基于改进蚁群算法的冷链物流多目标路径优化研究

Multi-Objective Route Optimization for Cold Chain Logistics Based on an Improved Ant Colony Algorithm

摘要: 在冷链物流配送过程中，如何在多目标约束下实现高效、低成本的路径优化，以降低运输成本、控制货损并满足时效要求，是当前企业面临的关键问题。本文针对多车辆冷链配送系统构建了一个多目标、多约束的优化模型，全面考虑运输成本、时间窗约束、货物损耗、制冷成本等因素，特别引入多温区需求建模与蓄冷箱配置限制，使模型更贴近实际业务需求。为克服传统蚁群算法在求解强约束多目标问题中易陷入局部最优、收敛速度慢等问题，本文提出了一种状态感知蚁群算法(State-Aware Ant Colony Algorithm, SA-ACA)。该算法融合车辆状态建模(位置、时间、载重、货损)、多温区路径动态评估机制、局部搜索(2-opt)及基于精英路径的Top-K信息素增强策略，从而显著提升了解的质量与算法效率。通过在典型冷链配送网络上的仿真实验，结果表明SA-ACA在运输成本、时间窗满足率、货损控制与制冷成本等关键指标上均优于标准蚁群算法，验证了该方法在冷链物流路径优化中的有效性与实际应用潜力。

Abstract: In the process of cold chain logistics distribution, achieving efficient and low-cost route optimization under multi-objective constraints—such as reducing transportation costs, minimizing time-related losses, and controlling cargo damage—remains a critical challenge. To address the complexity of multi-vehicle cold chain systems in real-world applications, this paper establishes a comprehensive optimization model that simultaneously considers transportation cost, service time windows constraint, vehicle loading capacity, cargo spoilage, and refrigeration cost. Notably, the model incorporates multi-temperature zone demand constraints and cold box configuration limits to better align with actual business scenarios. To overcome the limitations of the traditional Ant Colony Algorithm (ACA) in handling strongly constrained multi-objective problems—such as susceptibility to local optima and slow convergence—a State-Aware Ant Colony Algorithm (SA-ACA) is proposed. The algorithm integrates a vehicle state modeling mechanism (including position, time, residual load, and spoilage), multi-temperature path evaluation, local 2-opt search, and a Top-K elite-based pheromone reinforcement strategy, thereby significantly improving both the solution quality and convergence efficiency. Numerical experiments on a representative cold chain distribution network demonstrate that the SA-ACA outperforms the standard ACA in terms of transportation cost, time window satisfaction rate, cargo preservation, and refrigeration expense, confirming its effectiveness and practical value in solving real-world cold chain logistics routing problems.