesia
Almuzammil Informatics, Universitas Almuslim Bireuen, Indonesia
Farhana Johar Mathematical Sciences, Universiti Teknologi Malaysia, Malaysia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.6.5588

Keywords:

Ant Colony Optimization, Course Timetabling, Energy Efficiency, Multi-Constraint Optimization, Scheduling

Abstract

University course timetabling is a well-known NP-hard combinatorial optimization problem that involves multiple interacting constraints, including lecturer availability, classroom capacity, time-slot allocation, and course duration. Most existing metaheuristic-based approaches primarily focus on eliminating academic conflicts, while contextual and operational aspects, such as energy efficiency, are rarely considered explicitly. In addition, standard Ant Colony Optimization (ACO) methods often suffer from premature convergence and limited adaptability during the solution search process. This study proposes an Adaptive Heuristic-Based Ant Colony Optimization (AHB-ACO) approach for multi-constraint university course timetabling with a particular emphasis on morning slot preference as an energy efficiency proxy. The proposed method extends the conventional ACO framework by integrating an adaptive heuristic mechanism that dynamically guides the solution construction process toward compact and conflict-free schedules, while simultaneously favoring morning time slots to support reduced classroom cooling demand. Hard constraints, including lecturer and room conflicts, are strictly enforced, whereas the temporal preference is modeled as a soft constraint. The performance of AHB-ACO is evaluated through extensive scheduling simulations using academic datasets under various parameter settings. Experimental results demonstrate that the proposed approach consistently produces conflict-free timetables, achieving a conflict function value of C(S)=0 with stable convergence behavior. Furthermore, parameter sensitivity analysis indicates that AHB-ACO exhibits good robustness with respect to variations in the number of ants and iterations, showing a reasonable trade-off between solution quality and computational time. Additional analysis reveals an increased utilization of morning time slots compared to non-optimized schedules, indicating the effectiveness of the proposed energy-aware preference. Overall, the results suggest that AHB-ACO provides an effective and adaptive solution for university course timetabling that not only satisfies academic constraints but also addresses operational considerations related to energy efficiency.

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