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Green Vehicle Routing Optimization Based on Carbon Emission and Multiobjective Hybrid Quantum Immune Algorithm

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  • Xiao-Hong Liu
  • Mi-Yuan Shan
  • Ren-Long Zhang
  • Li-Hong Zhang

Abstract

Green Vehicle Routing Optimization Problem (GVROP) is currently a scientific research problem that takes into account the environmental impact and resource efficiency. Therefore, the optimal allocation of resources and the carbon emission in GVROP are becoming more and more important. In order to improve the delivery efficiency and reduce the cost of distribution requirements through intelligent optimization method, a novel multiobjective hybrid quantum immune algorithm based on cloud model (C-HQIA) is put forward. Simultaneously, the computational results have proved that the C-HQIA is an efficient algorithm for the GVROP. We also found that the parameter optimization of the C-HQIA is related to the types of artificial intelligence algorithms. Consequently, the GVROP and the C-HQIA have important theoretical and practical significance.

Suggested Citation

  • Xiao-Hong Liu & Mi-Yuan Shan & Ren-Long Zhang & Li-Hong Zhang, 2018. "Green Vehicle Routing Optimization Based on Carbon Emission and Multiobjective Hybrid Quantum Immune Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-9, April.
    • Handle: RePEc:hin:jnlmpe:8961505
    DOI: 10.1155/2018/8961505
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    Cited by:

    1. Behnke, Martin & Kirschstein, Thomas & Bierwirth, Christian, 2021. "A column generation approach for an emission-oriented vehicle routing problem on a multigraph," European Journal of Operational Research, Elsevier, vol. 288(3), pages 794-809.

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