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Reseach Article

Automated Timetable Generation using Bee Colony Optimization

by Deeptimanta Ojha, Rajesh Kumar Sahoo, Satyabrata Das
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 10 - Number 9
Year of Publication: 2016
Authors: Deeptimanta Ojha, Rajesh Kumar Sahoo, Satyabrata Das
10.5120/ijais2016451553

Deeptimanta Ojha, Rajesh Kumar Sahoo, Satyabrata Das . Automated Timetable Generation using Bee Colony Optimization. International Journal of Applied Information Systems. 10, 9 ( May 2016), 38-43. DOI=10.5120/ijais2016451553

@article{ 10.5120/ijais2016451553,
author = { Deeptimanta Ojha, Rajesh Kumar Sahoo, Satyabrata Das },
title = { Automated Timetable Generation using Bee Colony Optimization },
journal = { International Journal of Applied Information Systems },
issue_date = { May 2016 },
volume = { 10 },
number = { 9 },
month = { May },
year = { 2016 },
issn = { 2249-0868 },
pages = { 38-43 },
numpages = {9},
url = { https://www.ijais.org/archives/volume10/number9/893-2016451553/ },
doi = { 10.5120/ijais2016451553 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:03:10.770577+05:30
%A Deeptimanta Ojha
%A Rajesh Kumar Sahoo
%A Satyabrata Das
%T Automated Timetable Generation using Bee Colony Optimization
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 10
%N 9
%P 38-43
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Timetable problem is a NP-hard problem where different constraints and various resources are applied but the resources are limited. Optimization problem is a technique which can handle different constraints. This paper focuses the Bee colony Optimization (BCO) for finding the optimal solutions of course time table.BCO is a Meta heuristic optimization scheme where NP-hard with different parameter settings are solved. There are two objectives, first objective is to provide the introduction to timetabling and second objective is the BCO and their variations with timetable design. The proposed algorithm is used to construct the course time table and optimized that time table.

References
  1. Adrianto Dennise, “Comparison Using Particle Swarm Optimization and Genetic Algorithm for Timetable Scheduling”, Journal of Computer Science 10 (2),2014 pp- 341-346.
  2. AI.Betar,M.A. and A.T.Khader,A hybrid harmony search for university course timetabling. Proceedings of the Multidisciplinary International Conference on Scheduling: Theory and Applications, 2009, pp.157-179.
  3. Bhaduri,A.,University time table scheduling using genetic artificial immune network. Proceedings of the international Conference on Advances in Recent Technologies in Communication and Computing,IEEE Xplore Press,2009,pp.289-292.
  4. Campana Emilio Fortunato, Diez Matteo, Fasano Giovanni, and Peri Daniele, ”Initial Particles Position for PSO, in Bound Constrained Optimization”, 2013, ICSI, Part I, LNCS 7928, Springer-Verlag Berlin Heidelberg.
  5. Chu Shu-Chuan, Chen Yi-Tin, Ho Jiun-Huei, “Timetable Scheduling Using Particle Swarm Optimization”, Proceedings of the First International Conference on Innovation Computing, Information and Control 2006, ICICIC.
  6. D. Dervis Karaboga, An Idea Based On Honey Bee Swarm for Numerical Optimization 2005, Technical Report-TR06,Erciyes University, Engineering Faculty, Computer Engineering Department.
  7. Daskalaki Sophia, Birbas Theodore, Housos Efthymios, “An Integer Programming Formulation for a Case Study in University Timetabling”, European Journal of Operating Research: Elsevier, 2004.
  8. Ho Sheau Fen Irene, Safaai Deris, Hashim Zaiton Siti Mohd, “University Course Timetable Planning using Hybrid Particle Swarm Optimization”, Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation 2009, ISBN: 978-1-60558-326-6.
  9. Ioannis X. Tassopoulos, Grigorios N. Beligiannis, “A hybrid particle swarm optimization based algorithm for high school timetabling problems”, in applied soft computing 12(2012),pp- 3472-3489.
  10. Lai,L.F.,C.C. Wu.,N.L.Hsueh,l.t.Huang and S.F Hwang,An Artificial intelligence approach to course timetabling. International Journal Artificial Intelligence Tools, 2008,17:223-240.
  11. Montero Elizabeth, Riff Maria-Christina, Altamirano Leopoldo, “A PSO Algorithm to Solve a Real Course+Exam Timetabling Problem”,2011 ICSI: International Conference on Swarm Intelligence, France.
  12. Mudjihartono Paulus, Triadi Gunawan Wahyu, Jin AI The, “University Timetabling Problems With Customizable Constraints Using Particle Swarm Optimization Method”, International Proceeding: International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT), Petra University, Indonesia. 2010, ISBN: 978-602-97124-0-7.
Index Terms

Computer Science
Information Sciences

Keywords

Optimization Bee colony Optimization (BCO) Course time table