MERKEZİ SINAVLARDA YAŞANAN SINAV ÇİZELGELEME PROBLEMLERİNİN GENETİK ALGORİTMALAR İLE ÇÖZÜLMESİ

Year 2018, Volume: 4 Issue: 1, 102 - 115, 27.06.2018

Murat Dener , M. Hanefi Calp

https://doi.org/10.22531/muglajsci.423185

Cited By: 5

Abstract

Bir sınav çizelgeleme uygulamasından beklenen
kaynakların verimli kullanımıdır. Kaynakları verimli kullanabilmek ve en kısa
zamanda en az maliyetle bütün sınavların gerçekleştirilmesi için çeşitli
kıstaslar vardır. Yapılan çalışma ile bu tür kıstaslara sahip eğitim kurumlarının,
merkezi sınav organizasyonlarını başarıyla gerçekleştirmesi amaçlanmıştır.
Çalışmada, iki aşamalı genetik algoritma geliştirilmiştir. Birinci aşamada
derslerin oturumlara atanması işlemi, ikinci aşamada ise ilgili oturumda sınava
katılacak öğrencilerin sınav salonlarına atanması işlemi gerçekleştirilmiştir.
Oturumlara katılan ortak öğrenci sayısının arttırılması, aynı oturumda asgari
bina kullanımı, mümkün olan en az sayıda sınıf-sıra kullanılarak gözetmen
sayısının azaltılması yapılan çalışmanın amaçlarını oluşturmaktadır. Yapılan bu
çalışmada eğitim kurumlarına yönelik genel amaçlı sınav çizelgeleme çözümü
sunulmuştur. Geliştirilen sistem farklı merkezi sınavlar içinde
kullanılabilmesiyle özgünlük oluşturmaktadır. Örnek uygulama sonuçlarına
bakıldığında önerilen genetik algoritmanın başarılı sonuçlar verdiği
görülmektedir.

Keywords

Merkezi Sınavlar, Genetik Algoritma, Sınav Planlama Problemi, Çözme

SOLVING THE EXAM SCHEDULING PROBLEMS IN CENTRAL EXAMS WITH GENETIC ALGORITHMS

Abstract

It is the efficient use of resources expected from an exam scheduling application. There are various criteria for efficient use of resources and for all tests to be carried out at minimum cost in the shortest possible time. It is aimed that educational institutions with such criteria successfully carry out central examination organizations. In the study, a two-stage genetic algorithm was developed. In the first stage, the assignment of courses to sessions was carried out. In the second stage, the students who participated in the test session were assigned to examination rooms. Purposes of the study are increasing the number of joint students participating in sessions, using the minimum number of buildings in the same session, and reducing the number of supervisors using the minimum number of classrooms possible. In this study, a general purpose exam scheduling solution for educational institutions was presented. The developed system can be used in different central examinations to create originality. Given the results of the sample application, it is seen that the proposed genetic algorithm gives successful results.

Keywords

Central Exams, Genetic Algorithm, Exam Scheduling Problem, Solving

References

• 1. Zhang L., Lau S.K., “Constructing university time table using constraint satisfaction programming approach”, Proceedings of the 2005 International Conference on Computational Intelligence for Modelling, Control andAutomation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, Vienna, Austria, 55-60, (2005).
• 2. Bulut, F., Subaşı, Ş., “Best Seating Plan for Central Exams Using Genetic Algorithms”, Dokuz Eylül University Faculty of Engineering Science and Engineering Journal, Vol 17(3), 122-137, (2015).
• 3. Yaldır, A., Baysal, C., "Developing Examination Scheduling Automation System By Using Evolutionary Computing Technique", Pamukkale University Journal of Engineering Sciences, Vol 18 (2), 105-122, (2012).
• 4. Özdağ, H., Aygör, N., Parlak, A., “On the Timeline of the Ant Colony Algorithm: A Modeling and Its Application”, Academic Informatics12 - XIV. Academic Informations Conference Reports, 329-333, (2012).
• 5. İlkuçar, M., “Optimization of Examination Scheduling Problem and an Application Software”, Academic Informatics’11, 413-420, (2011).
• 6. Rozaimee, A., Shafee, A. N., Hadi, N. A. A., & Mohamed, M. A., “A Framework for University’s Final Exam Timetable Allocation Using Genetic Algorithm”, World Applied Sciences Journal, 35(7), 1210-1215, (2017).
• 7. Shatnawi, A., Fraiwan, M., & Al-Qahtani, H. S., “Exam scheduling: A case study”, In Advanced Computational Intelligence (ICACI), 2017 Ninth International Conference on (pp. 137-142). IEEE, (2017).
• 8. Moreira, J. J. , “A system for automatic construction of exam timetable using genetic algorithms”, Tékhne-Revista de Estudos Politécnicos, (9), 319-336, (2008).
• 9. Mandal, A. K., & Kahar, M. N. M., “Solving examination timetabling problem using partial exam assignment with great deluge algorithm”, In Computer, Communications, and Control Technology (I4CT), 2015 International Conference on(pp. 530-534). IEEE, (2015, April).
• 10. Gershil, M.,Palamutçuoğlu, T., “Performance Analysis of Course Scheduling Problem by Hybrid Genetic Algorithms”, Niğde University Faculty of Economics and Administrative Sciences Journal, Vol 6(1), 242-262, (2013).
• 11. Jha, S.K., “ExamTimetabling Problem Using GeneticAlgorithm”, International Journal of Research in EngineeringandTechnology, Volume: 03 Issue: 05, 649-654, (2014).
• 12. Mahto, M.K., Kumar, L., “Exam Time Table Scheduling using Genetic Algorithm”, International Journal of Enhanced Research in Management &Computer Applications, Vol. 4 Issue 8, 31-35, (2015).
• 13. Cupic, M.,Golub, M., Jakobovic, D., “Exam Timetabling Using Genetic Algorithm”, Proceedings of the ITI 2009 31st Int. Conf. on Information Technology Interfaces, Cavtat, Croatia, 357-362, (2009).
• 14. Cavdur, F., Kose, M.,"A Fuzzy Logic and Binary-Goal Programming-Based Approach for Solving the Exam Timetabling Problem to Create a Balanced-Exam Schedule", International Journal of Fuzzy Systems, Vol. 18 Issue: 1, 119-129, (2016).
• 15. Hassan, M.A., Hassan, O.A.,"Constraints Aware and User Friendly Exam Scheduling System", International Arab Journal of Information Technology, Vol. 13 Issue: 1A, 156-162, (2016).
• 16. Ivancevic, V., Knezevic, M., Lukovic, I., "A Course Exam Scheduling Approach based on Data Mining", Smart Digital Futures 2014, Vol. 262, 132-141, (2014).
• 17. Wahaishi, A.M., Aburukba, R.O., "An Agent-Based Personal Assistant for Exam Scheduling", World Congress on Computer & Information Technology (WCCIT 2013), (2013).
• 18. Di Pietro, G., "Exam Scheduling and Student Performance", Bulletin of Economıc Research, Vol. 65 Issue: 1, 65-81, (2013).
• 19. Sagir, M., Ozturk, Z.K., "Exam scheduling: Mathematical modeling and parameter estimation with the Analytic Network Process approach", Mathematical and Computer Modelling, Vol. 52 Issue: 5-6, 930-941, (2010).
• 20. Wang, S.Q., Bussieck, M., Guignard, M., Meeraus, A., O'Brien, F., "Term-end exam scheduling at United States Military Academy/West Point", Journal of Scheduling, Vol. 13 Issue: 4, 375-391, (2010).
• 21. Ayob, M., Hamdan, A.R., Abdullah, S., Othman, Z., Zakree, M., Nazri, A., Razak, K.A., Tan, R., Baharom, N., Ghafar, H.A., Md Dali, R., Sabar, N.R., Intelligent Examination Timetabling Software, Procedia Social and Behavioral Sciences, 18, 600–608, (2011).
• 22. Botsalı, A.R., “A Timetabling Problem: Constraintand Mathematical Approaches”, Master Dissertation, The Institute of Engineering and Sciences of Bilkent University, Ankara, 1-15, (2000).
• 23. Çoruhlu, A., Examination Staff Scheduling Models, Master Dissertation, Gazi University, Institute of Science and Technology, 92s, Ankara, (2007).
• 24. Dener, M., Akcayol, M.A., Toklu, S., Bay, Ö.F., “Genetic Algorithm Based a New Algorithm for Time Dynamic Shortest Path Problem”, J. Fac. Eng. Arch. Gazi Univ.,Vol 26(4), 915-928, (2011).
• 25. Yiğit, T., "Generating Weekly Timetable of Vocational High Schools By Genetic Algorithms", Gazi University Journal of Industrial Arts Education Faculty,Vol:19, 25-39, (2006).