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An Artificial Physarum polycephalum Colony for the Electric Location-Routing Problem

Author

Listed:
  • Zhengying Cai

    (Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipments, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China)

  • Xiaolu Wang

    (Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipments, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China)

  • Rui Li

    (Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipments, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China)

  • Qi Gao

    (Hubei Province Engineering Technology Research Center for Construction Quality Testing Equipments, College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China)

Abstract

Electric vehicles invented for environmental sustainability are prone to adverse impacts on environmental sustainability due to the location and construction of their charging facilities. In this article, an artificial Physarum polycephalum colony is proposed to solve the novel challenging problem. First, the electric location-routing problem is established as a multi-objective network panning model with electric constraints to provide the optimal charging infrastructure layout, electric vehicle maintenance costs, and traffic conditions. The electric facility location problem and vehicle routing problem are integrated by integer programming, which considers the total distance, total time, total cost, total number of electric vehicles, and order fill rate. Second, an artificial Physarum polycephalum colony is introduced to solve the complex electric location-routing problem and includes the two basic operations of expansion and contraction. In the expansion operation, the optimal parent individuals will generate more offspring individuals, so as to expand the population size. In the contraction operation, only individuals with high fitness will be selected to survive through a merge sorting algorithm, resulting in a decrease in population size to the initial value. Through the iterative computing of the two main operations, the proposed artificial Physarum polycephalum colony can finally find the optimal solution to the objective function. Third, a benchmark test is designed for the electric location-routing problem by extracting the real road network from Tokyo, and the experimental results prove the effectiveness and applicability of this work.

Suggested Citation

  • Zhengying Cai & Xiaolu Wang & Rui Li & Qi Gao, 2023. "An Artificial Physarum polycephalum Colony for the Electric Location-Routing Problem," Sustainability, MDPI, vol. 15(23), pages 1-29, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16196-:d:1285319
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    References listed on IDEAS

    as
    1. Alqahtani, Mohammed & Hu, Mengqi, 2022. "Dynamic energy scheduling and routing of multiple electric vehicles using deep reinforcement learning," Energy, Elsevier, vol. 244(PA).
    2. Nai K. Yu & Wen Jiang & Rong Hu & Bin Qian & Ling Wang & Lianbo Ma, 2021. "Learning Whale Optimization Algorithm for Open Vehicle Routing Problem with Loading Constraints," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-14, December.
    3. Elnaz Ghorbani & Tristan Fluechter & Laura Calvet & Majsa Ammouriova & Javier Panadero & Angel A. Juan, 2023. "Optimizing Energy Consumption in Smart Cities’ Mobility: Electric Vehicles, Algorithms, and Collaborative Economy," Energies, MDPI, vol. 16(3), pages 1-19, January.
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    Cited by:

    1. Garside, Annisa Kesy & Ahmad, Robiah & Muhtazaruddin, Mohd Nabil Bin, 2024. "A recent review of solution approaches for green vehicle routing problem and its variants," Operations Research Perspectives, Elsevier, vol. 12(C).
    2. Zhengying Cai & Xiangyu Du & Tianhao Huang & Tianrui Lv & Zhiheng Cai & Guoqiang Gong, 2024. "Robotic Edge Intelligence for Energy-Efficient Human–Robot Collaboration," Sustainability, MDPI, vol. 16(22), pages 1-19, November.

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