AN IMPROVED GENETIC ALGORITHM FOR SOLVING THE VEHICLE ROUTING PROBLEM WITH TIME WINDOWS

Tao-Shen Li, Jing-Li Wu

2005

Abstract

Many practical transport logistics and distribution problems can be formulated as the vehicle routing problem with time windows (VRPTM). The objective is to design an optimal set of routes that services all customers and satisfies the given constraints, especially the time window constraints. The complexity of the VRPTW requires heuristic solution strategies for most real-life instances. However, the VRPTM is a combination optimization problem and is a NP-complete problem, so we can’t get satisfying results when we use exact approaches and normal heuristic ones. In this paper, an improved genetic algorithm to solve the VRPTM problem is developed, which use an improved Route Crossover operator (RC’) and can meet the needs for solving VRPTM problem. Computational experiments show that the GA based on RC’ can obtain a general optimality for all evaluated indexes on the premise of satisfying every customer’s demand and its performance is superior to the GA based on PMX or RC.

References

  1. Ombuki, B. Nakamura, M. and Osamu, M., 2002 A Hybrid search based on genetic algorithms and tabu search for vehicle routing. In the 6th IASTED International Conference on Artificial Intelligence and Soft Computing, 176-181.
  2. Davis L., 1991, Handbook of genetic algorithms. Van Nostrand Reinhold.
  3. Solomon, M. M., and Desrosiers, J., 1988, Time window constrained routing and scheduling problem. Transportation Science, 22(1):1--13
  4. Lenstra, J. K., and Rinnooy Kan A. H. G., 1981, Complexity of vehicle routing and scheduling problems, Networks, 11,221-262.
  5. Savelsbergh M.W.P., 1985, Local search for routing problems with time windows. Annals of Operations Research 4, 285-30.
  6. Koskosidis, Yiannis, Warren B. Powell and Marius M. Solomon, 1992, An optimization based heuristic for vehicle routing and scheduling with time window Constraints. Transportation Science, 26(2), 69-85
  7. Desrosiers M. Desrosiers J.. Solomon M. M.,1992, A new optimization algorithm for the vehicle routing problem with time windows, Operation Research, 40,342--354
  8. Fisher M. L., Jornsten K. O., and Madsen O. B. G.,1997, Vehicle routing with time windows: two optimization algorithm, Operation Research, 45,488--492
  9. Kohl N., Madsen O.B.G., 1997, A optimization algorithm for the vehicle routing problem with time windows based on Lagrangian Relaxation, Operation Research, 40,342--354
  10. Russel R. A., 1995, Hybrid heuristics for the vehicle routing problem with time windows, Transportation Science, 29(2):156-166
  11. Holland J. H., 1975, Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor.
  12. DeJong, 1980, Adaptive system design: a genetic approach, IEEE Transaction on Systems, Man and Cybernetics,10(9):566--574
  13. Grefenstette J. J., 1986, Optimization of control parameter for genetic algorithms, IEEE Transactions on Systems, Man and Cybernetics,16(1):122--128
  14. Goldberg D., 1989, Genetic Algorithm in Search, Optimization, and Machine Learning, Addison Wesley Publishing Company Inc. New York
  15. Mitchell M. and Forrest S.1993, Genetic Algorithms and Artificial Life. [Online document],: http:// ww.santafe.edu/mm/GA.Alife.ps
  16. Bräysy O.2001 , Genetic Algorithms for the Vehicle Routing Problem with Time Widows, Internal Report STF42 A01021, SINTEF Applied Mathematics, Department of Optimisation, Norway.
  17. Thangiah, S.R., I.H. Osman, R. Vinayagamoorthy and T. Sun,1995, Algorithms for the Vehicle Routing Problems with Time Deadlines, American Journal of Mathematical and Management Sciences, 13, 323-355.
  18. Thangiah, S. R., 1995, Vehicle routing with time windows using genetic algorithms, in Application Handbook of Genetic Algorithms: New Frontiers, Volume II, L. Chambers (ed), 253-277, CRC Press
  19. Potvin J. Y. and Gengio S., 1996,The vehicle routing problem with time windows part II: genetic search. INFORMS Journal on Computing, 8(2):165-172
  20. Berger J., Salois M. and Begin R., 1998,A hybrid genetic algorithm for the vehicle routing problem with time windows. In Proceedings of the 12th Bienneal Conference of the Canadian Society for Computational Studies of Intelligence, Sringer-Verlag, 114--127
  21. Homberger J. and Gehring H.,1999,Two evolutionary meta-heuristics for the for the vehicle routing problem with time windows, INFORMS Journal on Computing, 37(3):297--318
  22. Bräysy, O. 1999, A new algorithm for the vehicle routing problem with time windows based on the hybridization of a genetic algorithm and routing construction heuristics. In Proceedings of the University of Vaasa, Research papers 227,
  23. Bräysy, O., Berger J. and Barkaoui M.,2000, A new hybrid evolutionary algorithm for the vehicle routing problem with time windows, Presented at the Route 2000-Workshop, Skodsborg, Denmark
  24. Table 4. Comparing the searching results using methods based on PMX, RC, RC' respectively
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Paper Citation


in Harvard Style

Li T. and Wu J. (2005). AN IMPROVED GENETIC ALGORITHM FOR SOLVING THE VEHICLE ROUTING PROBLEM WITH TIME WINDOWS . In Proceedings of the Second International Conference on e-Business and Telecommunication Networks - Volume 1: ICETE, ISBN 972-8865-32-5, pages 143-149. DOI: 10.5220/0001415101430149


in Bibtex Style

@conference{icete05,
author={Tao-Shen Li and Jing-Li Wu},
title={AN IMPROVED GENETIC ALGORITHM FOR SOLVING THE VEHICLE ROUTING PROBLEM WITH TIME WINDOWS},
booktitle={Proceedings of the Second International Conference on e-Business and Telecommunication Networks - Volume 1: ICETE,},
year={2005},
pages={143-149},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001415101430149},
isbn={972-8865-32-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on e-Business and Telecommunication Networks - Volume 1: ICETE,
TI - AN IMPROVED GENETIC ALGORITHM FOR SOLVING THE VEHICLE ROUTING PROBLEM WITH TIME WINDOWS
SN - 972-8865-32-5
AU - Li T.
AU - Wu J.
PY - 2005
SP - 143
EP - 149
DO - 10.5220/0001415101430149