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An artificial bee colony algorithm for the capacitated vehicle routing problem

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  • Szeto, W.Y.
  • Wu, Yongzhong
  • Ho, Sin C.

Abstract

This paper introduces an artificial bee colony heuristic for solving the capacitated vehicle routing problem. The artificial bee colony heuristic is a swarm-based heuristic, which mimics the foraging behavior of a honey bee swarm. An enhanced version of the artificial bee colony heuristic is also proposed to improve the solution quality of the original version. The performance of the enhanced heuristic is evaluated on two sets of standard benchmark instances, and compared with the original artificial bee colony heuristic. The computational results show that the enhanced heuristic outperforms the original one, and can produce good solutions when compared with the existing heuristics. These results seem to indicate that the enhanced heuristic is an alternative to solve the capacitated vehicle routing problem.

Suggested Citation

  • Szeto, W.Y. & Wu, Yongzhong & Ho, Sin C., 2011. "An artificial bee colony algorithm for the capacitated vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 215(1), pages 126-135, November.
    • Handle: RePEc:eee:ejores:v:215:y:2011:i:1:p:126-135
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    References listed on IDEAS

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    1. Michel Gendreau & Alain Hertz & Gilbert Laporte, 1994. "A Tabu Search Heuristic for the Vehicle Routing Problem," Management Science, INFORMS, vol. 40(10), pages 1276-1290, October.
    2. Derigs, U. & Kaiser, R., 2007. "Applying the attribute based hill climber heuristic to the vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 177(2), pages 719-732, March.
    3. B. Bullnheimer & R.F. Hartl & C. Strauss, 1999. "An improved Ant System algorithm for theVehicle Routing Problem," Annals of Operations Research, Springer, vol. 89(0), pages 319-328, January.
    4. César Rego, 1998. "A Subpath Ejection Method for the Vehicle Routing Problem," Management Science, INFORMS, vol. 44(10), pages 1447-1459, October.
    5. Yu, Bin & Yang, Zhong-Zhen & Yao, Baozhen, 2009. "An improved ant colony optimization for vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 196(1), pages 171-176, July.
    6. Jean-François Cordeau & Michel Gendreau & Alain Hertz & Gilbert Laporte & Jean-Sylvain Sormany, 2005. "New Heuristics for the Vehicle Routing Problem," Springer Books, in: André Langevin & Diane Riopel (ed.), Logistics Systems: Design and Optimization, chapter 0, pages 279-297, Springer.
    7. Paolo Toth & Daniele Vigo, 2003. "The Granular Tabu Search and Its Application to the Vehicle-Routing Problem," INFORMS Journal on Computing, INFORMS, vol. 15(4), pages 333-346, November.
    8. Roberto Baldacci & Paolo Toth & Daniele Vigo, 2010. "Exact algorithms for routing problems under vehicle capacity constraints," Annals of Operations Research, Springer, vol. 175(1), pages 213-245, March.
    9. C.D. Tarantilis & C.T. Kiranoudis, 2002. "BoneRoute: An Adaptive Memory-Based Method for Effective Fleet Management," Annals of Operations Research, Springer, vol. 115(1), pages 227-241, September.
    10. J-F Cordeau & G Laporte & A Mercier, 2001. "A unified tabu search heuristic for vehicle routing problems with time windows," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(8), pages 928-936, August.
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