Abstract
University course timetabling is a complex and time-consuming duty that every educational institution faces regularly. It consists of scheduling a set of lectures in predefined time slots so as to avoid student conflicts, meet teacher and room availability, and manage several institution-specific operational rules. In this paper, we schedule courses based on a curriculum, that is, before the students’ registration. Unlike other curriculum-based models, the proposed model considers two practical aspects when managing the conflicts between lectures: (i) it schedules sections of subjects so that each section is evenly likely to be registered by the students, and (ii) it considers the failure rates and periodicity a subject is taught. We present a multi-objective integer programming model that maximizes the use of specific time slots, the symmetry in which the lectures of a course are scheduled during a week, and the flexibility for straggler students to take courses. The model is solved using commercial software, and it is applied to a real course-timetabling problem. We show the advantages of its use by comparing the model’s solution with the actual solution obtained by the manual scheduling.
Highlights
Educational institutions constantly face the problem of scheduling activities where students, professors, classrooms and other resources are involved
To evaluate the performance of the model, we scheduled the classes of the first semester of 2017 in the Faculty of Engineering at Universidad del Desarrollo, Chile
Our results show that the solution obtained by the proposed formulation of the course-timetabling problem outperformed, in all aspects, the current solution of a real instance of the problem, and successfully managed some practical issues not previously considered in the literature
Summary
Educational institutions constantly face the problem of scheduling activities where students, professors, classrooms and other resources are involved. A number of courses need to be scheduled over a set of time slots, subject to both capacity and operational constraints. The use of mathematical tools to assist in this type of problems has translated into a more efficient use of resources, a significant reduction in the time required to generate the timetable, and the possibility of quickly evaluating alternative scenarios [4]. The timetabling problem has been thoroughly addressed using operations research. While the different models available in the literature are tailored to the needs of specific institutions, they all divide, based on their own reality, the problem components into hard and soft constraints [3]
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