IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i18p8207-d1482191.html
   My bibliography  Save this article

Cold Chain Distribution Route Optimization for Mixed Vehicle Types of Fresh Agricultural Products Considering Carbon Emissions: A Study Based on a Survey in China

Author

Listed:
  • Shuangli Pan

    (School of Logistics, Central South University of Forestry and Technology, Changsha 410004, China
    Hunan Key Laboratory of Intelligent Logistics Technology, Changsha 410004, China)

  • Huiyu Liao

    (School of Logistics, Central South University of Forestry and Technology, Changsha 410004, China)

  • Guijun Zheng

    (Business School, Central South University of Forestry and Technology, Changsha 410004, China)

  • Qian Huang

    (School of Logistics, Central South University of Forestry and Technology, Changsha 410004, China)

  • Maozhuo Shan

    (School of Logistics, Central South University of Forestry and Technology, Changsha 410004, China)

Abstract

With the improvement of people’s living standards and the widening of circulation channels, the demand for fresh agricultural products continues to increase. The increase in demand will lead to an increase in delivery vehicles, costs, and carbon emissions, among which the increase in carbon emissions will aggravate pollution and is not conducive to sustainable development. Therefore, it is very important to balance economic and environmental benefits in the distribution of fresh agricultural products. Based on the analysis of the distribution characteristics of fresh agricultural products, this paper studies the optimization of the cold chain distribution route of fresh agricultural products considering carbon emission. Firstly, the cold chain distribution route planning of fresh agricultural products was investigated and analyzed by the interview method, and the basis for establishing the model objective and constraint conditions was obtained. Then, taking the minimum total cost including carbon emission cost as the optimization goal, the cold chain distribution route optimization model for mixed vehicle types is established considering electric refrigerated vehicles, gasoline refrigerated vehicles, and so on. Genetic algorithm was used to solve the model, and MATLAB2018b was used to substitute specific case data for simulation analysis. The analysis results show that increasing the consideration of carbon emission and mixed vehicle types in the distribution route of fresh agricultural products can not only reduce the distribution cost but also reduce the carbon emission. To some extent, the research content of this paper can provide a reference for enterprises in planning cold chain distribution routes of fresh agricultural products.

Suggested Citation

  • Shuangli Pan & Huiyu Liao & Guijun Zheng & Qian Huang & Maozhuo Shan, 2024. "Cold Chain Distribution Route Optimization for Mixed Vehicle Types of Fresh Agricultural Products Considering Carbon Emissions: A Study Based on a Survey in China," Sustainability, MDPI, vol. 16(18), pages 1-22, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:18:p:8207-:d:1482191
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/18/8207/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/18/8207/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Leung, Stephen C.H. & Zhang, Zhenzhen & Zhang, Defu & Hua, Xian & Lim, Ming K., 2013. "A meta-heuristic algorithm for heterogeneous fleet vehicle routing problems with two-dimensional loading constraints," European Journal of Operational Research, Elsevier, vol. 225(2), pages 199-210.
    2. Yin-Yann Chen & Tzu-Li Chen & Chun-Chih Chiu & Yi-Jia Wu, 2023. "A multi-trip vehicle routing problem considering time windows and limited duration under a heterogeneous fleet and parking constraints in cold supply chain logistics," Transportation Planning and Technology, Taylor & Francis Journals, vol. 46(3), pages 335-358, April.
    3. Demir, Emrah & Bektaş, Tolga & Laporte, Gilbert, 2014. "The bi-objective Pollution-Routing Problem," European Journal of Operational Research, Elsevier, vol. 232(3), pages 464-478.
    4. Daqing Wu & Jiyu Li & Jiye Cui & Dong Hu, 2023. "Research on the Time-Dependent Vehicle Routing Problem for Fresh Agricultural Products Based on Customer Value," Agriculture, MDPI, vol. 13(3), pages 1-23, March.
    5. Qi Yao & Shenjun Zhu & Yanhui Li, 2022. "Green Vehicle-Routing Problem of Fresh Agricultural Products Considering Carbon Emission," IJERPH, MDPI, vol. 19(14), pages 1-17, July.
    6. Marius M. Solomon & Jacques Desrosiers, 1988. "Survey Paper---Time Window Constrained Routing and Scheduling Problems," Transportation Science, INFORMS, vol. 22(1), pages 1-13, February.
    7. Macrina, Giusy & Laporte, Gilbert & Guerriero, Francesca & Di Puglia Pugliese, Luigi, 2019. "An energy-efficient green-vehicle routing problem with mixed vehicle fleet, partial battery recharging and time windows," European Journal of Operational Research, Elsevier, vol. 276(3), pages 971-982.
    8. Ozgur Kabadurmus & Mehmet S. Erdogan, 2023. "A green vehicle routing problem with multi-depot, multi-tour, heterogeneous fleet and split deliveries: a mathematical model and heuristic approach," Journal of Combinatorial Optimization, Springer, vol. 45(3), pages 1-29, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Raeesi, Ramin & Zografos, Konstantinos G., 2020. "The electric vehicle routing problem with time windows and synchronised mobile battery swapping," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 101-129.
    2. Asghari, Mohammad & Mirzapour Al-e-hashem, S. Mohammad J., 2021. "Green vehicle routing problem: A state-of-the-art review," International Journal of Production Economics, Elsevier, vol. 231(C).
    3. Brunner, Carlos & Giesen, Ricardo & Klapp, Mathias A. & Flórez-Calderón, Luz, 2021. "Vehicle routing problem with steep roads," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 1-17.
    4. Liu, Yiming & Roberto, Baldacci & Zhou, Jianwen & Yu, Yang & Zhang, Yu & Sun, Wei, 2023. "Efficient feasibility checks and an adaptive large neighborhood search algorithm for the time-dependent green vehicle routing problem with time windows," European Journal of Operational Research, Elsevier, vol. 310(1), pages 133-155.
    5. Qiu, Rui & Xu, Jiuping & Ke, Ruimin & Zeng, Ziqiang & Wang, Yinhai, 2020. "Carbon pricing initiatives-based bi-level pollution routing problem," European Journal of Operational Research, Elsevier, vol. 286(1), pages 203-217.
    6. Mohammad Asghari & Seyed Mohammad Javad Mirzapour Al-E-Hashem, 2021. "Green vehicle routing problem: A state-of-the-art review," Post-Print hal-03182944, HAL.
    7. Elisabeth Lübbecke & Marco E. Lübbecke & Rolf H. Möhring, 2019. "Ship Traffic Optimization for the Kiel Canal," Operations Research, INFORMS, vol. 67(3), pages 791-812, May.
    8. Stephany Isabel Vallarta-Serrano & Ana Bricia Galindo-Muro & Riccardo Cespi & Rogelio Bustamante-Bello, 2023. "Analysis of GHG Emission from Cargo Vehicles in Megacities: The Case of the Metropolitan Zone of the Valley of Mexico," Energies, MDPI, vol. 16(13), pages 1-19, June.
    9. Maiyar, Lohithaksha M & Thakkar, Jitesh J, 2019. "Environmentally conscious logistics planning for food grain industry considering wastages employing multi objective hybrid particle swarm optimization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 220-248.
    10. Wen Li & Chenying Liu & Qizhi Yang & Yulan You & Zhihang Zhuo & Xiaolin Zuo, 2023. "Factors Influencing Farmers’ Vertical Collaboration in the Agri-Chain Guided by Leading Enterprises: A Study of the Table Grape Industry in China," Agriculture, MDPI, vol. 13(10), pages 1-14, September.
    11. Coelho, V.N. & Grasas, A. & Ramalhinho, H. & Coelho, I.M. & Souza, M.J.F. & Cruz, R.C., 2016. "An ILS-based algorithm to solve a large-scale real heterogeneous fleet VRP with multi-trips and docking constraints," European Journal of Operational Research, Elsevier, vol. 250(2), pages 367-376.
    12. Ehmke, Jan Fabian & Campbell, Ann M. & Thomas, Barrett W., 2018. "Optimizing for total costs in vehicle routing in urban areas," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 242-265.
    13. Yagcitekin, Bunyamin & Uzunoglu, Mehmet, 2016. "A double-layer smart charging strategy of electric vehicles taking routing and charge scheduling into account," Applied Energy, Elsevier, vol. 167(C), pages 407-419.
    14. Bixuan Sun & Jeffrey Apland, 2019. "Operational planning of public transit with economic and environmental goals: application to the Minneapolis–St. Paul bus system," Public Transport, Springer, vol. 11(2), pages 237-267, August.
    15. Tricoire, Fabien & Parragh, Sophie N., 2017. "Investing in logistics facilities today to reduce routing emissions tomorrow," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 56-67.
    16. Baals, Julian & Emde, Simon & Turkensteen, Marcel, 2023. "Minimizing earliness-tardiness costs in supplier networks—A just-in-time truck routing problem," European Journal of Operational Research, Elsevier, vol. 306(2), pages 707-741.
    17. Oscar Dominguez & Angel A. Juan & Barry Barrios & Javier Faulin & Alba Agustin, 2016. "Using biased randomization for solving the two-dimensional loading vehicle routing problem with heterogeneous fleet," Annals of Operations Research, Springer, vol. 236(2), pages 383-404, January.
    18. Suzuki, Yoshinori, 2016. "A dual-objective metaheuristic approach to solve practical pollution routing problem," International Journal of Production Economics, Elsevier, vol. 176(C), pages 143-153.
    19. Majid Eskandarpour & Pierre Dejax & Olivier Péton, 2019. "Multi-Directional Local Search for Sustainable Supply Chain Network Design," Post-Print hal-02407741, HAL.
    20. Jonathan F. Bard & George Kontoravdis & Gang Yu, 2002. "A Branch-and-Cut Procedure for the Vehicle Routing Problem with Time Windows," Transportation Science, INFORMS, vol. 36(2), pages 250-269, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:18:p:8207-:d:1482191. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.