Abstract
Vehicle routing problem becomes more remarkable with the development of modern logistics. Ant colony and genetic algorithm are combined for solving vehicle routing problem. GA can overcome the drawback of premature and weak exploitation capabilities of ant colony and converge to the global optimal quickly. The performance of the proposed method as compared to those of the genetic-based approaches is very promising.
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
Dorigo, M.: Optimization, learning and natural algorithms. Ph.D. Thesis, Italy (1992)
Huang, K., Liao, C.: Ant colony optimization combined with taboo search for the job shop scheduling problem. Computers and Operations Research 35, 1030–1046 (2008)
Jian, S., Jian, S., Lin, B.M.T., Hsiao, T.: Ant colony optimization for the cell assignment problem in PCS networks. Computer and Operations Research 33, 1731–1740 (2006)
McMullen, P.R.: An ant colony optimization approach to addressing a JIT sequencing problem with multiple objectives. Artificial Intelligence 15(3), 309–317 (2001)
Bell, J.E., McMullen, P.R.: Ant Colony Optimization Techniques for the Vehicle Routing Problem. Advanced Engineering Informatics 1(8), 41–48 (2004)
Tavakkoli-Moghaddam, R., Safaei, N., Gholipour, Y.: A hybrid simulated annealing for capacitated vehicle routing problems with the independent route length. Applied Mathematics and Computation 176, 445–454 (2006)
Prins, C.: A simple and effective evolutionary algorithm for the vehicle routing problem. Computers & Operations Research 31, 1985–2002 (2004)
Brandao, J., Mercer, A.: A Tabu Search Algorithm for the Multi-Trip Vehicle Routing and Scheduling Problem. European Journal of Operational Research 100, 180–191 (1997)
Doerner, K.F., Hartl, R.F., Kiechle, G., Lucka, M., Reimann, M.: Parallel Ant Systems for the Capacitated Vehicle Routing Problem. In: Gottlieb, J., Raidl, G.R. (eds.) EvoCOP 2004. LNCS, vol. 3004, pp. 72–83. Springer, Heidelberg (2004)
Liu, L., Zhu, J.: The Research of Optimizing Physical Distribution Routing Based on Genetic Algorithm. Computer Engineering and Application 27, 227–229 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Peng, W., Zhou, CY. (2008). Solving Vehicle Routing Problem Using Ant Colony and Genetic Algorithm. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2008. Communications in Computer and Information Science, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85930-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-85930-7_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85929-1
Online ISBN: 978-3-540-85930-7
eBook Packages: Computer ScienceComputer Science (R0)