Abstract
The problem of assigning customers to satellite channels is considered. Finding an optimal allocation of customers to satellite channels is a difficult combinatorial optimization problem and is shown to be NP-complete in an earlier study. We propose a genetic algorithm (GA) approach to search for the best/optimal assignment of customers to satellite channels. Various issues related to genetic algorithms such as solution representation, selection methods, genetic operators and repair of invalid solutions are presented. A comparison of this approach with the standard optimization method is presented to show the advantages of this approach in terms of computation time.
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.
References
Scoot, C.H., Skelton, O.G., Rolland, E.: Tactical and strategic models for satellite customer assignment. Journal of the Operational Research Society 51, 61–71 (2000)
Lee, H., Ahn, D.H., Kim, S.: Optimal routing in non-geostationary satellite ATM networks with intersatellite link capacity constraints. Journal of the Operational Research Society 54, 401–409 (2003)
Dell’Amico, M., Martello, S.: Open shop, satellite communication and a theorem by Egervary. Operations Research Letters 18, 209–211 (1931)
Prins, C.: An overview of scheduling problems arising in satellite communications. Journal of the Operational Research Society 45, 611–623 (1994)
Cattrysse, D., Van Wassenhove, L.N.: A survey of algorithms for the generalized assignment problem. European Journal of Operational Research 60, 260–272 (1992)
Montgomery, D.C., Johnson, L.A.: Operations Research in Production Planning Scheduling and Inventory Control. John Wiely & Sons, Chichester (1974)
Holland, H.J.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor (1975)
Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, New York (1989)
David, L.: Handbook of Genetic Algorithms. Van Nostrand Reingold, New York (1991)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. AI Series. Springer, New York (1994)
Chu, P.C., Beasley, J.E.: A genetic algorithm for the generalized assignment problem. Computers and Operations Research 24, 17–23 (1997)
Etiler, O., Toklu, B., Atak, M., Wilson, J.: A genetic algorithm for flow shop scheduling problems. Journal of the Operational Research Society 55, 830–835 (2004)
Kidwell, M.D., Cook, D.J.: Genetic algorithm for dynamic task scheduling. In: Proceedings of the International Phoenix Conference on Computers and Communication, pp. 61–67 (1994)
Kim, S.S., Smith, A.E., Lee, J.H.: A memetic algorithm for channel assignment in wireless FDMA systems. Computers and Operations Research. Corrected proof available online (in press)
Zomaya, A.Y., Ward, C., Macey, B.: Genetic scheduling for parallel processor systems: Comparative studies and performance issues. IEEE Transactions on Parallel and Distributed Systems 10, 795–812 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, S.S., Kim, H.J., Mani, V., Kim, C.H. (2006). Genetic Algorithm for Satellite Customer Assignment. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_106
Download citation
DOI: https://doi.org/10.1007/11893295_106
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
Online ISBN: 978-3-540-46485-3
eBook Packages: Computer ScienceComputer Science (R0)