Memetic Computing

, Volume 10, Issue 3, pp 307–319 | Cite as

A co-evolutionary approach using information about future requests for dynamic vehicle routing problem with soft time windows

  • Mohamed BarkaouiEmail author
Regular Research Paper


This paper presents a technique for integrating information about future customer requests to improve decision making for dynamic vehicle routing. We use a co-evolutionary approach to generate better waiting strategies such that the expected number of late-request customers who are served is maximized. An empirical evaluation of the proposed approach is performed within a previously reported hybrid genetic algorithm for the dynamic vehicle routing problem with time windows. Comparisons with other heuristic methods demonstrate the potential improvement that can be obtained through the application of the proposed approach.


Genetic algorithms Co-evolution Dynamic vehicle routing Waiting strategy 


  1. 1.
    Barkaoui M, Gendreau M (2013) An adaptive evolutionary approach for real-time vehicle routing and dispatching. J Comput Oper Res 40(7):1766–1776MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Bent R, Van Hentenryck P (2004) Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Oper Res 52:977–987CrossRefzbMATHGoogle Scholar
  3. 3.
    Bent R, Van Hentenryck P (2007) Waiting and relocation strategies in online stochastic vehicle routing. In: International joint conference on artificial intelligence, pp 1816–1821Google Scholar
  4. 4.
    Branke J, Middendorf M, Noeth G, Dessouky M (2005) Waiting strategies for dynamic vehicle routing. Transp Sci INFORMS 39(3):298–312CrossRefGoogle Scholar
  5. 5.
    Dondo R, Cerda J (2006) An MILP framework for dynamic vehicle routing problems with time windows. Lat Am Appl Res 36(4):255–261zbMATHGoogle Scholar
  6. 6.
    Gendreau M, Guertin F, Potvin J-Y, Taillard ÉD (1999) Parallel tabu search for real-time vehicle routing and dispatching. Transp Sci 33(4):381–390CrossRefzbMATHGoogle Scholar
  7. 7.
    Gendreau M, Jabali O, Rei W (2016) 50th anniversary invited article—future research directions in stochastic vehicle routing. Transp Sci 50(4):1163–1173CrossRefGoogle Scholar
  8. 8.
    Ghiani G, Laporte G, Manni E, Musmanno R (2008) Waiting strategies for the dynamic and stochastic traveling salesman problem. Int J Oper Res 5:233–241MathSciNetGoogle Scholar
  9. 9.
    Ghiani G, Manni E, Quaranta A, Triki C (2009) Anticipatory algorithms for same-day courier dispatching. Transp Res Part E 45:96–106CrossRefGoogle Scholar
  10. 10.
    Herrera F, Lozano M, Verdegay JL (1998) Tackling real-coded genetic algorithms: operators and tools for behavioural analysis. Artif Intell Rev 12(4):265–319CrossRefzbMATHGoogle Scholar
  11. 11.
    Hvattum LM, Løkketangen A, Laporte G (2006) Solving a dynamic and stochastic vehicle routing problem with a sample scenario hedging heuristic. Transp Sci 40(4):421–438CrossRefGoogle Scholar
  12. 12.
    Ichoua S, Gendreau M, Potvin JY (2006) Exploiting knowledge about future demands for real-time vehicle dispatching. Transp Sci 40:211–225CrossRefGoogle Scholar
  13. 13.
    Larsen A, Madsen O, Solomon MM (2002) Partially dynamic vehicle routing—models and algorithms. J Oper Res Soc 53:637–646CrossRefzbMATHGoogle Scholar
  14. 14.
    Larsen A, Madsen OBG, Solomon MM (2004) The a priori dynamic traveling salesman problem with time windows. Transp Sci 38:459–472CrossRefGoogle Scholar
  15. 15.
    Li H, Wang L, Hei X, Li W, Jiang Q (2017) A decomposition-based chemical reaction optimization for multi-objective vehicle routing problem for simultaneous delivery and pickup with time windows. Memet Comput. doi: 10.1007/s12293-016-0222-1
  16. 16.
    Meisel S (2011) Anticipatory optimization for dynamic decision making volume 51 of operations research/computer science interfaces series. Springer, New YorkCrossRefGoogle Scholar
  17. 17.
    Mitrovic-Minic S, Krishnamurti R, Laporte G (2004) Double-horizon based heuristics for the dynamic pickup and delivery problem with time windows. Transp Res Part B 38:669–685CrossRefGoogle Scholar
  18. 18.
    Mitrovic-Minic S, Laporte G (2004) Waiting strategies for the dynamic pickup and delivery problem with time windows. Transp Res Part B 38:635–655CrossRefGoogle Scholar
  19. 19.
    Pillac V, Gendreau M, Guéret C, Medaglia AL (2013) A review of dynamic vehicle routing problems. Eur J Oper Res 225(1):1–11MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Pureza V, Laporte G (2008) Waiting and buffering strategies for the dynamic pickup and delivery problem with time windows. INFOR 46:165–175Google Scholar
  21. 21.
    Thomas BW (2007) Waiting strategies for anticipating service requests from known customer locations. Transp Sci 41(3):319–331CrossRefGoogle Scholar
  22. 22.
    Tonda A, Lutton E, Squillero G (2012) A benchmark for cooperative coevolution. Memet Comput 4(4):263–277CrossRefGoogle Scholar
  23. 23.
    Ulmer MW, Mattfeld DC, Köster F (2015a) Budgeting time for dynamic vehicle routing with stochastic customer requests, (Working paper)Google Scholar
  24. 24.
    Ulmer MW, Mattfeld DC, Hennig M, Goodson JC (2015b) A rollout algorithm for vehicle routing with stochastic customer requests, logistics management, Mattfeld DC, Spengler TS, Brinkmann J, Grunewald M (Eds.), Lecture notes in logistics (LNL), SpringerGoogle Scholar
  25. 25.
    Van Hemert JI, La Poutré JA (2004) Dynamic routing with fruitful regions: models and evolutionary computation. Yao X, Burke E, Lozano JA, Smith J, Merelo-Guervos JJ, Bullinaria JA, Rowe J, Tino P, Kabaan A, Schwefel HP, (eds.) Parallel problem solving from nature VIII, Springer-Verlag, 690–699Google Scholar
  26. 26.
    Wang J-Q, Tong X-N, Li Z-M (2007) An improved evolutionary algorithm for dynamic vehicle routing problem with time windows. Comput Sci ICCS 4490:1147–1154Google Scholar
  27. 27.
    Yu X, Tang K, Chen T, Yao X (2009) Empirical analysis of evolutionary algorithms with immigrants schemes for dynamic optimization. Memet Comput 1(1):3–24CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Département d’informatique et de génie logicielUniversité Laval 1065, av. de la MédecineQuébecCanada

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