Solving University Course Timetabling Problems by a Novel Genetic Algorithm Based on Flow

Abstract

Since the University Course Timetabling Problem (UCTP) is a typical sort of combinatorial issues, many conventional methods turn out to be unavailable when confronted with this complex problem where lots of constraints need to be satisfied especially with the class-flow between floors added. Considering the supreme density of students between classes, this paper proposes a novel algorithm integrating Simulated Annealing (SA) into the Genetic Algorithm (GA) for solving the UCTP with respect to the class-flow where SA is incorporated into the competition and selection strategy of GA and concerning the class-flow caused by the assigned timetable, a modified fitness function is presented that determines the survival of generations. Moreover, via the exchange of lecturing classrooms the timetable with minimum class-flow is eventually derived with the values of defined fitness function. Finally, in terms of the definitions above, a simulation of virtual situation is implemented and the experimental results indicate that the proposed model of classroom arrangement in the paper maintains a high efficiency.