Shi Huaixia, Hong Yu, Zhang Qinglei, Qin Jiyun
Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China.
Business School, Shanghai DianJi University, Shanghai 201306, China.
Entropy (Basel). 2025 May 20;27(5):540. doi: 10.3390/e27050540.
The sharing economy is an inevitable trend in cold chain logistics. Most cold chain logistics enterprises are small and operate independently, with limited collaboration. Joint distribution is key to integrating cold chain logistics and the sharing economy. It aims to share logistics resources, provide collective customer service, and optimize distribution routes. However, existing studies have overlooked uncertainty factors in joint distribution optimization. To address this, we propose the Cold Chain Logistics Joint Distribution Vehicle Routing Problem with Time-Varying Network (CCLJDVRP-TVN). This model integrates traffic congestion uncertainty and constructs a time-varying network to reflect real-world conditions. The solution combines simulated annealing strategies with genetic algorithms. It also uses the entropy mechanism to optimize uncertainties, improving global search performance. The method was applied to optimize vehicle routing for three cold chain logistics companies in Beijing. The results show a reduction in logistics costs by 18.3%, carbon emissions by 15.8%, and fleet size by 12.5%. It also effectively addresses the impact of congestion and uncertainty on distribution. This study offers valuable theoretical support for optimizing joint distribution and managing uncertainties in cold chain logistics.
共享经济是冷链物流的必然趋势。大多数冷链物流企业规模较小且独立运营，协作有限。共同配送是整合冷链物流与共享经济的关键。它旨在共享物流资源、提供集体客户服务并优化配送路线。然而，现有研究忽略了共同配送优化中的不确定性因素。为解决这一问题，我们提出了具有时变网络的冷链物流共同配送车辆路径问题（CCLJDVRP-TVN）。该模型整合了交通拥堵的不确定性，并构建了一个时变网络以反映现实情况。该解决方案将模拟退火策略与遗传算法相结合。它还使用熵机制来优化不确定性，提高全局搜索性能。该方法被应用于优化北京三家冷链物流企业的车辆路径。结果表明，物流成本降低了18.3%，碳排放量降低了15.8%，车队规模缩小了12.5%。它还有效解决了拥堵和不确定性对配送的影响。本研究为优化冷链物流中的共同配送和管理不确定性提供了有价值的理论支持。
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