Abstract
This paper presents a simulated annealing algorithm that based on multiple search neighborhoods to solve a special kind of timetable problem. The new algorithm also can solve those problems that can be solved by local search algorithm. Various experimental results show that the new algorithm can actually give more satisfactory solutions than general simulated annealing algorithm can do.
1. This work is supported by the Science Foundation of Shanghai Municipal Commission of Science and Technology, grant No. 00JC14052. 2. This work is also supported by the peoject of Shanghai Municipal Commission of Education: Grid Technology E-Institute
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Li-Shan, K., Yun, X., Shi-Yong, Y., zhu-hua, L.: Nonnumerical Parallel Algorithm (The First Volume): Simulated Annealing Algorithm. Science Press, Beijing (1995)
Ling, W.: Intelligent Optimization Algorithms with Applications. Tsinghua University Press, Beijing (2001)
Kai-Cheng, L.: Introduce to Algorithm-— Algorithm Design and Analyze. Tsinghua University Press, Beijing (1996)
Burke, E.K.: Recent Research Directions in Automated Timetabling. Accepted for publication in European Journal of Operational Research – EJOR (2002)
Abramson, D.A., Abela, J.: A Parallel Genetic Algorithm for Solving the School Timetabling Problem. In: IJCAI workshop on Parallel Processing in AI, Sydney (August 1991); Also appearing in 15 Australian Computer Science Conference, 1 – 11, Hobart (February 1992)
Schaerf, A.: Local Search Techniques for Large High School Timetabling Problems. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems And Humans 29, 368–377 (1999)
Burke, E.K., Newall, J.P.: A Multistage Evolutionary Algorithm for the Timetable Problem. IEEE Transactions on Evolutionary Computation 3(1), 63–74 (1999)
Abramson, D.: Constructing School Timetables using Simulated Annealing: Sequential and Parallel Algorithms. Management Science 37(1), 98–113 (1991)
Hertz, A.: Tabu search for large scale timetabling problems. European Journal of Operational Research 54, 39–47 (1992)
Aarts, E., Lenstra, J.K.: Local Search in Combinatorial Optimization. John Wiley & Son, Chichester (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yan, H., Yu, SN. (2004). A Multiple-Neighborhoods-Based Simulated Annealing Algorithm for Timetable Problem. In: Li, M., Sun, XH., Deng, Q., Ni, J. (eds) Grid and Cooperative Computing. GCC 2003. Lecture Notes in Computer Science, vol 3033. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24680-0_81
Download citation
DOI: https://doi.org/10.1007/978-3-540-24680-0_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21993-4
Online ISBN: 978-3-540-24680-0
eBook Packages: Springer Book Archive