Investigation on Cooling Schemes and Parameters of Simulated Annealing for Timetabling University Courses

Every academic year, educational institutions usually have a problem related to course timetabling solved by academic staff with/without scheduling tool. The course timetabling is known to be NP-hard problem, which means that it is often impossible to provide exact solutions within a reasonable time limit. A feasible timetable must be satisfied by all hard constraints whilst soft constraints are not absolutely essential. This paper presents simulated annealing algorithm applied to solve university course timetabling problem. The algorithm includes a repair process, which ensures that all infeasible timetables that may be produced during neighbourhood search process are rectified. The algorithm evaluates the violation of four soft constraints. The computational experiment using a dataset obtained from a collaborative university was aimed to identify the appropriate setting of the cooling schedules, neighbourhood search strategies and parameters required by the proposed method. The analysis on the experimental results indicated that some of the findings were not in agreement with previous research.