Akademik unvan temelli tercihlere ve sapmalara dayalı ders çizelgeleme modeli-endüstri mühendisliği bölümü örneği

Öz

Ders programı çizelgeleme problemi, üniversiteler ve benzeri eğitim kurumlarında yaygın olarak karşılaşılan zaman çizelgeleme problemlerinden birisidir. Çözümü, yoğun iş gücü ve kaynak gerektirmekte olup birçok eğitim kurumunda çizelgeleme problemi halen elle yapılmakta ve çok zaman kaybettirmektedir. Ders programı çizelgeleme problemi, eğitim kurumlarına özgü kısıt yapıları dikkate alınarak, derslerin uygun zaman dilimlerine atanmasını konu almaktadır. Kısıt yapıları, eğitim kurumunun özel kuralları, kapasite kısıtlamaları, yasal düzenlemeler, öğretim elemanları ve öğrencilerin tercihleri gibi farklı faktörlere bağlı olabilir. Bu çalışmada, öğretim elamanı tercihleri doğrultusunda iki yeni 0-1 tam sayılı programlama modeli önerilmiş, bir devlet üniversitesinde yapılan uygulama ile modeller test edilmiştir. Önerilen ilk modelde, öğretim elemanlarının memnuniyetleri en büyüklenmek istenirken, istenmeyen ders çakışması da en küçüklenmek istenmektedir. İkinci modelde ise, ilk model amaçlarına ilave olarak aynı unvana sahip öğretim elemanlarının memnuniyet değerleri arasındaki farkın da en küçüklenmesi amaçlanmıştır. Önerilen modeller, GAMS 23.8.2 yazılımı ile çözülmüş, karar vericiler için uygun olabilecek seçenekler sunulmuştur

Anahtar Kelimeler

• Ders programı çizelgeleme
• Öğretim elemanı tercihleri
• Akademik unvan-temelli çizelgeleme
• Tam sayılı programlama

A course scheduling model based on academic title-based preferences and deviations- a case study of industrial engineering department

Abstract

Course timetabling problem is one of the most common time scheduling problems that frequently encountered in universities and whole education institutions. To eliminate this problem requires notable amount of labour and supply and in several education institutions, this scheduling is done manually but it doesn’t occur as desired and causes to loss of time. Course timetabling problem is the assignment of courses to appropriate time periods, considering the constraint structures specific to the educational institutions. Constraints may depend on different factors such as the specific rules of the educational institution, capacity constraints, legal regulations, faculty and students' preferences. In this study, two new 0-1 integer programming models were proposed by considering preferences of the faculty members, and the models were tested with the application in a state university. In first proposed model, while the total gladness value is desired to be maximized by considering the time-slot preferences of lecturers, the conflict of courses is tried to be minimized. In the second proposed model, the differences in gladness value between lecturers are aimed to be minimized in addition to first model objectives. The proposed models were solved with GAMS 23.8.2 software, and alternatives that could be suitable for decision makers were presented.

Keywords

• Timetable Scheduling
• Lecturer preferences
• Academic title based scheduling
• Integer programming

Kaynakça

1. [1] Thepphakorn T, Pongcharoen P. “Performance improvement strategies on Cuckoo Search algorithms for solving the university course timetabling problem”. Expert Systems with Applications, 161, 1-21, 2020.
2. [2] Kamışlı Öztürk Z, Kasımbeyli N, Sağır Özdemir M, Soyuöz Acar M, Özçetin E, Alegöz M, Ceylan G. “Kullanıcı Tercihlerinin Dikkate Alınması Durumunda Üniversite Ders Çizelgeleme Problemi”. Endüstri Mühendisliği Dergisi, 27(1), 2-16, 2015.
3. [3] Burke EK, MacCarthy B, Petrovic S, Qui R. “Case-Based Reasoning in Course Timetabling: An attribute Graph Approach”. Proceedings of the 4th International Conference on Case-Based Reasoning, Vancouver, Canada, 12 July 2001.
4. [4] Lai L, Hsueh N, Huang L, Chen T. “An Artificial İntelligence Approach to Course Timetabling”. Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence, Washington D.C., USA, 13-15 November 2006.
5. [5] Junn KY, Obit JH, Alfred R. “Comparison of simulated annealing and great deluge algorithms for university course timetabling problems”. Advanced Science Letters, 23(11), 11413-11417, 2017.
6. [6] Zhang MX, Zhang B, Qian N. “University course timetabling using a new ecogeography-based optimization algorithm”. Natural Computing, 16(1), 61-74, 2017.
7. [7] Matias JB, Fajardo AC, Medina RM. “A fair course timetabling using genetic algorithm with guided search technique”. In 5th International Conference on Business and Industrial Research (ICBIR), Bangkok, Thailand, 18 May 2018.
8. [8] Mazlan M, Makhtar M, Ahmad Khairi AFK, Mohamed MA. “University course timetabling model using ant colony optimization algorithm approach”. Indonesian Journal of Electrical Engineering and Computer Science, 13(1), 72-76, 2019.

9. [9] Bashab A, Ibrahim AO , AbedElgabar EE, Ismail MA, Elsafi A, Ahmed A, Abraham A. “A systematic mapping study on solving university timetabling problems using metaheuristic algorithms”. Neural Computing and Applications, 32, 17397-17432, 2020.
10. [10] Gunawan A, Ng KM, Poh KL. “A Hybridized Langrangian Relaxation and Simulated Aannealing Method for The Course Timetabling Problem”. Computers & Operations Research, 39(12), 3074-3088, 2012.
11. [11] Abuhamdah A, Ayob M, Kendall G, Sabar NR. “Population based Local Search for university course timetabling problems”. Applied intelligence, 40(1), 44-53, 2014.
12. [12] Demir Y, Çelik C. “Müfredat Bazlı Akademik Zaman Çizelgeleme Probleminin Çözümüne Tam Sayılı Doğrusal Programlama Yaklaşımı”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 31(1), 145-159, 2016.
13. [13] Vermuyten H, Lemmens S, Marques I, Belian J. “Developing compact course timetables with optimized student flows”. European Journal of Operational Research, 251, 651-661, 2016.
14. [14] Domenech B, Lusa A. “A MILP model for teacher assignment problem considering teachers’ preferences”. European Journal of Operational Research, 249, 1153-1160, 2016.
15. [15] [Lindahl M, Mason AJ, Stidsen T, Sorensen M. “A strategic view of University timetabling”. European Journal of Operational Research, 266, 35-45, 2018.
16. [16] Faudzi S, Abdul-Rahman S, Rahman R. “An assignment problem and its application in education domani: a review and potential path”. Hindawi Advances in Operations Research, 2018, 1-19, 2018.
17. [17] Song T, Liu S, Tang X, Peng X, Chen M. “An iterated local search algorithm for the University Course Timetabling Problem”. Applied Soft Computing, 68, 597-608, 2018.
18. [18] Wikarek J. “Lecturers' competences configuration model for the timetabling problem”. In 2018 Federated Conference on Computer Science and Information Systems (FedCSIS), Poznań, Poland, 9-12 September 2018.
19. [19] Babaei H, Karimpour J, Hadidi A. “Generating an optimal timetabling for multi-departments common lecturers using hybrid fuzzy and clustering algorithms”. Soft Computing, 23, 4735-4747, 2019.
20. [20] Yasari P, Ranjbar M, Jamili N, Shaelaie MH. “A two-stage stochastic programming approach for a multi-objective course timetabling problem with courses cancelation risk”. Computers & Industrial Engineering, 130, 650-660, 2019.
21. [21] Lindahl M, Stidsen T, Sørensen M. “Quality recovering of university timetables”. European Journal of Operational Research, 276, 422-435, 2019.
22. [22] Al-Hawari F, Al-Ashi M, Abawi F, Alouneh S. “A Practical three-phase ILP approach for solving the examination timetabling problem”. International Transactions in Operational Research, 27, 924-944, 2020.
23. [23] Altunay H, Eren T. “Ders programı çizelgeleme problemi için bir literatür taraması”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 23, 55-70, 2017.
24. [24] Altunay H, Tamer Eren. “A 0-1 Integer Programming Model for the Course Scheduling Problem and A Case Study”. Uludağ University Journal of the Faculty of Engineering, 21(2), 473-488, 2016.
25. [25] Saraç T, Özçelik F, Erdoğan H. “Mazeret/Telafi sınavı çizelgeleme problemi için bir hedef programlama modeli”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 26(1) , 203-210, 2020.
26. [26] Sancar Edis R, Edis E. B. “Gerçek bir sınav çizelgeleme problemi için iki aşamalı çözüm yaklaşımı”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 25 (1), 71-81, 2019.