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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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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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  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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