Araştırma Makalesi
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Yıl 2021, Cilt 1, Sayı 2, 43 - 51, 05.09.2021

Öz

Kaynakça

  • [1] B. McCollum, P. McMullan, A. Parkes, E. Burke, R. Qu, A new model for automated examination timetabling, Annals of Operations Research 194(1), 291315 (2012).
  • [2] Qu, R., Burke, E.K., McCollum, B., Merlot, L., Lee, S.: A survey of search methodologies and automated system development for examination timetabling. Journal of Scheduling 12(1), 55-89 (2009).
  • [3] Muller, T.: Itc2007 solver description: A hybrid approach. Annals of Operations Research 172(1), 429-446 (2009).
  • [4] Amaratunga, D., Baldry, D. (2000) Assessment of Facilities Management Performance in Higher Education Properties Facilities Management, 18(7/8), 293-301.
  • [5] Pursglove, J., & Simpson, M. (2007). Benchmarking the performance of English universities. Benchmarking: An International Journal, 14, 102-122.
  • [6] Rogers, C. (2002). Space Management in Higher Education: Report of the Findings of the Newcastle University Space Management Project, Jointly Funded by the HEFCE Good Management Practice Programme and the University (pp. 1-88). Newcastle: University of Newcastle.
  • [7] HEFCW. (2002). Space Management: A Good Practice Guide. Swansea: University of Wales.
  • [8] SCHEV. (2004). Space Utilization and Comparison Report. Richmond: State Council of Higher Education for Virginia.
  • [9] C. Gogos, P. Alefragis, E. Housos, A multi-staged algorithmic process for the solution of the examination timetabling problem, in: Practice and Theory of Automated Timetabling (PATAT 2008), Montreal, 19-22, August (2008).
  • [10] M. Atsuta, K. Nonobe, T. Ibaraki, Itc2007 track 1: An approach using general csp solver, www.cs.qub.ac.uk/itc2007.
  • [11] Space Management Group. (2006). Space Utilisation: Practice, Performace and Guidelines. London.
  • [12] NAO. (1996). Space Management in Higher Education: A Good Practice Guide. London: National Audit Office.
  • [13] Aziz A. A., Hashim A. E., Baharum Z. A. (2013). Space Inventory Management in the Malaysian Public Universities Social and Behavioral Science
  • [14] G. De Smet, Itc2007 - examination track, in: Practice and Theory of Automated Timetabling (PATAT 2008), Montreal, 19-22, August (2008).
  • [15] N. Pillay, A developmental approach to the examination timetabling problem., in: Practice and Theory of Automated Timetabling (PATAT 2008), Montreal, 19-22, August (2008).
  • [16] B. McCollum, P. McMullan, A. J. Parkes, E. K. Burke, S. Abdullah, An extended great deluge approach to the examination timetabling problem, in: The 4th Multidisciplinary International Conference on Scheduling: Theory and Applications (MISTA09), Dublin, (2009).
  • [17] E. Özcan, et al., A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings, Applied Soft Computing 49,81-93, (2016).
  • [18] S. Abdul-Rahman, A. Bargiela, E. K. Burke, E. Ozcan, B. McCollum, Linear Combination of Heuristic Orderings in Constructing Examination Timetables, European Journal of Operational Research, 232(2), 287-297 (2014).
  • [19] P. Moscato, On evolution, search, optimization, genetic algorithms and martial arts:Towards memetic algorithms, Caltech concurrent computation program, C3P Report, vol.826, p. 1989, (1989).

Analysing The Effects of Classroom Utilisation with A Self-Generating Multimeme Memetic Algorithm for The Exam Timetabling

Yıl 2021, Cilt 1, Sayı 2, 43 - 51, 05.09.2021

Öz

Universities have three main missions which are education, research and community service. The activities related to these missions gain vitality through the elements of “human” and “space.” The Human element consisting of academic staff and administrative staff working in universities represents the university's human capital. The buildings titled as faculty, vocational school, hospital, laboratory, research center on the other hand, constitute the elements of space. These spaces, housing the activities of academic and administrative staff, also represent the physical capital of universities. Universities not only need to be continue their education processes successfully, but they should also be manage to the use of resources in the most effective way. The classroom is the center of the school activities. Classroom management is very different from planning and evaluating other space needs. Without an effective classroom management, the heavy investment in the school system could produce loss rather than gain. It relates to the effective classroom planning, management techniques and classroom utilisation in determining accurately how many students the facilities will adequately support. Classroom capacity utilization is an economics concept which refers to the extent to which a higher education institutes or a nation actually uses its available classroom capacity. Therefore, the relationship between whether used and how classroom is being used is very important. Classroom utilisation rate is a percentage-based ratio based on an occupancy rate and frequency rate. The frequency rate evaluates how many times that classroom is used compared to its availability, and the occupancy rate evaluates how many users can actually use the space at one time the classroom is compared to its actual capacity. The main purpose of this study is to analysis the classroom utilisation effects on exam timetabling problem with a self-generating memetic algorithm. The results from the analysis show that classroom utilisation rates for exam timetabling problem should be addressed fully by top management. This study also offers that in order to improve the classroom utilisation rate, higher education institutes should think about the occupancy rate as it is the determining agent affecting the utilisation rate.

Kaynakça

  • [1] B. McCollum, P. McMullan, A. Parkes, E. Burke, R. Qu, A new model for automated examination timetabling, Annals of Operations Research 194(1), 291315 (2012).
  • [2] Qu, R., Burke, E.K., McCollum, B., Merlot, L., Lee, S.: A survey of search methodologies and automated system development for examination timetabling. Journal of Scheduling 12(1), 55-89 (2009).
  • [3] Muller, T.: Itc2007 solver description: A hybrid approach. Annals of Operations Research 172(1), 429-446 (2009).
  • [4] Amaratunga, D., Baldry, D. (2000) Assessment of Facilities Management Performance in Higher Education Properties Facilities Management, 18(7/8), 293-301.
  • [5] Pursglove, J., & Simpson, M. (2007). Benchmarking the performance of English universities. Benchmarking: An International Journal, 14, 102-122.
  • [6] Rogers, C. (2002). Space Management in Higher Education: Report of the Findings of the Newcastle University Space Management Project, Jointly Funded by the HEFCE Good Management Practice Programme and the University (pp. 1-88). Newcastle: University of Newcastle.
  • [7] HEFCW. (2002). Space Management: A Good Practice Guide. Swansea: University of Wales.
  • [8] SCHEV. (2004). Space Utilization and Comparison Report. Richmond: State Council of Higher Education for Virginia.
  • [9] C. Gogos, P. Alefragis, E. Housos, A multi-staged algorithmic process for the solution of the examination timetabling problem, in: Practice and Theory of Automated Timetabling (PATAT 2008), Montreal, 19-22, August (2008).
  • [10] M. Atsuta, K. Nonobe, T. Ibaraki, Itc2007 track 1: An approach using general csp solver, www.cs.qub.ac.uk/itc2007.
  • [11] Space Management Group. (2006). Space Utilisation: Practice, Performace and Guidelines. London.
  • [12] NAO. (1996). Space Management in Higher Education: A Good Practice Guide. London: National Audit Office.
  • [13] Aziz A. A., Hashim A. E., Baharum Z. A. (2013). Space Inventory Management in the Malaysian Public Universities Social and Behavioral Science
  • [14] G. De Smet, Itc2007 - examination track, in: Practice and Theory of Automated Timetabling (PATAT 2008), Montreal, 19-22, August (2008).
  • [15] N. Pillay, A developmental approach to the examination timetabling problem., in: Practice and Theory of Automated Timetabling (PATAT 2008), Montreal, 19-22, August (2008).
  • [16] B. McCollum, P. McMullan, A. J. Parkes, E. K. Burke, S. Abdullah, An extended great deluge approach to the examination timetabling problem, in: The 4th Multidisciplinary International Conference on Scheduling: Theory and Applications (MISTA09), Dublin, (2009).
  • [17] E. Özcan, et al., A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings, Applied Soft Computing 49,81-93, (2016).
  • [18] S. Abdul-Rahman, A. Bargiela, E. K. Burke, E. Ozcan, B. McCollum, Linear Combination of Heuristic Orderings in Constructing Examination Timetables, European Journal of Operational Research, 232(2), 287-297 (2014).
  • [19] P. Moscato, On evolution, search, optimization, genetic algorithms and martial arts:Towards memetic algorithms, Caltech concurrent computation program, C3P Report, vol.826, p. 1989, (1989).

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Bilimleri, Yapay Zeka
Bölüm Research Articles
Yazarlar

Cevriye ALTINTAŞ> (Sorumlu Yazar)
TEKNOLOJİ FAKÜLTESİ
0000-0001-5928-3402
Türkiye


Tuncay YİĞİT>
SÜLEYMAN DEMİREL ÜNİVERSİTESİ
0000-0001-7397-7224
Türkiye

Destekleyen Kurum süleyman demirel üniversitesi
Proje Numarası 4538-D1-16
Teşekkür The authors wish to thank Suleyman Demirel University Scientific Research Projects Management Unit Presidency that supported this Project financially with project number 4538-D1-16.
Yayımlanma Tarihi 5 Eylül 2021
Kabul Tarihi 19 Nisan 2021
Yayınlandığı Sayı Yıl 2021, Cilt 1, Sayı 2

Kaynak Göster

IEEE C. Altıntaş ve T. Yiğit , "Analysing The Effects of Classroom Utilisation with A Self-Generating Multimeme Memetic Algorithm for The Exam Timetabling", Advances in Artificial Intelligence Research, c. 1, sayı. 2, ss. 43-51, Eyl. 2021