AUTOMATED COLLEGE TIMETABLE GENERATOR SYSTEM

Scheduling course timetables for a large array of courses is a very complex problem which often has to be solved manually by the center staff even though results are not always fully optimal. Timetabling being a highly constrained combinatorial problem, this work attempts to put into play the effectiveness of evolutionary techniques based on Darwin's theories to solve the timetabling problem if not fully optimal but near optimal. Genetic Algorithm is a popular meta-heuristic that has been successfully applied to many hard combinatorial optimization problems which includes timetabling and scheduling problems. In this work, the course sets, halls and time allocations are represented by a multidimensional array on which a local search is performed and a combination of the direct representation of the timetable with heuristic crossover is made to ensure that fundamental constraints are not violated. Finally, the genetic algorithm was applied in the development of a viable timetabling system which was tested to demonstrate the variety of possible timetables that can be generated based on user specified constraint and requirements. I. Introduction Timetabling concerns all activities with regard to making a timetable that must be subjective to different constraints. According to Collins Concise Dictionary (4th Edition) " a timetable is a table of events arranged according to the time when they take place. " A critical factor in running a university or essentially an academic environment is the need for a well-planned and clash-free timetable. Back in the times when technology was not in wide use, academic timetables were manually created by the educational center staff. Every school year, institutions of education face the rigorous task of drawing up timetables that satisfies the various courses and their respective examinations being offered by the different department. The difficulty is due to the great complexity of the construction of timetables for lectures and exams, due to the scheduling size of the lectures and examinations periods and the high number of constraints and criteria of allocation, usually circumvented with the use of little strict heuristics, based on solutions from previous years (Jose, 2008). Nowadays, this process has been simplified by semi-automatic solutions based on timetable generation applications (e.g. Open Course Timetabler). A timetable management system is designed and created to handle as much course data as fed while ensuring the avoidance of redundancy. An educational timetable must meet a number of requirements and should satisfy the desires of all entities involved simultaneously as well as possible. The timing of events must be such that nobody has more than one event at the same time (Robertus, 2002). The proposed timetabling system is designed to handle events of course lectures offered at a university (university course timetabling). Based on the above event, the system would have only one module which is the Course Lecture Timetable Module.

International Journal of Engineering Research and, 2020

Timetable generation is a very burdensome and time consuming task. This is usually done ‘by hand’, taking several days or weeks of iterative repair. Timetable generation is the NP-hard problem, which is very difficult to solve using conventional methods. A highly constrained timetabling problem can also be solved by evolutionary techniques. We must determine an acceptable assignment of the time slots and rooms to the courses based on a variety of their requirements. This project will try to reduce the difficulties of generating timetable by using Genetic Algorithm. By using Genetic algorithm, the time required to generate time table will be reduced and the generated timetable will be more accurate, precise and free of human errors. Main goal is to minimize the number of conflicts in the timetable. System generates timetable for each class and faculty, in keeping with the availability calendar of teachers, availability and capacity of physical resources (such as classrooms, laborator...

CERN European Organization for Nuclear Research - Zenodo, 2022

Scheduling course timetables for a large array of courses is a very complex problem which often has to be solved manually by the center staff even though results are not always fully optimal. Timetabling being a highly constrained combinatorial problem, this work attempts to put into play the effectiveness of evolutionary techniques based on Darwin’s theories to solve the timetabling problem if not fully optimal but near optimal. Genetic Algorithm is a popular meta-heuristic that has been successfully applied to many hard combinatorial optimization problems which includes timetabling and scheduling problems. In this work, the course sets, halls and time allocations are represented by a multidimensional array on which a local search is performed and a combination of the direct representation of the timetable with heuristic crossover is made to ensure that fundamental constraints are not violated. Finally, the genetic algorithm was applied in the development of a viable timetabling system which was tested to demonstrated the variety of possible timetables that can be generated based on user specified constraint and requirements.

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.

Timetable creation is a very arduous and time consuming task. To create timetable it takes lots of patience and man hours. Time table is created for various purposes like to organize lectures in school and colleges, to create timing charts for train and bus schedule and many more. To create timetable it requires lots of time and man power .In our paper we have tried to reduce these difficulties of generating timetable by Genetics Algorithm. By using Genetic algorithm we are able to reduce the time require to generate time table and generate a timetable which is more accurate, precise and free of human errors. The first phase contains all the common compulsory classes of the institute, which are scheduled by a central team. The second phase contains the individual departmental classes. Presently this timetable is prepared manually, by manipulating those of earlier years, with the only aim of producing a feasible timetable.

Jordanian Journal of Computers and Information Technology, 2017

2014

Timetable creation is very burdening and time consuming task. Scheduling course timetables for a large array of courses is very complicated problem which often has to be solved by the center staff manually even though the result are not always satisfying. In this paper Genetic Algorithm along with heuristic approach is used to schedule timetable which includes various entities of university such as rooms, curriculum, teachers, time on which a local search is performed and it also ensures that fundamental constraints are not violated. Genetic Algorithm are heuristic search algorithm which is based on the ideas of natural selection. The algorithm repeatedly mutates a population of individual solutions. In each step, the algorithm calculates the fitness function of every individual and selects them randomly from the population to produce children for the next generation. Every successive generation drives towards an optimal solution.

International Journal of Innovative Computing, 2021

Current timetable scheduling system in School of Computing(SC), Universiti Teknologi Malaysia(UTM) is done manually which consumes time and human effort. In this project, a Genetic Algorithm (GA) approach is proposed to aid the timetable scheduling process. GA is a heuristic search algorithm which finds the best solution based on current individual characteristics. Using GA and scheduling info such as rooms available and timeslots needed, it is shown that scheduling can be done more efficiently, with less time, effort and errors. As a testbed, a web application is developed to maintain records needed and generate timetables. Introduction of GA helps in generating a timetable automatically based on information such as rooms, subjects, lecturers, student group and timeslot. GA reduces human error and human efforts in the timetable scheduling process.