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Hybrid Differential Evolution Optimization for the Vehicle Routing Problem with Time Windows and Driver-Specific Times

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Abstract

This paper proposes a hybrid differential evolution (HDE) optimization for the vehicle routing problem with time windows and driver specific times (VRPTWDST), a typical problem of the general VRPTW which adopts service times and driver-specific travel times to model the degree of familiarity to different drivers with the service for customers. We carry out a scientific research for VRPTWDST on a comprehensive benchmark with randomly generated instances. By comparing the results of experiment and simulation, the knowledge for drivers in the routing problem may effectively enhances the efficiency for vehicle routes, especially, this effect that strengthen for the drivers with higher familiarity levels. Increased benefits are acquired if some familiar customers for the drivers are contiguous geographically. Furthermore, the higher number for the drivers that are familiar with the same district provides higher benefits compared to the scheme where drivers are only familiar with a smaller areas. The numerical examples of computational experiments are carried out to validate the proposed HDE can efficiently address VRPTWDST, yielding high-quality solutions with strong robustness.

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References

  1. Brito, J., Martínez, F. J., Moreno, J. A., et al. (2015). An ACO hybrid metaheuristic for close–open vehicle routing problems with time windows and fuzzy constraints. Applied Soft Computing, 32, 154–163.

    Article  Google Scholar 

  2. Dan, Z. G., Cai, L. N., & Zheng, L. (2009). Improved multi-agent system for the vehicle routing problem with time windows. Tsinghua Sci. Technol., 14(3), 407–412.

    Article  Google Scholar 

  3. Vidal, T. (2016). Technical note: Split algorithm in O(n) for the capacitated vehicle routing problem. Computers & Operations Research, 69, 40–47.

    Article  MathSciNet  MATH  Google Scholar 

  4. Kallehauge, B., Larsen, J., Madsen, O.B.G., & Solomon, M. (2005). Vehicle routing problem with time windows. In Column generation (pp. 67–98). Springer.

  5. Russell, R. A. (1995). Hybrid heuristics for the vehicle routing problem with time windows. Transportation Science, 29(2), 156–166.

    Article  MATH  Google Scholar 

  6. Hiermann, G., Puchinger, J., Ropke, S., & Hartl, R. F. (2016). The electric fleet size and mix vehicle routing problem with time windows and recharging stations. European Journal of Operational Research, 252, 995–1018.

    Article  MathSciNet  MATH  Google Scholar 

  7. Augerat, P., Belenguer, J. M., Benavent, E., Corberin, A., & Naddef, D. (1998). Separating capacity constraints in the CVRP using Tabu search. European Journal of Operational Research, 106, 546–557.

    Article  MATH  Google Scholar 

  8. Potvin, J. Y., Kervahut, T., Garcia, B. L., & Rousseau, J. M. (1996). The vehicle routing problem with time windows. Part I: Tabu search. INFORMS Journal on Computing, 8(2), 158–164.

    Article  MATH  Google Scholar 

  9. Yin, X. Y., & Yuan, Z. Y. (1999). Multiple vehicle routing with time windows using genetic algorithms. Proceedings of Evolutionary Computation. CEC, 3(1999), 1804–1808.

    Google Scholar 

  10. Ai, J., & Kachitvichyanukul, V. (2009). A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery. Computers & Operations Research, 36(5), 1693–1702.

    Article  MATH  Google Scholar 

  11. Madsen, O. B. G. (1983). Methods for solving combined two level location routing problems of realistic dimension. European Journal of Operational Research, 12(3), 295–301.

    Article  MathSciNet  Google Scholar 

  12. Christofides, N., & Eilon, S. (1969). An algorithm for the vehicle dispatching problem. Oper. Res. Q., 20, 309–318.

    Article  Google Scholar 

  13. Barreto, S., Ferreira, C., Paixao, J., & Santos, B. S. (2007). Using clustering analysis in a capacitated location-routing problem. European Journal of Operational Research, 179(3), 968–977.

    Article  MATH  Google Scholar 

  14. Dondo, R., & Cerda, J. (2007). A cluster-based optimization approach for the multi-depot heterogeneous fleet vehicle routing problem with time windows. European Journal of Operational Research, 176(3), 1478–1507.

    Article  MATH  Google Scholar 

  15. Tuzun, D., & Burke, L. I. (1999). A two-phase tabu search approach to the location routing problem. European Journal of Operational Research, 116, 87–99.

    Article  MATH  Google Scholar 

  16. Caballero, R., González, M., Guerrero, F. M., Molina, J., & Paralera, C. (2007). Solving a multiobjective location routing problem with a metaheuristic based on a tabu search. Application to a real case in Andalusia. European Journal of Operational Research, 177, 1751–1763.

    Article  MATH  Google Scholar 

  17. Prins, C., Prodhon, C., Ruiz, A., Soriano, P., & Wolfler-Calvo, R. (2007). Solving the capacitated location-routing problem by a cooperative Lagrangean relaxation-granular tabu search heuristic. Transportation Science, 41(4), 470–483.

    Article  Google Scholar 

  18. Wu, T. H., Low, C., & Bai, J. W. (2002). Heuristic solutions to multi-depot location-routing problems. Computers & Operations Research, 29, 1393–1415.

    Article  MATH  Google Scholar 

  19. Bouhafs, L., Hajjam, A., & Koukam, A. (2006). A combination of simulated annealing and ant colony system for the capacitated location-routing problem. Knowledge-Based and Intelligent Information and Engineering Systems LNCS, 4251, 409–416.

    Google Scholar 

  20. Lin, C. K. Y., & Kwok, R. C. W. (2006). Multi-objective metaheuristics for a location-routing problem with multiple use of vehicles on real data and simulated data. European Journal of Operational Research, 175(3), 1833–1849.

    Article  MATH  Google Scholar 

  21. Prins, C., Prodhon, C., & Wolfler-Calvo, R. (2006). Solving the capacitated location-routing problem by a GRASP complemented by a learning process and a path relinking. 4OR: A Quarterly Journal of Operations Research, 4, 221–238.

    Article  MathSciNet  MATH  Google Scholar 

  22. Marinakis, Y., & Marinaki, M. (2008). A bilevel genetic algorithm for a real life location routing problem. International Journal of Logistics: Research and Applications, 11(1), 49–65.

    Article  MATH  Google Scholar 

  23. Prins, C., Prodhon, C., & Wolfler-Calvo, R. (2006) A memetic algorithm with population management (MAPM) for the capacitated location-routing problem. In J. Gottlieb, & G. R. Raidl (Eds.), EvoCOP 2006, LNCS, 3906. Springer, Berlin, pp. 183–194.

  24. Duhamel, C., Lacomme, P., Prins, C., & Prodhon, C. (2010). A GRASP × ELS approach for the capacitated location-routing problem. Computers & Operations Research, 37, 1912–1923.

    Article  MATH  Google Scholar 

  25. Contardo, C., Cordeau, J. F., & Gendron, B. (2014). A GRASP + ILP-based metaheuristic for the capacitated location-routing problem. Journal of Heuristics, 20, 1–38.

    Article  MATH  Google Scholar 

  26. Duhamel, C., Lacomme, P., Prins, C., & Prodhon, C. (2008) A memetic approach for the capacitated location routing problem. In EU/meeting 2008, Troyes, France.

  27. Melechovsky, J., Prins, C., & Wolfler-Calvo, R. (2005). A metaheuristic to solve a location routing problem with non-linear costs. Journal of Heuristics, 11(5–6), 375–391.

    Article  MATH  Google Scholar 

  28. Belenguer, J. M., Benavent, E., Prins, C., Prodhon, C., & Wolfler-Calvo, R. (2011). A branchand-cut method for the capacitated location-routing problem. Computers & Operations Research, 38, 931–941.

    Article  MathSciNet  MATH  Google Scholar 

  29. Bouhafs, L., Hajjam, A., & Koukam, A. (2006). A combination of simulated annealing and ant colony system for the capacitated location-routing problem. Knowledge-Based and Intelligent Information and Engineering Systems LNCS, 4251, 409–416.

    Google Scholar 

  30. Ting, C. J., & Chen, C. H. (2013). A multiple ant colony optimization algorithm for the capacitated location routing problem. International Journal of Production Economics, 141, 34–44.

    Article  Google Scholar 

  31. Marinakis, Y., & Marinaki, M. (2008). A particle swarm optimization algorithm with path elinking for the location routing problem. Journal of Mathematical Modelling and Algorithms, 7(1), 59–78.

    Article  MathSciNet  MATH  Google Scholar 

  32. Marinakis, Y., Marinaki, M., & Matsatsinis, N. (2008). Honey bees mating optimization for the location routing problem. In IEEE international engineering management conference (IEMC-Europe 2008), Estoril, Portugal.

  33. Kennedy, J., & Eberhart, R. C. (2001). Swarm intelligence. San Francisco: Morgan Kaufmann.

    Google Scholar 

  34. Li, X. L., Shao, Z. J., & Qian, J. X. (2002). An optimizing method based on autonomous animates: Fish-swarm algorithm. Systems Engineering Theory and Practice, 22, 32–38.

    Google Scholar 

  35. Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: From natural to artificial system. New York, NY: Oxford University Press.

    MATH  Google Scholar 

  36. Karaboga, D., & Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied Math and Computation, 214, 108–132.

    Article  MathSciNet  MATH  Google Scholar 

  37. Storn, R., & Price, K. (1997). Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.

    Article  MathSciNet  MATH  Google Scholar 

  38. Tasgetiren, M. F., Liang, Y. C., Sevkli, M., & Gencyilmaz, G. (2004). Differential evolution algorithm for permutation flowshop sequencing problem with makespan criterion. In Proceedings of 4th international symposium on intelligent manufacturing systems, Sakarya, Turkey, pp. 442–452.

  39. Mohamed, A. W. (2015). An improved differential evolution algorithm with triangular mutation for global numerical optimization. Computers & Industrial Engineering, 85, 359–375.

    Article  Google Scholar 

  40. Hansen, P., & Mladenovic, N. (2001). Variable neighborhood search: Principles and applications. European Journal of Operational Research, 130, 449–467.

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work was supported by Yunnan Power Grid Co. Ltd science fund (Grant No.YNKL00000096).

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Authors and Affiliations

  1. Yunnan Power Grid Co. Ltd, Kunming, 650011, China

    Enlai Pu, Fei Wang & Zongliang Yang

  2. Kunming Enersun Technology Co. Ltd, Kunming, 650021, China

    Jun Wang, Zhiming Li & Xin Huang

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  1. Enlai Pu
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  2. Fei Wang
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  3. Zongliang Yang
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  4. Jun Wang
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  5. Zhiming Li
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  6. Xin Huang
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Correspondence to Xin Huang.

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Pu, E., Wang, F., Yang, Z. et al. Hybrid Differential Evolution Optimization for the Vehicle Routing Problem with Time Windows and Driver-Specific Times. Wireless Pers Commun 95, 2345–2357 (2017). https://doi.org/10.1007/s11277-017-4107-5

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  • DOI: https://doi.org/10.1007/s11277-017-4107-5

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