A Heuristic Approach to Course Scheduling Problem (original) (raw)
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AUTOMATED UNIVERSITY LECTURE TIMETABLE USING HEURISTIC APPROACH
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DEVELOPING A COURSE TIMETABLE SYSTEM FOR ACADEMIC DEPARTMENTS USING GENETIC ALGORITHM
Preparing course timetables for universities is a search problem with many constraints. Exhaustive search techniques in theory can be used to develop course timetables for academic departments, but unfortunately these techniques are computation intensive, since the search space is very large and therefore are impractical. In this paper, Genetic Algorithms (GA's) are utilized to build an automated course timetable system. The system is designed for any academic department. The proposed timetabling system requires minimal effort from the administration staff to prepare the course timetable. Moreover, the prepared course timetable considers faculties' desires, students' needs and available resources, such as classrooms and laboratories with optimal utilization. The proposed timetabling process was divided into three stages. The first stage is the data collection stage. In this stage, the administrative staff; usually the head of the department, is responsible for preparing the required data, such as the names of the faculty personnel and their desires of courses and laboratories ordered with some priority scheme. Number and type of theoretical and practical courses are also fed to the system based on some statistics about student numbers and previous course timetable history. The system is also fed with number of lecture rooms allocated for the department and number of labs with information about theoretical courses they are able to serve. In the second stage, the program generates an initial set of suggested schedules (chromosomes). Each chromosome represents a solution to the problem, but usually is not satisfactory. Finally, the proposed timetabling system starts the search for a good solution that satisfies best interests of the department according to a cost function. GA is applied in search for a satisfactory course timetable based on a pre-defined criterion. The system has been developed and tested utilizing benchmarked datasets developed by an international timetabling competition (ITC2007) and for the Computer Engineering Department at Yarmouk University. In both cases, the algorithm showed very satisfactory results.
An Automatic Course Scheduling Approach Using Instructors’ Preferences
Abstract University Courses Timetabling problem hasbeen extensively researched in the last decade. Therefore,numerous approaches were proposed to solve UCT prob-lem. This paper proposes a new approach to process asequence of meetings between instructors, rooms, andstudents in predefined periods of time with satisfying a setof constraints divided in variety of types. In addition, thispaper proposes new representation for courses timetablingand conflict-free for each time slot by mining instructorpreferences from previous schedules to avoid undesirabletimes for instructors. Experiments on different real datashowed the approach achieved increased satisfaction degreefor each instructor and gives feasible schedule with satisfy-ing all hard constraints in construction operation. Thegenerated schedules have high satisfaction degrees compar-ing with schedules created manually. The research conductsexperiments on collected data gathered from the computerscience department and other related departments in Jor-dan University of Science and Technology- Jordan
Discrete Dynamics in Nature and Society
University class scheduling problem is one of the most important and complex issues in the academic field. This problem is recognized as one of the NP-HARD issues due to its various limitations. On the contrary, genetic algorithms are commonly used to solve NP-HARD problems, which is one of the decision-making problems and is basically one of the most fundamental classes of complexity. The university course planning includes severe constraints such as classroom, classroom curriculum, and faculty. At the same time, some soft constraints should be considered, such as student and faculty preferences and favorite class time. In this research, as a novel contribution, an integer model for scheduling university classes is presented. In this model, the preferences of professors and students are in accordance with the satisfaction values obtained through questionnaires. Moreover, a genetic algorithm has been developed to solve the model. The results show that the classroom timeline by this ...