Advertisement

A Multi-Objective Approach for Master’s Thesis Committees Scheduling Using DMEA

  • Lam T. Bui
  • Viet Hoang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7673)

Abstract

In this paper, we propose a multi-objective approach for investigating the Multi-Objective Master’s Thesis Committees Scheduling (MMTCS), a practical scheduling problem that arises from our university. For this problem, We need to schedule for a large set of students, each needs an oral defense in front of a committee, given that the time slots, rooms and professors are limited. For it, we first try to derive a mathematical formulation of the problems as a multi-objective problem with a set of hard constraints. We used the satisfaction values of soft constraints as objectives. We adjusted our previous published version of multi-objective evolutionary algorithm to work with this combinatorial problem. We conducted a case study to investigate the problem using our newly multi-objective design. The results showed clearly the efficiency of the multi-objective approach on this problem. The non-dominated solutions showed trade-off between two objectives.

Keywords

Schedule Problem Time Slot Project Schedule Constraint Violation Hard Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abbasi, B., Shadrokh, S., Arkat, J.: Bi-objective resource-constrained project scheduling with robustness and makespan criteria. Applied Math. and Comp. 180, 146–152 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Belfares, L., Klibi, W., Lo, N., Guitouni, A.: Multi-objective tabu search based algorithm for progressive resource allocation. E. J. of Op. Res. 177, 1779–1799 (2007)CrossRefzbMATHGoogle Scholar
  3. 3.
    Blazewicz, J., Lenstra, J.K., Rinnooy Kan, A.H.G.: Scheduling subject to resource constraints: Classification and complexity. Discrete Applied Math. 5, 11–24 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Brucker, P.: Scheduling algorithms. Springer (2007)Google Scholar
  5. 5.
    Bui, L.T., Liu, J., Bender, A., Barlow, M., Wesolkowski, S., Abbass, H.A.: Dmea: a direction-based multiobjective evolutionary algorithm. Memetic Computing 3(4), 271–285 (2011)CrossRefGoogle Scholar
  6. 6.
    Bui, L.T., Michalewicz, Z., Parkinson, E., Abello, M.B.: Adaptation in dynamic environments: A case study in mission planning. IEEE Trans. Evolutionary Computation 16(2), 190–209 (2012)CrossRefGoogle Scholar
  7. 7.
    Carter, M.: Timetabling. In: Encyclopedia of Operations Research and Management Science, pp. 833–836. Kluwer Academic Publishers (2001)Google Scholar
  8. 8.
    Corne, D., Ross, P., Fang, H.L.: Evolutionary timetabling: Practice, prospects and work in progress. In: UK Planning and Scheduling SIG Workshop (1994)Google Scholar
  9. 9.
    Datta, D., Deb, K., Fonseca, C.M.: Solving class timetabling problem of iit kanpur using multi-objective evolutionary algorithm. Technical report, KanGAL, IIK Kanpur India,Report No. 2006006 (2006)Google Scholar
  10. 10.
    Lewis, R.: A survey of metaheuristic-based techniques for university timetabling problems. OR Spectrum 30(1), 167–190 (2008)CrossRefzbMATHGoogle Scholar
  11. 11.
    Nagar, A., Haddock, J., Heragu, S.: Multiple and bi-criteria scheduling: a literature survey. European Journal of Operational Research 81, 88–104 (1995)CrossRefzbMATHGoogle Scholar
  12. 12.
    Petrovic, S., Burke, E.K.: University timetabling. Handbook of Scheduling Algorithms, Models, and Performance Analysis 45, 1–23 (2004)MathSciNetGoogle Scholar
  13. 13.
    Pinedo, M.: Scheduling: Theory, Algorithms, and Systems, 2nd edn. Springer (2001)Google Scholar
  14. 14.
    Ross, P., Hart, E., Corne, D.: Genetic algorithms and timetabling (2003)Google Scholar
  15. 15.
    Slowinski, R.: Multiobjective project scheduling under multiple-category resource constraints. In: Advances in project Scheduling. Elsevier (1989)Google Scholar
  16. 16.
    Viana, A., de Sousa, J.P.: Using metaheuristics in multiobjective resource constrained project scheduling. European Journal of Operational Research 120, 359–374 (2000)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lam T. Bui
    • 1
  • Viet Hoang
    • 1
  1. 1.Le Quy Don Technical UniversityVietnam

Personalised recommendations