Skip to main content

Advertisement

SpringerLink
Log in

Dynamic vehicle routing using genetic algorithms

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Many difficult combinatorial optimization problems have been modeled as static problems. However, in practice, many problems are dynamic and changing, while some decisions have to be made before all the design data are known. For example, in the Dynamic Vehicle Routing Problem (DVRP), new customer orders appear over time, and new routes must be reconfigured while executing the current solution. Montemanni et al. [1] considered a DVRP as an extension to the standard vehicle routing problem (VRP) by decomposing a DVRP as a sequence of static VRPs, and then solving them with an ant colony system (ACS) algorithm.

This paper presents a genetic algorithm (GA) methodology for providing solutions for the DVRP model employed in [1]. The effectiveness of the proposed GA is evaluated using a set of benchmarks found in the literature. Compared with a tabu search approach implemented herein and the aforementioned ACS, the proposed GA methodology performs better in minimizing travel costs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Montemanni R, Gambardella LM, Rizzoli AE, Donati AV (2005) A new algorithm for a dynamic vehicle routing problem based on Ant colony system. J Comb Optim 10:327–343

    Article  MATH  MathSciNet  Google Scholar 

  2. Fleischmann B, Gnutzmann S, Sandvob E (2004) Dynamic vehicle routing based on online traffic information. Trans Sci 38(4):420–433

    Article  Google Scholar 

  3. Bianchi L (2000) Notes on dynamic vehicle routing—the state of the art. Technical Report IDSIA-05-01 Istituto Dalle Molle di Studi sull’Intelligenza Artificiale

  4. Gendreau M, Potvin J-Y (1998) Dynamic vehicle routing and dispatching. In: Crainic TG, Lapoorte G (eds) Fleet management and logistics, pp 115–226

  5. Gendreau M, Guertin F, Potvin J-Y, Seguin R (2006) Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick-ups and deliveries. Trans Res, Part C 14(3):157–174

    Article  Google Scholar 

  6. Bent R, Hentenryck P (2003) Van dynamic vehicle routing with stochastic requests. In: Proceedings of the eighteenth international joint conference on artificial intelligence (IJCAI-2003). Acapulco, Mexico, pp 1362–1363

  7. Kilby P, Prosser P, Shaw P (1998) Dynamic VRPs: a study of scenarios. Technical Report APES-0-1998, University of Strathclyde

  8. Gendreau M, Geurtin F, Potvin JY, Taillard E (1999) Parallel tabu search for real-time vehicle routing and dispatching. Trans Sci 33(4):381–390

    MATH  Google Scholar 

  9. Ichoua S, Gendreau M, Potvin J-Y (2000) Diversion issues in real-time vehicle dispatching. Trans Sci 34(4):426–438

    Article  MATH  Google Scholar 

  10. Ichoua S, Gendreau M, Potvin JY (2005) Exploiting knowledge about future demands for real-time vehicle dispatching. Forthcoming in Trans Sci

  11. Zhu K, Ong K (2000) A reactive method for real time dynamic vehicle routing problems. In: 12th ICTAI 2000. Vancouver, Canada

  12. Coslovich L, Pesenti R, Ukovich W (2006) A two-phase insertion technique of unexpected customers for a dynamic dial-a-ride problem. Eur J Oper Res 175(3):1605–1615

    Article  MATH  Google Scholar 

  13. Caramia M, Italiano GF, Oriolo G, Pacifici A, Perugia A (2001) Routing a fleet of vehicles for dynamic combined pick-up and deliveries services. In: Symposium on operations research, Duisburg, Germany, 3–5 Sept. 2001. Springer-Verlag, Berlin, pp 3–8

  14. Krumke SO, Rambau J, Torres LM (2002) Real-time dispatching of guided and unguided automobile service units with soft time windows. In: Möhring R et al. (eds) Appeared in: Algorithms—ESA 2002. Proceedings of the 10th european symposium on algorithms, Rome, Italy, September 17–21, 2002. Springer, Berlin 2002. LNCS 2461, pp 637–648

  15. Savelsbergh MWP, Sol M (1998) DRIVE: dynamic routing of independent vehicles georgia institute of technology. Oper Res 46:474–490

    Article  MATH  Google Scholar 

  16. Larsen A (2003) The a-priori dynamic traveling salesman problem with time windows. In: ODYSEUS 2003—second international workshop on freight transportation and logistics. Palermo, Italien, pp 27–30

  17. Guntsch M, Middendorf M (2002) Applying population based ACO to dynamic optimization problems. In: Dorigo et al (eds), ANTS 2002, Lecture Notes in Computer Science 2463, pp 111–122

  18. Psaraftis H (1988) Dynamic vehicle routing problems. In: Golden BL, Assad AA (eds) Vehicle routing: methods and studies, pp 223–248

  19. Psaraftis H (1995) Dynamic vehicle routing: status and prospects. Ann Oper Res 61:143–164

    Article  MATH  Google Scholar 

  20. Golden BL, Wasil EA, Kelly JP, Chao IM (1998) The impact of metaheuristics on the solving the vehicle routing problem: algorithms, problem sets, and computational results. In: Fleet management and logisitics, Kluwer, Dordrecht pp 33–56

    Google Scholar 

  21. Toth P, Vigo D (2003) The granular Tabu search and its application to the VRP. Research Report OR-98–9, DEIS, University of Bologna, 1998, to appear in INFORMS Journal of Computing. ACM

  22. Garey MR, Johnson DS (1979) Computers and intractability, a guide to the theory of NP-completeness. W. H. Freeman and Company

  23. Holland JH (1992) Adaptation in natural and artificial systems university of michigan press Second Edition. MIT Press

  24. Branke J (2002) Evolutionary optimization in dynamic environments. Kluwer Academic Publishers

  25. Ombuki B, Ross BJ, Hanshar F (2006) Multi-objective genetic algorithms for vehicle routing problem with time windows. Appl Intell 24(1):17–30

    Article  Google Scholar 

  26. Thangiah SR, Salhi S (2001) Genetic clustering: an adaptive heuristic for the multi-depot vehicle routing problem. Appl Artif Intell 15:361–383

    Article  Google Scholar 

  27. Ombuki B, Nakamura M, Maeda O (2002) A hybrid search based on genetic algorithms and Tabu search for vehicle routing. In: Banff AB, Leung H (eds) 6th IASTED intl. conf. on artificial intelligence and soft computing (ASC 2002). ACTA Press, pp 176–181

  28. Whitley D, Starkweather T, Shaner D (1989) Scheduling problems and traveling salesman: the genetic edge recombination. In: Proceedings of the 3rd international conference on genetic algorithms, pp 133–140

  29. Ombuki B, Hanshar F (2004) An effective genetic algorithm for the multi-depot vehicle routing problem. submitted, Preliminary version at Brock COSC TR CS-04-10

  30. Mitchell M (1996) An introduction to genetic algorithms. MIT Press

  31. Taillard ÉD (1994) Parallel iterative search methods for vehicle routing problems. Networks 23(8):661–673

    Article  Google Scholar 

  32. Christophides N, Beasley J (1984) The period routing problem. Networks 14:237–256

    Article  Google Scholar 

  33. Fisher ML (1995) Vehicle routing. Handbooks Oper Res Manage Sci 8

  34. Glover F (1990)Tabu search—part II. ORSA J Comput 2(1), Winter 4–32

    Google Scholar 

  35. Rochat Y, Taillard ED (1995) Probabilistic diversification and intensification in local search for vehicle routing. J Heuristics 1:147–167

    Article  MATH  Google Scholar 

  36. Nanry W, Barnes J (2000) Solving the pickup and delivery problem with time windows using Tabu search. Trans Res Part B 34:107–121

    Article  Google Scholar 

  37. Attanassio A, Cordeau JF, Ghiani G, Laporte G (2004) Parallel Tabu search heuristics for the dynamic multi-vehicle dial-a-ride problem. Parallel Comput 30(3):377–387

    Article  Google Scholar 

  38. Ibrahim OH (1993) Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problems. Ann Opera Res 41:421–451

    Article  MATH  Google Scholar 

  39. Larsen A (2000) The dynamic vehicle routing problem. Ph.D. Thesis, Technical University of Denmark

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Brock University, St. Catharines, ON, Canada, L2S 3A1

    Franklin T. Hanshar & Beatrice M. Ombuki-Berman

Authors
  1. Franklin T. Hanshar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Beatrice M. Ombuki-Berman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Beatrice M. Ombuki-Berman.

Additional information

Franklin T. Hanshar is currently a M.Sc. student in the Department of Computing and Information Science at the University of Guelph, Ontario, Canada. He received a B.Sc. degree in Computer Science from Brock University in 2005. His research interests include uncertain reasoning, optimization and evolutionary computation.

Beatrice Ombuki-Berman is currently an Associate Professor in the Department of Computer Science at Brock University, Ontario, Canada. She obtained a PhD and ME in Information Engineering from University of The Ryukyus, Okinawa, Japan in 2001 and 1998, respectively. She received a B.Sc. in Mathematics and Computer Science from Jomo Kenyatta University, Nairobi, Kenya. Her primary research interest is evolutionary computation and applied optimization. Other research interests include neural networks, machine learning and ant colony optimization.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hanshar, F.T., Ombuki-Berman, B.M. Dynamic vehicle routing using genetic algorithms. Appl Intell 27, 89–99 (2007). https://doi.org/10.1007/s10489-006-0033-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-006-0033-z

Keywords

Search

Navigation