交通运输系统工程与信息

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (5): 162-172.DOI: 10.16097/j.cnki.1009-6744.2026.05.014

• 综合交通运输体系 • 上一篇    下一篇

考虑交通拥堵规避策略的区域时变冷链配送路径优化

张小花1a，2 ，钟婷婷1a ，曾涛*1a，2 ，黄兆轩1b ，张文峰1a，2 ，吴卓葵1a，2 ，张权3   

  1. 1. 仲恺农业工程学院，a.自动化学院，b.机电工程学院，广州510550；2.广东省智能海洋牧场与装备重点实验室， 广州510550；3. 广东新供销天业冷链集团有限公司，广州510000
  • 收稿日期:2026-05-02 修回日期:2026-07-14 接受日期:2026-07-27 出版日期:2026-10-25 发布日期:2026-10-21
  • 作者简介:张小花（1979— ），女，江西丰城人，教授。
  • 基金资助:
    国家自然科学基金(32472015)；广东省普通高校重点领域专项(2023ZDZX4018)。

Optimization for Regional Time-Varying Cold Chain Distribution Path Considering Traffic Congestion Avoidance Strategy

ZHANG Xiaohua1a，2, ZHONG Tingting1a, ZENG Tao*1a，2, HUANG Zhaoxuan1b, ZHANG Wenfeng1a，2, WU Zhuokui1a，2, ZHANG Quan3   

  1. 1a. College of Automation, 1b. College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510550, China; 2. Guangdong Province Key Laboratory of Intelligent Ocean Ranching and Equipment Technology, Guangzhou 510550, China; 3. Guangdong New Supply and Marketing Tianye Cold Chain Group Co Ltd, Guangzhou 510000, China
  • Received:2026-05-02 Revised:2026-07-14 Accepted:2026-07-27 Online:2026-10-25 Published:2026-10-21
  • Supported by:
    National Natural Science Foundation of China (32472015)； Guangdong Provincial Key-Area Special Project for Ordinary Institutions of Higher Education (2023ZDZX4018)。

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摘要： 针对交通流高峰期导致的交通拥堵影响冷链配送效率的问题，本文研究带时间窗的区域时变冷链车辆路径规划问题，并针对该问题提出一种两阶段协同决策的交通拥堵规避策略。综合考虑车辆固定成本、运输成本、货损成本、制冷成本、碳排放成本和时间窗的惩罚成本等因素，构建以总成本最小为目标的冷链车辆路径优化模型。最后，设计带交通拥堵规避的改进遗传算法求解多类型算例。实验结果表明，所提模型计算复杂程度相对较低，提升了算法的收敛速度，能高效、迅速地应对不同规模大小和不同分布类型的客户分布；在小规模问题上，带交通拥堵规避策略的改进遗传算法（improvedAdaptive Genetic Algorithm integrated with Traffic Congestion Avoidance, AGATCA）相较于CPLEX求解器能在较快收敛速度前提下得出较优质量的解；在总成本和碳排放量上，本文模型相较于不带拥堵规避策略的改进遗传算法（AGA）下降8.4%和2.3%，相较于传统遗传算法（GA）下降15.7%和19.9%，相较于头脑风暴优化算法（BSO）下降15.0%和4.9%。本文模型能快速为决策者生成有效规避交通拥堵，减少车辆碳排放，降低总配送成本的配送路线，促进冷链物流配送与生态环境和谐发展。

关键词: 公路运输, 路径优化, 改进遗传算法, 冷链配送, 交通拥堵, 时变路网

Abstract: Aiming at the problem that traffic congestion during peak traffic flow periods impairs the efficiency of cold chain distribution, this paper investigates the regional routing problem of time-varying cold chain vehicles with time windows and proposes a two-stage collaborative decision-making strategy for traffic congestion avoidance tailored to this issue. Synthesizing the considerations of vehicle fixed costs, transportation costs, cargo damage costs, refrigeration costs, carbon emission costs, and time window penalty costs, a cold chain vehicle routing optimization model is constructed with the objective of total cost minimization. Subsequently, an improved Adaptive Genetic Algorithm integrated with Traffic Congestion Avoidance (AGATCA) mechanisms is designed to solve multi-type test instances. Experimental results demonstrate that the proposed model exhibits relatively low computational complexity, and improves the convergence speed of the algorithm. It can enable efficient and rapid adaptation to customer distributions of varying scales and types. For small-scale problems, the AGATCA algorithm can achieve higher-quality solutions with faster convergence compared to the CPLEX solver. In terms of total cost and carbon emissions, the model achieves reductions of 8.4% and 2.3% compared to the Adaptive Genetic Algorithm (AGA); it reduces by 15.7% and 19.9% compared to the traditional Genetic Algorithm (GA); and decreases by 15.0% and 4.9% compared to the Brain Storm Optimization algorithm (BSO). This model can quickly generate distribution routes for decision-makers to effectively avoid traffic congestion, reduce vehicle carbon emissions, and lower total distribution costs, thereby promoting the harmonious development of cold chain logistics distribution and the ecological environment.

Key words: highway transportation, route optimization, improved genetic algorithm, cold chain distribution, traffic congestion, time-varying road network

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