Research Article
BibTex RIS Cite

DEVELOPING A DECISION SUPPORT SYSTEM FOR EXAM SCHEDULING PROBLEM USING GENETIC ALGORITHM

Year 2021, Volume: 22 Issue: 3, 274 - 289, 29.09.2021
https://doi.org/10.18038/estubtda.890307

Abstract

References

  • [1] Warke Y, Munje D, Swami A, Raskar S, Tapkir G. Automatic timetable generation using genetic and hungarian model. Studia Rosenthaliana (Journal for the Study of Research), 2020; 12 (5): 67-74.
  • [2] Bhaduri A. University timetable scheduling using genetic artificial immune network. International Conference on Advances in Recent Technologies in Communication and Computing. IEEE. 2009; 289-292.
  • [3] Haupt RL, Haupt SE. Practical genetic algorithms. 2th ed. New Jersey, John Wiley & Sons, 2004.
  • [4] Duong TA, Lam KH. Combining constraint programming and simulated annealing on university exam timetabling. In Proceedings of the 2nd international conference in computer sciences, research, innovation & vision for the future (RIVF2004). Hanoi, Vietnam, 2004; 205-210.
  • [5] Sagir M, Ozturk ZK. Exam scheduling: Mathematical modeling and parameter estimation with the Analytic Network Process approach. Mathematical and Computer Modelling, 2010; 52 (5-6): 930-941.
  • [6] Yaldır A, Baysal, C. Evrimsel hesaplama tekniği kullanarak sınav takvimi otomasyon sistemi geliştirilmesi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 2012; 18 (2), 105-122.
  • [7] Anwar K, Khader AT, Al-Betar MA, Awadallah MA. Harmony search-based hyper-heuristic for examination timetabling. In Proceedings of the IEEE 9th international colloquium on signal processing and its application, Kuala Lumpur, Malaysia, 2013 (March); 176-181.
  • [8] Jha SK. Exam timetabling problem using genetic algorithm. International Journal of Research in Engineering and Technology, 2014; 3 (5): 649-654.
  • [9] Koide T. Mixed integer programming approach on examination proctor assignment problem. Procedia Computer Science, 2015; 60: 818-823.
  • [10] Woumans G, De Boeck L, Beliën J, Creemers S. A column generation approach for solving the examination-timetabling problem. European Journal of Operational Research, 2016; 253 (1): 78-194.
  • [11] Dener M, Calp MH. Solving the exam scheduling problems in central exams with genetic algorithms. Mugla Journal of Science and Technology, 2018; 4 (1): 102-115.
  • [12] Çavdur F, Değirmen S, Küçük M. K. Sınav çizelgeleme problemlerinde homojen sınav dağılımının oluşturulması için kümeleme ve hedef programlama temelli bir yaklaşım. Uludağ University Journal of The Faculty of Engineering, 2018; 23 (1): 167-188.
  • [13] Tapkan PZ. Final sınav programı hazırlama problemine ait bir matematiksel model ve uygulama. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 2019; 53: 138-156.
  • [14] Ceylan Z, Yüksel A, Yıldız A, Şimşak B. Sınav çizelgeleme problemi için hedef programlama yaklaşımı ve bir uygulama. Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2019; 12 (2): 942-956.
  • [15] Leite N, Melício F, Rosa A. C. A fast simulated annealing algorithm for the examination timetabling problem. Expert Systems with Applications, 2019; 122: 137-151.
  • [16] MirHassani SA. Improving paper spread in examination timetables using integer programming. Applied Mathematics and Computation, 2006; 179 (2): 702-706.
  • [17] Mathew TV. Genetic algorithm. Report submitted Indian Institute of Technology (IIT), Bombay, Mumbai, 2012.
  • [18] Taşkın Ç, Gül Gökay E. Sayısal yöntemlerde genetik algoritmalar. Alfa Aktüel Yayıncılık. Bursa 2009.
  • [19] Karaboğa D. Yapay zeka optimizasyon algoritmaları, Nobel Yayıncılık, Ankara, 2017.
  • [20] Soghier A, Qu R. Adaptive selection of heuristics for assigning time slots and rooms in exam timetables. Applied Intelligence, 2013; 39 (2): 438-450.
  • [21] Tumbas P, Sedlak O, Matkovic P. Decision Support Systems for Logistics Management. The International Scientific Journal of Management Information Systems, 2007; 2 (2): 32-39.
  • [22] Dios M, Molina-Pariente JM, Fernandez-Viagas V, Andrade-Pineda JL, Framinan JMA decision support system for operating room scheduling. Computers & Industrial Engineering, 2015; 88: 430-443.
  • [23] Poon T C, Choy K. L, Chan F. T, Lau H. C. A real-time production operations decision support system for solving stochastic production material demand problems. Expert Systems with Applications, 2011; 38 (5): 4829-4838.
  • [24] Kandakoglu A, Sauré A, Michalowski W, Aquino M, Graham J, McCormick B. A decision support system for home dialysis visit scheduling and nurse routing. Decision Support Systems, 2020; 130: 113224.
  • [25] Güler MG, Geçici E. A spreadsheet-based decision support system for examination timetabling. Turkish Journal of Electrical Engineering & Computer Sciences, 2020; 28 (3): 1584-1598.
  • [26] Chen X, Qi Z, Gui D, Gu Z, Ma L, Zeng F, Sima M. W. A model-based real-time decision support system for irrigation scheduling to improve water productivity. Agronomy, 2019; 9 (11): 686.
  • [27] Bomsdorf F, Derigs U. A model, heuristic procedure and decision support system for solving the movie shoot scheduling problem. Or Spectrum, 2008; 30 (4): 751-772.
  • [28] Lin C, Choy K. L, Ho G. T, Lam H. Y, Pang G. K, Chin K. S. A decision support system for optimizing dynamic courier routing operations. Expert Systems with Applications, 2014; 41 (15): 6917-6933.
  • [29] Ruiz R, Maroto C, Alcaraz J. A decision support system for a real vehicle routing problem. European Journal of Operational Research, 2004; 153 (3): 593-606.
  • [30] Kocsi B, Matonya M. M, Pusztai L. P, Budai I. Real-time decision-support system for high-mix low-volume production scheduling in industry 4.0. Processes, 2020; 8 (8): 912.
  • [31] Ko C. H, Wang S. F. GA-based decision support systems for precast production planning. Automation in Construction, 2010; 19 (7): 907-916.
  • [32] Gençal MC. A Study to improve performance of genetic algorithms, PhD Thesis, Çukurova University Institute of Natural and Applied Sciences, Department of Computer Engineering, Adana, 2019.
  • [33] Mukhopadhyay DM, Balitanas MO, Farkhod A, Jeon SH, Bhattacharyya D. Genetic algorithm: A tutorial review. International Journal of Grid and Distributed Computing, 2009; 2 (3): 25-32.
  • [34] Sezik N. Speed control of BLDC motor based on multicriteria optimization with genetic algorithm, Master Thesis, The Graduate School of Natural and Applied Sciences of Çankaya University, Ankara. 2020.
  • [35] Dener M, Akcayol M, Toklu S, Bay Ö. Genetic algorithm based a new algorithm for time dynamic shortest path problem. Journal of the Faculty of Engineering and Architecture of Gazi University, 2011; 26 (4): 915-928.

DEVELOPING A DECISION SUPPORT SYSTEM FOR EXAM SCHEDULING PROBLEM USING GENETIC ALGORITHM

Year 2021, Volume: 22 Issue: 3, 274 - 289, 29.09.2021
https://doi.org/10.18038/estubtda.890307

Abstract

Exam scheduling is a very complex process done every semester in every educational institution and is usually done manually. The limited resources in these institutions make the preparation of the exam program a demanding and inconvenient task. In addition, when exam scheduling is examined in detail, it is seen that it is a comprehensive task that requires fulfillment of many situations other than the appointment of the appropriate course for the appropriate time period. In this study, a solution approach that allows the assignment of courses to time periods is proposed for the educational institution whose data we use. Thus, it is aimed to obtain a program that can quickly solve the exam scheduling problem, which is a comprehensive task for the educational institution. A Genetic Algorithm, an artificial intelligence optimization algorithm, was used as a solution method, and the performance of the method on the problem was tested. In addition, taking into account students, lecturers, and administrative staff's performance within the study's scope and purpose, it aims to obtain exam programs that will satisfy everyone. However, most studies focused on the solution to the problem, and the idea of making this program suitable for the use of non-technical personnel was neglected. The program we developed in this study has been turned into a decision support system. Thus, the program has become a structure suitable for the use of non-technical personnel. As a result of the study, considering the institution's entire structure, a program that the institution can use in every exam period was obtained, and the exam program was automated, eliminating the time and effort spent by the institution staff.

References

  • [1] Warke Y, Munje D, Swami A, Raskar S, Tapkir G. Automatic timetable generation using genetic and hungarian model. Studia Rosenthaliana (Journal for the Study of Research), 2020; 12 (5): 67-74.
  • [2] Bhaduri A. University timetable scheduling using genetic artificial immune network. International Conference on Advances in Recent Technologies in Communication and Computing. IEEE. 2009; 289-292.
  • [3] Haupt RL, Haupt SE. Practical genetic algorithms. 2th ed. New Jersey, John Wiley & Sons, 2004.
  • [4] Duong TA, Lam KH. Combining constraint programming and simulated annealing on university exam timetabling. In Proceedings of the 2nd international conference in computer sciences, research, innovation & vision for the future (RIVF2004). Hanoi, Vietnam, 2004; 205-210.
  • [5] Sagir M, Ozturk ZK. Exam scheduling: Mathematical modeling and parameter estimation with the Analytic Network Process approach. Mathematical and Computer Modelling, 2010; 52 (5-6): 930-941.
  • [6] Yaldır A, Baysal, C. Evrimsel hesaplama tekniği kullanarak sınav takvimi otomasyon sistemi geliştirilmesi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 2012; 18 (2), 105-122.
  • [7] Anwar K, Khader AT, Al-Betar MA, Awadallah MA. Harmony search-based hyper-heuristic for examination timetabling. In Proceedings of the IEEE 9th international colloquium on signal processing and its application, Kuala Lumpur, Malaysia, 2013 (March); 176-181.
  • [8] Jha SK. Exam timetabling problem using genetic algorithm. International Journal of Research in Engineering and Technology, 2014; 3 (5): 649-654.
  • [9] Koide T. Mixed integer programming approach on examination proctor assignment problem. Procedia Computer Science, 2015; 60: 818-823.
  • [10] Woumans G, De Boeck L, Beliën J, Creemers S. A column generation approach for solving the examination-timetabling problem. European Journal of Operational Research, 2016; 253 (1): 78-194.
  • [11] Dener M, Calp MH. Solving the exam scheduling problems in central exams with genetic algorithms. Mugla Journal of Science and Technology, 2018; 4 (1): 102-115.
  • [12] Çavdur F, Değirmen S, Küçük M. K. Sınav çizelgeleme problemlerinde homojen sınav dağılımının oluşturulması için kümeleme ve hedef programlama temelli bir yaklaşım. Uludağ University Journal of The Faculty of Engineering, 2018; 23 (1): 167-188.
  • [13] Tapkan PZ. Final sınav programı hazırlama problemine ait bir matematiksel model ve uygulama. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 2019; 53: 138-156.
  • [14] Ceylan Z, Yüksel A, Yıldız A, Şimşak B. Sınav çizelgeleme problemi için hedef programlama yaklaşımı ve bir uygulama. Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2019; 12 (2): 942-956.
  • [15] Leite N, Melício F, Rosa A. C. A fast simulated annealing algorithm for the examination timetabling problem. Expert Systems with Applications, 2019; 122: 137-151.
  • [16] MirHassani SA. Improving paper spread in examination timetables using integer programming. Applied Mathematics and Computation, 2006; 179 (2): 702-706.
  • [17] Mathew TV. Genetic algorithm. Report submitted Indian Institute of Technology (IIT), Bombay, Mumbai, 2012.
  • [18] Taşkın Ç, Gül Gökay E. Sayısal yöntemlerde genetik algoritmalar. Alfa Aktüel Yayıncılık. Bursa 2009.
  • [19] Karaboğa D. Yapay zeka optimizasyon algoritmaları, Nobel Yayıncılık, Ankara, 2017.
  • [20] Soghier A, Qu R. Adaptive selection of heuristics for assigning time slots and rooms in exam timetables. Applied Intelligence, 2013; 39 (2): 438-450.
  • [21] Tumbas P, Sedlak O, Matkovic P. Decision Support Systems for Logistics Management. The International Scientific Journal of Management Information Systems, 2007; 2 (2): 32-39.
  • [22] Dios M, Molina-Pariente JM, Fernandez-Viagas V, Andrade-Pineda JL, Framinan JMA decision support system for operating room scheduling. Computers & Industrial Engineering, 2015; 88: 430-443.
  • [23] Poon T C, Choy K. L, Chan F. T, Lau H. C. A real-time production operations decision support system for solving stochastic production material demand problems. Expert Systems with Applications, 2011; 38 (5): 4829-4838.
  • [24] Kandakoglu A, Sauré A, Michalowski W, Aquino M, Graham J, McCormick B. A decision support system for home dialysis visit scheduling and nurse routing. Decision Support Systems, 2020; 130: 113224.
  • [25] Güler MG, Geçici E. A spreadsheet-based decision support system for examination timetabling. Turkish Journal of Electrical Engineering & Computer Sciences, 2020; 28 (3): 1584-1598.
  • [26] Chen X, Qi Z, Gui D, Gu Z, Ma L, Zeng F, Sima M. W. A model-based real-time decision support system for irrigation scheduling to improve water productivity. Agronomy, 2019; 9 (11): 686.
  • [27] Bomsdorf F, Derigs U. A model, heuristic procedure and decision support system for solving the movie shoot scheduling problem. Or Spectrum, 2008; 30 (4): 751-772.
  • [28] Lin C, Choy K. L, Ho G. T, Lam H. Y, Pang G. K, Chin K. S. A decision support system for optimizing dynamic courier routing operations. Expert Systems with Applications, 2014; 41 (15): 6917-6933.
  • [29] Ruiz R, Maroto C, Alcaraz J. A decision support system for a real vehicle routing problem. European Journal of Operational Research, 2004; 153 (3): 593-606.
  • [30] Kocsi B, Matonya M. M, Pusztai L. P, Budai I. Real-time decision-support system for high-mix low-volume production scheduling in industry 4.0. Processes, 2020; 8 (8): 912.
  • [31] Ko C. H, Wang S. F. GA-based decision support systems for precast production planning. Automation in Construction, 2010; 19 (7): 907-916.
  • [32] Gençal MC. A Study to improve performance of genetic algorithms, PhD Thesis, Çukurova University Institute of Natural and Applied Sciences, Department of Computer Engineering, Adana, 2019.
  • [33] Mukhopadhyay DM, Balitanas MO, Farkhod A, Jeon SH, Bhattacharyya D. Genetic algorithm: A tutorial review. International Journal of Grid and Distributed Computing, 2009; 2 (3): 25-32.
  • [34] Sezik N. Speed control of BLDC motor based on multicriteria optimization with genetic algorithm, Master Thesis, The Graduate School of Natural and Applied Sciences of Çankaya University, Ankara. 2020.
  • [35] Dener M, Akcayol M, Toklu S, Bay Ö. Genetic algorithm based a new algorithm for time dynamic shortest path problem. Journal of the Faculty of Engineering and Architecture of Gazi University, 2011; 26 (4): 915-928.
There are 35 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Ahmet Doğan 0000-0002-7116-3558

Ahmet Yurtsal 0000-0003-0523-3519

Publication Date September 29, 2021
Published in Issue Year 2021 Volume: 22 Issue: 3

Cite

AMA Doğan A, Yurtsal A. DEVELOPING A DECISION SUPPORT SYSTEM FOR EXAM SCHEDULING PROBLEM USING GENETIC ALGORITHM. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering. September 2021;22(3):274-289. doi:10.18038/estubtda.890307