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OR Practice—An Efficient Decision Support System for Academic Course Scheduling

Author

Listed:
  • John J. Dinkel

    (Texas A&M University, College Station, Texas)

  • John Mote

    (The University of Texas, Austin, Texas)

  • M. A. Venkataramanan

    (Indiana University, Bloomington, Indiana)

Abstract

This paper describes a network-based decision support system approach to the most general form of the academic course scheduling problem. The dimensions of faculty, subject, time, and room are considered by incorporating a penalty function into a network optimization approach. The approach, based on a network algorithm, is capable of solving very large problems. This methodology can be applied to other scheduling situations where there are competing objectives and multiple resources. Such situations include: scheduling of exams, times, and rooms in an academic setting, and scheduling of clients, times, and facilities for physicians, hospitals, dentists, counselors, and clinics. Common problems in such settings include the utilization of available space, and dissatisfaction with assigned times and locations. The proposed system results in more effective room utilization patterns, improved instructor satisfaction levels, and streamlines the tedious scheduling process. We describe the use of the model to schedule all graduate and undergraduate courses in the College of Business Administration at Texas A&M University. This involves 175 faculty, over 300 sections, 20 rooms, and 16 time slots for each semester's scheduling problem.

Suggested Citation

  • John J. Dinkel & John Mote & M. A. Venkataramanan, 1989. "OR Practice—An Efficient Decision Support System for Academic Course Scheduling," Operations Research, INFORMS, vol. 37(6), pages 853-864, December.
    • Handle: RePEc:inm:oropre:v:37:y:1989:i:6:p:853-864
    DOI: 10.1287/opre.37.6.853
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    Citations

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    Cited by:

    1. Drexl, Andreas & Salewski, Frank, 1997. "Distribution requirements and compactness constraints in school timetabling," European Journal of Operational Research, Elsevier, vol. 102(1), pages 193-214, October.
    2. Dimopoulou, M. & Miliotis, P., 2001. "Implementation of a university course and examination timetabling system," European Journal of Operational Research, Elsevier, vol. 130(1), pages 202-213, April.
    3. Badri, Masood A., 1996. "A two-stage multiobjective scheduling model for [faculty-course-time] assignments," European Journal of Operational Research, Elsevier, vol. 94(1), pages 16-28, October.
    4. Dimopoulou, M. & Miliotis, P., 2004. "An automated university course timetabling system developed in a distributed environment: A case study," European Journal of Operational Research, Elsevier, vol. 153(1), pages 136-147, February.
    5. Andrei STANCIU & Mihai FLORIN & Cristina RADULESCU & Ofelia ALECA, 2009. "Solutions for decision support in university management," Economia. Seria Management, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 12(1), pages 136-151, June.
    6. Biniyam Asmare Kassa, 2015. "Implementing a Class-Scheduling System at the College of Business and Economics of Bahir Dar University, Ethiopia," Interfaces, INFORMS, vol. 45(3), pages 203-215, June.
    7. Domenech, B & Lusa, A, 2016. "A MILP model for the teacher assignment problem considering teachers’ preferences," European Journal of Operational Research, Elsevier, vol. 249(3), pages 1153-1160.
    8. Caselli, Giulia & Delorme, Maxence & Iori, Manuel, 2022. "Integer linear programming for the Tutor Allocation Problem : A practical case in a British University," Other publications TiSEM 983593a6-c17d-4b87-8ee1-a, Tilburg University, School of Economics and Management.
    9. Mustafa, A. & Goh, M., 1996. "Multi-criterion models for higher education administration," Omega, Elsevier, vol. 24(2), pages 167-178, April.
    10. Drexl, Andreas & Juretzka, Jan & Salewski, Frank, 1993. "Academic course scheduling under workload and changeover constraints," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 337, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    11. Boronico, Jess, 2000. "Quantitative modeling and technology driven departmental course scheduling," Omega, Elsevier, vol. 28(3), pages 327-346, June.
    12. Daskalaki, S. & Birbas, T. & Housos, E., 2004. "An integer programming formulation for a case study in university timetabling," European Journal of Operational Research, Elsevier, vol. 153(1), pages 117-135, February.
    13. Janice K. Winch & Jack Yurkiewicz, 2014. "Case Article—Class Scheduling with Linear Programming," INFORMS Transactions on Education, INFORMS, vol. 15(1), pages 143-147, September.
    14. Partovi, Fariborz Y. & Arinze, Bay, 1995. "A knowledge based approach to the faculty-course assignment problem," Socio-Economic Planning Sciences, Elsevier, vol. 29(3), pages 245-256, September.
    15. Daskalaki, S. & Birbas, T., 2005. "Efficient solutions for a university timetabling problem through integer programming," European Journal of Operational Research, Elsevier, vol. 160(1), pages 106-120, January.

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