TOOL DEVELOPMENT FOR UNIVERSITY COURSE TIMETABLING PROBLEM CASE STUDY: ITB UNIVERSITY COURSE TIMETABLING

University timetabling in ITB generally submitted by each Department (Prodi). But for first <br /> <br /> <br /> level course (TPB) and all public lecture scheduled by Directorate of Education (Dirdik). This <br /> <br /> <br /> can make a problem because many...

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Bibliographic Details
Main Author: SUCIPTO, LUBIS
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/22922
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Institution: Institut Teknologi Bandung
Language: Indonesia
Description
Summary:University timetabling in ITB generally submitted by each Department (Prodi). But for first <br /> <br /> <br /> level course (TPB) and all public lecture scheduled by Directorate of Education (Dirdik). This <br /> <br /> <br /> can make a problem because many course clashed because it is regulated by each Department. <br /> <br /> <br /> Also the schedule are not evenly distributed where some students with dense schedule on a <br /> <br /> <br /> certain day and empty the other days. <br /> <br /> <br /> Timetabling algorithm that will be implemented is greedy algorthm and genetic algorithm. Both <br /> <br /> <br /> algorthm was optimization algorithm that often used for searching solutions for any problem. All <br /> <br /> <br /> problem from ITB university course timetabling will be analyzed to be constraint for each <br /> <br /> <br /> algorthm while finding the solution. <br /> <br /> <br /> The result from this final project is that algorthm can make schedule with optimal whre have <br /> <br /> <br /> least constraint violation. The average of vitness value form greedy algorithm result is 0.967. <br /> <br /> <br /> While the genetic algorthm result in 100th <br /> <br /> <br /> , 300th, and 500th generation is 0.986, 0.913 and 0.939. <br /> <br /> <br /> The result from combining two algorthms where initiation of genetic used result from greedy <br /> <br /> <br /> algorithm is more optimal and get better result. The average fitness value from 100th generation <br /> <br /> <br /> is 0.986. <br /> <br /> <br /> From the result, the most constraint violation in the solution is where course that have some <br /> <br /> <br /> course number with same Departement participant must scheduled at the same time. This is <br /> <br /> <br /> because the lack of available room. Adding the available room will make the better result from <br /> <br /> <br /> the tool.