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Research on the Time-Dependent Vehicle Routing Problem for Fresh Agricultural Products Based on Customer Value

Author

Listed:
  • Daqing Wu

    (College of Economics and Management, Shanghai Ocean University, Shanghai 201306, China
    Department of Nanchang Technology, Economic and Technological Development Zone, 901 Yingxiong Dadao, Nanchang 330044, China)

  • Jiyu Li

    (College of Economics and Management, Shanghai Ocean University, Shanghai 201306, China)

  • Jiye Cui

    (College of Economics and Management, Shanghai Ocean University, Shanghai 201306, China)

  • Dong Hu

    (College of Continuing Education, Shanghai Jianqiao University, Shanghai 202151, China)

Abstract

With continuous improvements in people’s consumption levels, consumers’ demands for safe and fresh agricultural products increase. The increase in the number of vehicles and serious congestion on roads has led to problems, such as the weak timeliness of urban cold chain logistics, high carbon emissions, low customer value and reduced customer satisfaction. In this study, carbon emissions, customer satisfaction, customer value and cost are considered, and an optimization algorithm is established to solve the time-dependent vehicle routing problem in urban cold chain logistics. For road congestion at different time periods during the cold chain distribution process, the segment function is used to express the vehicle speed. According to the characteristics of the model, considering the constraints of the time window and vehicle capacity, an improved NSGA-II algorithm with the local optimization characteristics of the greedy algorithm (G-NSGA-II) is proposed, and the sorting fitness strategy is optimized. In addition, we carry out a series of experiments on existing vehicle routing problem examples and analyze them in a real background to evaluate and prove the effectiveness of the proposed model and algorithm. The experiment results show that the proposed approach effectively reduces the total cost, enhances customer value and promotes the long-term development of logistics companies.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:3:p:681-:d:1097346
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    References listed on IDEAS

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    1. Chungmok Lee, 2021. "An exact algorithm for the electric-vehicle routing problem with nonlinear charging time," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 72(7), pages 1461-1485, July.
    2. Yu, Yang & Wang, Sihan & Wang, Junwei & Huang, Min, 2019. "A branch-and-price algorithm for the heterogeneous fleet green vehicle routing problem with time windows," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 511-527.
    3. Ichoua, Soumia & Gendreau, Michel & Potvin, Jean-Yves, 2003. "Vehicle dispatching with time-dependent travel times," European Journal of Operational Research, Elsevier, vol. 144(2), pages 379-396, January.
    4. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    5. Yang, Senyan & Ning, Lianju & Shang, Pan & (Carol) Tong, Lu, 2020. "Augmented Lagrangian relaxation approach for logistics vehicle routing problem with mixed backhauls and time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    6. Chryssi Malandraki & Mark S. Daskin, 1992. "Time Dependent Vehicle Routing Problems: Formulations, Properties and Heuristic Algorithms," Transportation Science, INFORMS, vol. 26(3), pages 185-200, August.
    7. Emrah Demir & Martin Hrušovský & Werner Jammernegg & Tom Van Woensel, 2019. "Green intermodal freight transportation: bi-objective modelling and analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 57(19), pages 6162-6180, October.
    8. Ezzatollah Asgharizadeh & Sobhan Jooybar & Hannan Amoozad Mahdiraji & Jose Arturo Garza-Reyes, 2022. "A Novel Travel Time Estimation Model for Modeling a Green Time-Dependent Vehicle Routing Problem in Food Supply Chain," Sustainability, MDPI, vol. 14(14), pages 1-16, July.
    9. Ziqi Liu & Yeping Chen & Jian Li & Dongqing Zhang & Dragan PamuÄ ar, 2021. "Spatiotemporal-Dependent Vehicle Routing Problem Considering Carbon Emissions," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-21, September.
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    2. Vadim Bolshev & Vladimir Panchenko & Alexey Sibirev, 2023. "Engineering Innovations in Agriculture," Agriculture, MDPI, vol. 13(7), pages 1-4, June.

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