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Truck Scheduling for Cross-Docking of Fresh Produce with Repeated Loading

Author

Listed:
  • Fei Pan
  • Tijun Fan
  • Xinyi Qi
  • Jingyi Chen
  • Chong Zhang

Abstract

Due to mismanagement of supply chain operations, fresh produce, which deteriorates highly depending on time and operating environment (including temperature and humidity), will suffer huge losses in transit, resulting in substantial monetary losses. Cross-docking, as an efficient logistics operation strategy, has been widely used in fresh produce distribution in the cold supply chain, whereas it has not received adequate attention in the scientific literature. In order to improve the efficiency of fresh produce distribution, this study formulates a novel mixed-integer mathematical formulation model that allows repeated loading of outbound trucks to minimize the total deterioration (TD) of all the fresh produce in the cross-docking center. To solve this problem, an advanced genetic algorithm is proposed based on a constructional mixed chromosome with two parts and three levels. The numerical analyses are conducted on 10 typical instances under different combinations of parameters. Results show that our proposed model based on the repeated loading mode can effectively decrease the total deterioration compared with the traditional nonrepeated loading mode. And this superiority becomes more significant, as the value of truck changeover time and lot loading quantity (called lot size in the text) decrease. In particular, when the truck changeover time equals 0, the total deterioration obtained under repeated loading mode will be more than 31.8% on average smaller than that under nonrepeated loading mode.

Suggested Citation

  • Fei Pan & Tijun Fan & Xinyi Qi & Jingyi Chen & Chong Zhang, 2021. "Truck Scheduling for Cross-Docking of Fresh Produce with Repeated Loading," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-16, June.
  • Handle: RePEc:hin:jnlmpe:5592122
    DOI: 10.1155/2021/5592122
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    Cited by:

    1. Mohammad Amin Amani & Mohammad Mahdi Nasiri, 2023. "A novel cross docking system for distributing the perishable products considering preemption: a machine learning approach," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-32, July.
    2. Feng Yang & Zhong Wu & Xiaoyan Teng & Shaojian Qu, 2022. "Robust Counterpart Models for Fresh Agricultural Product Routing Planning Considering Carbon Emissions and Uncertainty," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
    3. Suprava Chakraborty & Devaraj Elangovan & Padma Lakshmi Govindarajan & Mohamed F. ELnaggar & Mohammed M. Alrashed & Salah Kamel, 2022. "A Comprehensive Review of Path Planning for Agricultural Ground Robots," Sustainability, MDPI, vol. 14(15), pages 1-19, July.

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