Explaining Heuristic Performance Differences for Vehicle Routing Problems with Time windows

  • Jeroen CorstjensEmail author
  • An Caris
  • Benoît Depaire
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11353)


Heuristic algorithms are most commonly applied in a competitive context in which the algorithm is tested on well-known benchmarks of some problem application with the objective of obtaining better performance results than the state-of-the-art. Focusing on characterising heuristic algorithm behaviour to acquire insight and knowledge of how these solution procedures operate given a certain problem application, is a rarely applied research context. In this paper we strive to obtain a better understanding of heuristic performance. Based on an exploratory analysis of a large neighbourhood search algorithm applied on instances of the vehicle routing problem with time windows, we perform a detailed study on one of the detected patterns and seek to explain it. We learn that a regret operator functions best when it can take into account many and good alternatives, which is not the case when removing geographical clusters of customers. In the latter case some customers become isolated and have no feasible insertion option in one of the existing routes at the start of the repair phase. Their insertion is therefore postponed, but we show that it is beneficial for performance to assign them a higher priority through the creation of individual routes.



The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation Flanders (FWO) and the Flemish Government - department EWI.


  1. 1.
    Bartz-Beielstein, T., Preuss, M.: The future of experimental research. Experimental Methods for the Analysis of Optimization Algorithms, pp. 17–49. Springer, Berlin (2010)Google Scholar
  2. 2.
    Birattari, M.: Tuning Metaheuristics. Springer, Berlin (2009)Google Scholar
  3. 3.
    Box, G.E.P., Hunter, J.S., Hunter, W.G.: Statistics for Experimenters: Design, Innovation, and Discovery. Wiley-Interscience, New Jersey (2005)Google Scholar
  4. 4.
    Corstjens, J., Depaire, B., Caris, A., Sörensen, K.: A multilevel evaluation method for heuristics with an application to the VRPTW. Sumbitted for publication (2017)Google Scholar
  5. 5.
    Fawcett, C., Hoos, H.H.: Analysing differences between algorithm configurations through ablation. J. Heuristics 22(4), 431–458 (2015)CrossRefGoogle Scholar
  6. 6.
    Gelman, A., Hill, J.: Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, Cambridge (2006)Google Scholar
  7. 7.
    Hooker, J.N.: Testing heuristics: we have it all wrong. J. Heuristics 1(1), 33–42 (1995)CrossRefGoogle Scholar
  8. 8.
    Hoos, H.H.: Automated algorithm configuration and parameter tuning. In: Hamadi, Y., Monfroy, E., Saubion, F. (eds.) Autonomous Search, pp. 37–71. Springer, Berlin (2011)Google Scholar
  9. 9.
    Hoos, H.H.: Programming by optimization. Commun. ACM 55(2), 70–80 (2012)CrossRefGoogle Scholar
  10. 10.
    Hox, J.J., Moerbeek, M.: Schoot, R.v.d.: Multilevel Analysis: Techniques and Applications, 2nd edn, Routledge, Abingdon (2010)Google Scholar
  11. 11.
    Hutter, F., Hoos, H., Leyton-Brown, K.: An efficient approach for assessing hyperparameter importance. In: ICML, pp. 754–762 (2014)Google Scholar
  12. 12.
    Hutter, F., Hoos, H.H., Leyton-Brown, K.: Identifying key algorithm parameters and instance features using forward selection. In: LION 7. Lecture Notes in Computer Science, vol. 7997, pp. 364–381. Springer, Berlin (2013)Google Scholar
  13. 13.
    Pisinger, D., Ropke, S.: A general heuristic for vehicle routing problems. Comput. Oper. Res. 34(8), 2403–2435 (2007)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Potvin, J.Y., Rousseau, J.M.: A parallel route building algorithm for the vehicle routing and scheduling problem with time windows. Eur. J. Oper. Res. 66(3), 331–340 (1993)CrossRefGoogle Scholar
  15. 15.
    Rardin, R.L., Uzsoy, R.: Experimental evaluation of heuristic optimization algorithms: a tutorial. J. Heuristics 7(3), 261–304 (2001)CrossRefGoogle Scholar
  16. 16.
    Ropke, S., Pisinger, D.: An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Trans. Sci. 40(4), 455–472 (2006)CrossRefGoogle Scholar
  17. 17.
    Shaw, P.: Using constraint programming and local search methods to solve vehicle routing problems. In: Maher, M., Puget, J.F. (eds.) Principles and Practice of Constraint Programming CP98. Lecture Notes in Computer Science, vol. 1520, pp. 417–431. Springer, Berlin (1998)CrossRefGoogle Scholar
  18. 18.
    Smith-Miles, K., Bowly, S.: Generating new test instances by evolving in instance space. Comput. Oper. Res. 63, 102–113 (2015)MathSciNetCrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.UHasselt, Research Group LogisticsDiepenbeekBelgium

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