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.
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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)
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)
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)
Brucker, P.: Scheduling algorithms. Springer (2007)
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)
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)
Carter, M.: Timetabling. In: Encyclopedia of Operations Research and Management Science, pp. 833–836. Kluwer Academic Publishers (2001)
Corne, D., Ross, P., Fang, H.L.: Evolutionary timetabling: Practice, prospects and work in progress. In: UK Planning and Scheduling SIG Workshop (1994)
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)
Lewis, R.: A survey of metaheuristic-based techniques for university timetabling problems. OR Spectrum 30(1), 167–190 (2008)
Nagar, A., Haddock, J., Heragu, S.: Multiple and bi-criteria scheduling: a literature survey. European Journal of Operational Research 81, 88–104 (1995)
Petrovic, S., Burke, E.K.: University timetabling. Handbook of Scheduling Algorithms, Models, and Performance Analysis 45, 1–23 (2004)
Pinedo, M.: Scheduling: Theory, Algorithms, and Systems, 2nd edn. Springer (2001)
Ross, P., Hart, E., Corne, D.: Genetic algorithms and timetabling (2003)
Slowinski, R.: Multiobjective project scheduling under multiple-category resource constraints. In: Advances in project Scheduling. Elsevier (1989)
Viana, A., de Sousa, J.P.: Using metaheuristics in multiobjective resource constrained project scheduling. European Journal of Operational Research 120, 359–374 (2000)
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Bui, L.T., Hoang, V. (2012). A Multi-Objective Approach for Master’s Thesis Committees Scheduling Using DMEA. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds) Simulated Evolution and Learning. SEAL 2012. Lecture Notes in Computer Science, vol 7673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34859-4_45
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DOI: https://doi.org/10.1007/978-3-642-34859-4_45
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