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Multi-Depot Vehicle Routing Optimization Considering Energy Consumption for Hazardous Materials Transportation

Author

Listed:
  • Cunrui Ma

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

  • Baohua Mao

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

  • Qi Xu

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

  • Guodong Hua

    (Bank of China, Beijing 100818, China)

  • Sijia Zhang

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

  • Tong Zhang

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Focusing on the multi-depot vehicle routing problem (MDVRP) for hazardous materials transportation, this paper presents a multi-objective optimization model to minimize total transportation energy consumption and transportation risk. A two-stage method (TSM) and hybrid multi-objective genetic algorithm (HMOGA) are then developed to solve the model. The TSM is used to find the set of customer points served by each depot through the global search clustering method considering transportation energy consumption, transportation risk, and depot capacity in the first stage, and to determine the service order of customer points to each depot by using a multi-objective genetic algorithm with the banker method to seek dominant individuals and gather distance to keep evolving the population distribution in the second stage, while with the HMOGA, customer points serviced by the depot and the serviced orders are optimized simultaneously. Finally, by experimenting on two cases with three depots and 20 customer points, the results show that both methods can obtain a Pareto solution set, and the hybrid multi-objective genetic algorithm is able to find better vehicle routes in the whole transportation network. Compared with distance as the optimization objective, when energy consumption is the optimization objective, although distance is slightly increased, the number of vehicles and energy consumption are effectively reduced.

Suggested Citation

  • Cunrui Ma & Baohua Mao & Qi Xu & Guodong Hua & Sijia Zhang & Tong Zhang, 2018. "Multi-Depot Vehicle Routing Optimization Considering Energy Consumption for Hazardous Materials Transportation," Sustainability, MDPI, vol. 10(10), pages 1-21, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3519-:d:173011
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    References listed on IDEAS

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    5. Yan Sun & Xinya Li & Xia Liang & Cevin Zhang, 2019. "A Bi-Objective Fuzzy Credibilistic Chance-Constrained Programming Approach for the Hazardous Materials Road-Rail Multimodal Routing Problem under Uncertainty and Sustainability," Sustainability, MDPI, vol. 11(9), pages 1-27, May.
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    7. Zhongxin Zhou & Minghu Ha & Hao Hu & Hongguang Ma, 2021. "Half Open Multi-Depot Heterogeneous Vehicle Routing Problem for Hazardous Materials Transportation," Sustainability, MDPI, vol. 13(3), pages 1-17, January.

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