Skip to main content

Advertisement

SpringerLink
Log in

Three multi-start data-driven evolutionary heuristics for the vehicle routing problem with multiple time windows

  • Published:
Journal of Heuristics Aims and scope Submit manuscript

Abstract

This paper considers the vehicle routing problem with multiple time windows. It introduces a general framework for three evolutionary heuristics that use three global multi-start strategies: ruin and recreate, genetic cross-over of best parents, and random restart. The proposed heuristics make use of information extracted from routes to guide customized data-driven local search operators. The paper reports comparative computational results for the three heuristics on benchmark instances and identifies the best one. It also shows more than 16% of average cost improvement over current practice on a set of real-life instances, with some solution costs improved by more than 30%.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Al-Duoli, F., Rabadi, G.: Data mining based hybridization of Meta-RaPS. Procedia Comput. Sci. 36, 301–307 (2014)

    Article  Google Scholar 

  • Azi, N., Gendreau, M., Potvin, J.-Y.: An adaptive large neighborhood search for a vehicle routing problem with multiple routes. Comput. Oper. Res. 41, 167–173 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  • Beheshti, A.K., Hejazi, S.R., Alinaghian, M.: The vehicle routing problem with multiple prioritized time windows: a case study. Comput. Ind. Eng. 90, 402–413 (2015)

    Article  Google Scholar 

  • Berger, J., Barkaoui, M., Bräysy, O.: A route-directed hybrid genetic approach for the vehicle routing problem with time windows. Inf. Syst. Oper. Res. 41, 179–194 (2003)

    Google Scholar 

  • Belhaiza, S.: A data driven hybrid heuristic for the dial-a-ride problem with time windows. In: IEEE Symposium Series on Computational Intelligence SSCI 2017, Proceedings January, 1–8 (2018a)

  • Belhaiza, S.: A game theoretic approach for the real-life multiple-criterion vehicle routing problem with multiple time windows. IEEE Syst. J. 12, 1251–1262 (2018b)

    Article  Google Scholar 

  • Belhaiza, S., M’Hallah, R.: A Pareto non-dominated solution approach for the vehicle routing problem with multiple time windows. In: Proceedings of the 2016 IEEE Congress on Evolutionary Computation CEC, pp. 3515–3524 (2016)

  • Belhaiza, S., Hansen, P., Laporte, G.: Hybrid variable neighborhood tabu search heuristic for the vehicle routing problem with multiple time windows. Comput. Oper. Res. 52, 269–281 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  • Belhaiza, S., M’Hallah, R., Ben Brahim, G.: A new hybrid genetic variable neighborhood search heuristic for the vehicle routing problem with multiple time windows. In: IEEE Congress on Evolutionary Computation CEC, pp. 1319–1326 (2017)

  • BeTeLL.: Transportation & Logistics Labs., Canada (2018)

  • Bouziyane, B., Dkhissi, B., Cherkaoui, M.: A hybrid genetic algorithm for the static and dynamic vehicle routing problem with soft time windows. In: Proceedings of the Third IEEE International Conference on Logistics Operations Management GOL (2016)

  • Blanton, J.L. Jr., Wainwright, R.L.: Multiple vehicle routing with time and capacity constraints using genetic algorithms. In: Forrest, S. (ed.) Proceedings of the Fifth International Conference on Genetic Algorithm, pp. 452–459. Morgan Kaufmann Publisher, San Mateo, California (1993)

  • Calvet, L., Ferrer, A., Gomes, M.I., Juan, A., Masip, D.: Combining statistical learning with metaheuristics for the multi-depot vehicle routing problem with market segmentation. Comput. Ind. Eng. 94, 93–104 (2016)

    Article  Google Scholar 

  • Cakir, F.: Data-centric solution methodologies for vehicle routing problems. PhD thesis, University of Iowa (2016)

  • Cooray, P., Rupasinghe, P.: Machine learning-based parameter tuned genetic algorithm for energy minimizing vehicle routing problem. J. Ind. Eng. 2017, 1–13 (2017)

    Google Scholar 

  • Defryn, C., Sörensen, K.: A fast two-level variable neighborhood search for the clustered vehicle routing problem. Comput. Oper. Res. 83, 78–94 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  • De Jong, K.A.: An analysis of the behavior of a class of genetic adaptive systems. PhD dissertation, University of Michigan, Ann Arbor (1975)

  • Favaretto, D., Moretti, E., Pellegrini, P.: Ant colony system for a VRP with multiple time windows and multiple visits. J. Interdiscip. Math. 10, 263–284 (2007)

    Article  MATH  Google Scholar 

  • Ferreira, H.S., Bogue, E.T., Noronha, T.F., Belhaiza, S., Prins, C.: Variable neighborhood search for vehicle problem with multiple time windows. Electron. Notes Discrete Math. 66, 207–214 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  • Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, New York (1989)

    MATH  Google Scholar 

  • Hansen, P., Mladenović, N., Moreno-Perez, J.A.: Variable neighbourhood search: methods and applications. Ann. Oper. Res. 175, 367–407 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  • Hoogeboom, M., Dullaert, W.: Vehicle routing with arrival time diversification. Eur. J. Oper. Res. 275, 93–107 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  • Hoogeboom, M., Dullaert, W., Lai, D., Vigo, D.: Efficient neighborhood evaluations for the vehicle routing problem with multiple time windows. Technical report OR-18-2 DEI, University of Bologna, Italy (2018)

  • Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  • Liu, L., Zhou, H.: Hybridization of harmony search with variable neighborhood search for restrictive single-machine earliness/tardiness problem. Inf. Sci. 226, 68–92 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  • Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated local search: framework and applications. In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of Metaheuristics. Int. Ser. Oper. Res. Manag. Sci. 146, 363–397 (2015)

  • Michalet, J., Prins, C., Amodeo, L., Yalaoui, F., Vitry, G.: Multi-start iterated local search for the periodic vehicle routing problem with time window and time spread constraints on services. Comput. Oper. Res. 41, 196–207 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  • Mirabi, M.: A novel hybrid genetic algorithm for the multidepot periodic vehicle routing problem. Artif. Intell. Eng. Des. Anal. Manuf. AIEDAM 29, 45–54 (2015)

    Article  Google Scholar 

  • Nagata, Y., Bräysy, O., Dullaert, W.: A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows. Comput. Oper. Res. 37, 724–737 (2010)

    Article  MATH  Google Scholar 

  • Nazari, M., Oroojlooy, A., Snyder, L., Takac, M.: Deep reinforcement learning for solving the vehicle routing problem. In: 35th International Conference on Machine Learning, Stockholm, PMLR 80 (2018)

  • Pisinger, D., Ropke, S.: A general heuristic for vehicle routing problems. Comput. Oper. Res. 34, 2403–2435 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  • Potvin, J.-Y., Bengio, S.: The vehicle routing problem with time windows part II: genetic search. INFORMS J. Comput. 8, 165–172 (1996)

    Article  MATH  Google Scholar 

  • Prescott-Gagnon, E., Desaulniers, G., Rousseau, L.M.: A branch-and-price-based large neighborhood search algorithm for the vehicle routing problem with time windows. Networks 54, 190–204 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  • Prins, C.: A simple and effective evolutionary algorithm for the vehicle routing problem. Comput. Oper. Res. 31(1985–2002), 624 (2004)

    MathSciNet  MATH  Google Scholar 

  • Repoussis, P.P., Tarantilis, C.D., Ioannou, G.: Arc guided evolutionary algorithm for the vehicle routing problem with time windows. IEEE Trans. Evol. Comput. 13, 624–647 (2009)

    Article  Google Scholar 

  • Rasku, J., Kärkkäinen, T., Musliu, N.: Feature extractors for describing vehicle routing problem instances. In: OASIcs-OpenAccess Series in Informatics 50, Schloss Dagstuhl–Leibniz-Zentrum für Informatik (2016)

  • Sánchez-Oro, J., Pantrigo, J.J., Duarte, A.: Combining intensification and diversification strategies in VNS. An application to the vertex separation problem. Comput. Oper. Res. 52, 209–219 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  • Solomon, M.: Vehicle routing and scheduling with time window constraints: models and algorithms. Ph.D. Dissertation, Dept. of Decision Sciences, University of Pennsylvania (1983)

  • Steinhaus, M.: The application of the self-organizing map to the vehicle routing problem. PhD thesis, University of Rhode Island (2015)

  • Tangiah, S.: Vehicle routing with time windows using genetic algorithms. In: Chambers, L. (ed.) Application Handbook of Genetic Algorithms: New Frontiers, Volume II, pp. 253–277. CRC Press, Boca Raton (1995)

  • Toth, P., Vigo, D.: Vehicle Routing: Problems, Methods and Applications, 2nd edn. MOS-SIAM Series on Optimization, Philadelphia (2014)

  • Vidal, T., Crainic, T.G., Gendreau, M., Prins, C.: A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time windows. Comput. Oper. Res. 40, 475–489 (2013a)

    Article  MathSciNet  MATH  Google Scholar 

  • Vidal, T., Crainic, T.G., Gendreau, M., Prins, C.: Heuristics for multi-attribute vehicle routing problems: a survey and synthesis. Eur. J. Oper. Res. 231, 1–21 (2013b)

    Article  MathSciNet  MATH  Google Scholar 

  • Yang, N., Li. X-P., Zhu, J., Wang, Q.: Hybrid genetic-VNS algorithm with total flowtime minimization for the no-wait flowshop problem. In: International Conference on Machine Learning and Cybernetics (2008)

  • Zennaki, M., Ech-Cherif, A.: A new approach using machine learning and data fusion techniques for solving hard combinatorial optimization problems, vol. 1–5 (2008)

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Statistics, King Fahd University of Petroleum and Minerals, Dhahran, Kingdom of Saudi Arabia

    Slim Belhaiza

  2. Department of Statistics and Operations Research, Kuwait University, Kuwait City, Kuwait

    Rym M’Hallah

  3. Department of Computer Science, Prince Mohammad Bin Fahd University, Dhahran, Kingdom of Saudi Arabia

    Ghassen Ben Brahim

  4. CIRRELT and HEC Montréal, Montreal, Canada

    Gilbert Laporte

Authors
  1. Slim Belhaiza
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rym M’Hallah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ghassen Ben Brahim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gilbert Laporte
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Slim Belhaiza.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was partly funded by the Canadian Natural Sciences and Engineering Research Council under Grant 2015-06189. This support is gratefully acknowledged. Thanks are due to the referees for their valuable comments.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Belhaiza, S., M’Hallah, R., Ben Brahim, G. et al. Three multi-start data-driven evolutionary heuristics for the vehicle routing problem with multiple time windows. J Heuristics 25, 485–515 (2019). https://doi.org/10.1007/s10732-019-09412-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10732-019-09412-1

Keywords

Search

Navigation