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Visual Analytics Solution for Scheduling Processing Phases

  • J. Joshua ThomasEmail author
  • Bahari BelatonEmail author
  • Ahamad Tajudin KhaderEmail author
  • JusttinaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 866)

Abstract

University Examination Timetabling Problem (UETP) is a computationally complex scheduling problem. Visual Analytics (VA) is a modern visualization supported with automated processing method. The major impulse of the method lies in its ability to integrate the key component of scientific visualization and search based heuristics in the same optimization model. This paper presents a visual analytics process (VAP) adapted for UETP. The adaption involves the human context of visual analytics on timetabling data, which are typically processed computationally with local search algorithm and then visualized and interpreted by the user in order to perform problem solving with direct interactions between the primary data, processing and visualization. The three processing phases are invoked with user-driven and algorithmic-driven steering that analyses the combined effect with automatic tuning of algorithmic parameters based on constraints and the criticality of the application for the simulations is proposed. The optimal solution for the small datasets and best overall results for the medium and large datasets are experimented.

Keywords

Examination timetabling Interactive visualization User-driven Algorithm driven steering 

References

  1. 1.
    Ranson, D. Cheng, P.-H.: Graphical tools for heuristic visualization. In: Kendall, G., Lei, L., Pinedo, M. (eds.) Proceedings of the 2nd Multidisciplinary International Conference on Scheduling: Theory and Applications (MISTA), 18–21 July 2005, vol. 2, New York, USA, pp. 658–667 (2005)Google Scholar
  2. 2.
    Thomas, J.J., Khader, A.T., Belaton, B., Ken, C.C.: Integrated problem solving steering framework on clash reconciliation strategies for university examination timetabling problem. In: Neural Information Processing , pp. 297–304. Springer, Heidelberg (2012)Google Scholar
  3. 3.
    Thomas, J.J., Khader, A.T., Belaton, B.: A parallel coordinates visualization for the uncapaciated examination timetabling problem. In: Visual Informatics: Sustaining Research and Innovations, pp. 87–98. Springer, Heidelberg (2011)Google Scholar
  4. 4.
    Thomas, J.J., Khader, A.T., Belaton, B.: The perception of interaction on the university examination timetabling problem. In: PATAT 2010, p. 392 (2010)Google Scholar
  5. 5.
    Qu, R., Burke, E.K., McCollum, B., Merlot, L.T., Lee, S.Y.: A survey of search methodologies and automated system development for examination timetabling. J. Sched. 12(1), 55–89 (2009)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Bonutti, A., De Cesco, F., Di Gaspero, L., Schaerf, A.: Benchmarking curriculum-based course timetabling: formulations, data formats, instances, validation, visualization, and results. Ann. Oper. Res. 194(1), 59–70 (2012)CrossRefGoogle Scholar
  7. 7.
    Schneider, T., Aigner, W.: A-Plan: integrating interactive visualization with automated planning for cooperative resource scheduling. In: Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies, p. 44. ACM (2011, September)Google Scholar
  8. 8.
    Hinneburg, A., Keim, D.A.: A general approach to clustering in large databases with noise. Knowl. Inf. Syst. 5(4), 387–415 (2003)CrossRefGoogle Scholar
  9. 9.
    Davey, J., Mansmann, F., Kohlhammer, J., Keim, D.: Visual analytics: towards intelligent interactive internet and security solutions. In: The Future Internet, pp. 93–104. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Kroenung, L., Tauritz, D.: Visualization for Hyper-Heuristics. Front-End Graphical User Interface (No. SAND2015-2324R). Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States) (2015)Google Scholar
  11. 11.
    Razaghi, R., Amanifard, N., Narimanzadeh, N.: Modeling and multi-objective optimization of stall control on NACA0015 airfoil with a synthetic jet using GMDH type neural networks and genetic algorithms. Int. J. Eng. Trans. A 22(1), 69–88 (2009)Google Scholar
  12. 12.
    Nahavandi, N., Zegordi, S.H., Abbasian, M.: Solving the dynamic job shop scheduling problem using bottleneck and intelligent agents based on genetic algorithm. Int. J. Eng. Trans. C Asp. 29(3), 347 (2016)Google Scholar
  13. 13.
    Lewis, R.: A survey of metaheuristic-based techniques for university timetabling problems. OR Spectr. 30(1), 167–190 (2008)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Carter, M.W., Laporte, G., Lee, S.Y. Examination timetabling: algorithmic strategies and applications. J. Oper. Res. Soc., 373–383 (1996)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computing, School of Engineering, Computing, and Built EnvironmentKDU Penang University CollegeGeorge TownMalaysia
  2. 2.School of Computer SciencesUniversity Sains MalaysiaGelugorMalaysia

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