Key Factors and Model Analysis of Multi-Vehicle Distribution Route Optimization

在生鲜产品高频履约和冷链物流高质量发展的背景下，如何在满足时效性与温控要求的前提下实现多车型协同配送路径优化，成为冷链企业亟待解决的核心问题。本文聚焦于多车型配送路径优化中的关键变量与调度逻辑，构建了一个兼顾运输成本、配送时效和货损控制的多目标优化模型。模型综合考虑不同车型在运载能力、能耗水平与温控能力方面的差异特征，引入时间–温度耦合的货损函数与分层时间窗惩罚机制，以刻画生鲜订单的动态履约约束。为提高模型求解效率与解集质量，设计了改进型NSGA-II算法，并在实验中结合仿真订单数据进行了路径规划优化。结果显示，相较于传统方案，优化后可实现22.4%的成本下降、66.1%的配送时延缩短及58.1%的货损率降低。研究表明，该模型在多目标调度与智能分配方面具备良好的实用性与推广价值，可为生鲜物流企业制定高效、可持续的配送策略提供理论支持与技术方案。Under the background of high-frequency fulfillment of fresh products and the high-quality development of cold chain logistics, optimizing multi-vehicle collaborative distribution routes while meeting timeliness and temperature-control requirements has become a critical challenge for cold chain enterprises. This study focuses on key variables and scheduling logic in multi-vehicle distribution route optimization, constructing a multi-objective optimization model that balances transportation costs, delivery timeliness, and spoilage control. The model comprehensively integrates the heterogeneous characteristics of vehicles, including load capacity, energy consumption levels, and temperature-control capabilities, and introduces a time–temperature coupled spoilage function and a hierarchical time window penalty mechanism to characterize the dynamic fulfillment constraints of fresh product orders. To enhance solution efficiency and solution set quality, an improved NSGA-II algorithm is designed and applied to path planning optimization using simulated order data. Experimental results show that, compared to traditional solutions, the optimized approach achieves a 22.4% reduction in costs, a 66.1% decrease in delivery delays, and a 58.1% reduction in spoilage rates. The research demonstrates that the proposed model exhibits strong practicality and promotion value in multi-objective scheduling and intelligent allocation, providing theoretical support and technical solutions for fresh product logistics enterprises to formulate efficient and sustainable distribution strategies.