Kasunpa Abeywardena . Gayan Dilantha Illeperuma

Department of Mathematics, University of Colombo, Sri Lanka. Email: kasunpaabeywar- dena@gmail.com, Department of Physics, Open University of Sri Lanka, Email: g.d.illeperuma@gmail.com

Received in final form on January 22, 2022

Abstract
Timetabling is the process of allocating time for planned activities orderly, to bring an optimum solution, without violating any hard constraints and with minimum soft constraint violations. It portrays a difficult optimization problem. There exists many commercial software with general features for this task. However, those cannot be adopted into The Open University of Sri Lanka due to its unique, complex requirements. This research paper presents a genetic algorithm-based MATLAB program which, automatically generates a semester-long, optimized timetable and eliminates the current, time-consuming, manual process. The algorithm considers, the number of levels in a degree program, credit hours, days and time slot fixeded slots, clash avoidance, holidays, lecturer preferences, and workload distributions. The genetic algorithm consists of four main steps: initialization, validation, fitness calculation, and mutation. The solution space is represented as a four-dimensional matrix. Rows, columns, and planes represented slots per day, days in a semester, and levels. Fourth dimension represented different solutions. To measure the performance, a point system was devised where violation of each constraint was penalized and vice versa. Based on this point system, a theoretical maximum was calculated without considering the feasibility of achieving all constraints simultaneously. In this study, algorithm reached a maximum fitness value of 100 without violating any hard constraints. Whereas the theoretical maximum was 126. Repair strategies were implemented to improve the performance which reduced the execution time from 90 to 14 minutes. The results show that it is possible to generate an optimized timetable consistently using this method.

Keywords
Timetabling Problem, Scheduling, Genetic Algorithm, Optimiza- tion, Fitness function, Mutation, Applications of Genetic Algorithm.

Cite This Article
Kasunpa Abeywardena and Gayan Dilantha Illeperuma, A Genetic Algorithm for University Timetabling, J. Innovation Sciences and Sustainable Technologies, 2(1)(2022),1-9. https://doie.org/10.0608/JISST.2022481277

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