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<p>As consumer demand for high-quality geographically indicated fresh agricultural products(GIFPs) increases, ensuring timely, high-quality, and low-carbon long-distance cold chain delivery has become a central goal in GIFP supply chains. This study focuses on optimizing the operational strategy of cross-docking cold supply chain networks for GIFPs, aiming to balance product quality, carbon emissions, and total transportation costs. Given the randomness of goods arriving at transshipment centers and the state of perishable goods, a flexible random batch delivery strategy based on product quality is proposed, and a corresponding mixed integer model is constructed. This model is solved using simulated annealing algorithm with large neighborhood search under genetic algorithm framework. Taking the Guangxi logistics network as case study, the feasibility of the model and algorithm is verified. Results indicate that the proposed state-aware, flexible batch delivery strategy significantly improves delivery quality under high time sensitivity. Although this approach requires more frequent vehicle scheduling, it reduces deterioration-related loss costs by up to 66.56% compared to conventional uniform delivery strategies. Furthermore, under a scenario where the product shelf life is shortened by 40%, the total operational cost decreases substantially. While extending the freshness period helps preserve product quality, it markedly increases operating costs; conversely, shortening the period enhances cost efficiency and reduces resource consumption. Overall, dynamic and adaptive logistics planning proves effective in balancing freshness preservation with economic performance, contributing to the development of a sustainable and high-performance cold chain network for perishable GIFPs.</p>

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Optimization of cross-docking for perishable goods considering freshness dynamics

  • 期刊论文

  • 发表日期：

    2025年12月8日

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  • Xinyu Wang,
  • Xinquan Liu,
  • Yingyi Huang

摘要

As consumer demand for high-quality geographically indicated fresh agricultural products(GIFPs) increases, ensuring timely, high-quality, and low-carbon long-distance cold chain delivery has become a central goal in GIFP supply chains. This study focuses on optimizing the operational strategy of cross-docking cold supply chain networks for GIFPs, aiming to balance product quality, carbon emissions, and total transportation costs. Given the randomness of goods arriving at transshipment centers and the state of perishable goods, a flexible random batch delivery strategy based on product quality is proposed, and a corresponding mixed integer model is constructed. This model is solved using simulated annealing algorithm with large neighborhood search under genetic algorithm framework. Taking the Guangxi logistics network as case study, the feasibility of the model and algorithm is verified. Results indicate that the proposed state-aware, flexible batch delivery strategy significantly improves delivery quality under high time sensitivity. Although this approach requires more frequent vehicle scheduling, it reduces deterioration-related loss costs by up to 66.56% compared to conventional uniform delivery strategies. Furthermore, under a scenario where the product shelf life is shortened by 40%, the total operational cost decreases substantially. While extending the freshness period helps preserve product quality, it markedly increases operating costs; conversely, shortening the period enhances cost efficiency and reduces resource consumption. Overall, dynamic and adaptive logistics planning proves effective in balancing freshness preservation with economic performance, contributing to the development of a sustainable and high-performance cold chain network for perishable GIFPs.