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A Quantum Particle Swarm-Inspired Algorithm for Dynamic Vehicle Routing Problem

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Recent Developments in Mechatronics and Intelligent Robotics (ICMIR 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 690))

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Abstract

In order to solve the scheduling of dynamic vehicle routing problem, this paper establishes the simulation model to minimize the cost and stability value and maximize the loading rate. Then a quantum particle swarm-inspired algorithm is proposed. At first, it introduces the method based on the DCSC (double chains structure coding) including vehicle allocation chain and goods chain. Finally, the proposed method is applied to a dynamic simulation and the result of comparing with other classical algorithms verifies its effectiveness.

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Acknowledgements

This work is financially supported by the National Natural Science Foundation of China (Nos. 51579024, 61374114), Dr scientific research fund of Liaoning Province (No. 201601244), Liaoning Provincial Social Science Planning Foundation of China (No. L16BGL008), China. Postdoctoral Science Foundation Funded Project (No. 2017M611231), Dalian Social Science Planning Foundation of China (No. 2016dlskyb104).

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Authors and Affiliations

  1. Institute of Information, Dalian Maritime University, Dalian, 116023, China

    Bo Li & Guo Chen

  2. Institute of Software, Dalian Jiaotong University, Dalian, 116045, China

    Bo Li & Ning Tao

Authors
  1. Bo Li
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  2. Guo Chen
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  3. Ning Tao
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Corresponding author

Correspondence to Bo Li .

Editor information

Editors and Affiliations

  1. Shenyang Jianzhu University , Shenyang, Liaoning, China

    Feng Qiao

  2. SOA University , Bhubaneswar, Odisha, India

    Srikanta Patnaik

  3. Montclair State University , Montclair, New Jersey, USA

    John Wang

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Li, B., Chen, G., Tao, N. (2018). A Quantum Particle Swarm-Inspired Algorithm for Dynamic Vehicle Routing Problem. In: Qiao, F., Patnaik, S., Wang, J. (eds) Recent Developments in Mechatronics and Intelligent Robotics. ICMIR 2017. Advances in Intelligent Systems and Computing, vol 690. Springer, Cham. https://doi.org/10.1007/978-3-319-65978-7_28

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  • DOI: https://doi.org/10.1007/978-3-319-65978-7_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65977-0

  • Online ISBN: 978-3-319-65978-7

  • eBook Packages: EngineeringEngineering (R0)

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