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Optimization of Inventory Routing Problem in Refined Oil Logistics with the Perspective of Carbon Tax

Author

Listed:
  • Songyi Wang

    (College of Mechanical Engineering, Chongqing University, Chongqing 400044, China)

  • Fengming Tao

    (College of Mechanical Engineering, Chongqing University, Chongqing 400044, China
    School of Economics and Business Administration, Chongqing University, Chongqing 400044, China)

  • Yuhe Shi

    (School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China)

Abstract

In order to solve the optimization problem of the refined oil distribution system from the perspectives of low-carbon and environmental protection, this paper focuses on the characteristics of the secondary distribution of refined oil and combines it with the integrated optimization concept of refined oil distribution network, where a low-carbon inventory routing problem (LCIRP) model is constructed with the minimum total costs as the objective function on the basis of considering carbon emissions. An adaptive genetic algorithm combined with greedy algorithm is designed to solve the model, and an example is given to verify the effectiveness of the algorithm. Then, this paper solves the model with two parts by introducing a practical numerical example: in the first part, the LCIRP models with different carbon tax values are solved, which verifies the effectiveness of the model and proves that carbon tax policies can effectively reduce the carbon emissions in the secondary distribution network of refined oil; in the second part, the LCIRP models with the different maximum load capacity of oil tank trucks are solved, which provides the economic and environmentally friendly distribution schemes for refined oil distribution enterprises under the premise of carbon tax policies and load limitation. Finally, the emission reduction proposals that take into account both economic and environmental benefits are given respectively from the aspect of government environmental protection agencies and from the aspect of refined oil distribution enterprises.

Suggested Citation

  • Songyi Wang & Fengming Tao & Yuhe Shi, 2018. "Optimization of Inventory Routing Problem in Refined Oil Logistics with the Perspective of Carbon Tax," Energies, MDPI, vol. 11(6), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1437-:d:150473
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    References listed on IDEAS

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    Cited by:

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    2. Ling Shen & Fengming Tao & Songyi Wang, 2018. "Multi-Depot Open Vehicle Routing Problem with Time Windows Based on Carbon Trading," IJERPH, MDPI, vol. 15(9), pages 1-20, September.
    3. Gaoyuan Qin & Fengming Tao & Lixia Li, 2019. "A Vehicle Routing Optimization Problem for Cold Chain Logistics Considering Customer Satisfaction and Carbon Emissions," IJERPH, MDPI, vol. 16(4), pages 1-17, February.
    4. Wen-Hsien Tsai, 2019. "Modeling and Simulation of Carbon Emission-Related Issues," Energies, MDPI, vol. 12(13), pages 1-8, July.
    5. Abdul Salam Khan & Bashir Salah & Dominik Zimon & Muhammad Ikram & Razaullah Khan & Catalin I. Pruncu, 2020. "A Sustainable Distribution Design for Multi-Quality Multiple-Cold-Chain Products: An Integrated Inspection Strategies Approach," Energies, MDPI, vol. 13(24), pages 1-25, December.
    6. Xu, Xiaofeng & Wang, Chenglong & Zhou, Peng, 2021. "GVRP considered oil-gas recovery in refined oil distribution: From an environmental perspective," International Journal of Production Economics, Elsevier, vol. 235(C).
    7. Jing Chen & Pengfei Gui & Tao Ding & Sanggyun Na & Yingtang Zhou, 2019. "Optimization of Transportation Routing Problem for Fresh Food by Improved Ant Colony Algorithm Based on Tabu Search," Sustainability, MDPI, vol. 11(23), pages 1-22, November.
    8. Yuhe Shi & Zhenggang He, 2018. "Decision Analysis of Disturbance Management in the Process of Medical Supplies Transportation after Natural Disasters," IJERPH, MDPI, vol. 15(8), pages 1-18, August.
    9. Wang, Minxi & Wang, Yajie & Liu, Wei & Ma, Yu & Xiang, Longtao & Yang, Yunqi & Li, Xin, 2021. "How to achieve a win–win scenario between cost and customer satisfaction for cold chain logistics?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).

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