USING A GENETIC ALGORITHM TO SOLVE THE COURSES TIMETABLING CREATION PROBLEM

Abstract

Creating of courses timetable is an extremely difficult, time-consuming task and usually takes a long time. In many educational institutions, the courses schedule is developed manually. Schedule theory includes problems that are actually less complex than problems in practice, but theoretical analysis provides a fundamental understanding of the complexity of the schedule. The logical result is that the schedule is very difficult to build in practice due to many constraints [1]. Scheduling courses is a planning problem. In 1996, the problem of scheduling was described as the allocation of some resources with restrictions on a limited number of time intervals and at the same time to satisfy the set of stated objectives [2]. This is a general statement and is a common description of the courses timetabling creation problem. Schedule of courses is an important administrative activity in most educational institutions. The timetable problem is the distribution of classes by available audiences and time intervals, taking into account the constraints. We usually distinguish between two types of constraints: hard and soft. Hard constraints are compulsorily fulfilled by the educational institution. Decisions that do not violate hard constraints are called possible solutions. With the development of the general theory of the schedule, the approaches to the formalization and solution of the courses timetabling creation problem in educational institutions also changed. Currently, the problem of automation of the courses timetabling creation remains relevant. The urgency of the problem is determined by the growing requirements for the quality of education, student work planning, rational use of the audiences, as well as taking into account additional optimization parameters. The task of finding the optimal schedule of courses in most cases belongs to the class of complex problems. If we take into account the real conditions, the problem is even more complicated, because the desired solutions must meet numerous constraints of production, organizational and psychophysiological nature, which contradict each other. The genetic algorithm helps to efficiently search for optimal solutions in spaces with a very large dimension.

Keywords

• automated control system
• automation
• genetic algorithm
• crossover
• mutation
• optimization
• scheduling