摘要
文章基于客户对生鲜易腐品时效和品质的双重要求,开展了考虑客户满意度的易腐品配送路径优化研究。首先,将客户对配送时间和产品质量的满意程度作为约束条件,构建了时变车速环境下的路径优化模型。该模型综合考虑了运输成本、制冷成本、碳排放成本、货损成本和时间窗成本。其次,提出了一种改进蚁群算法(IAACO)。该算法利用遗传算法生成初始信息素分布,并采用自适应蚁群算法进行优化搜索,在状态转移规则中引入自适应机制提高收敛精度。最后,基于实际案例和Solomon标准数据集进行仿真实验,对比分析了ACO、AACO和IAACO三种算法的性能。实验结果表明,所提出的改进算法具有较好的有效性。文章为冷链物流等时效性要求较高的配送场景提供了可行的决策支持。
This study addresses the dual requirements of timeliness and quality for perishable fresh products from customers by conducting research on perishable product delivery route optimization that considers customer satisfaction.First,the model incorporates customer satisfaction levels regarding delivery time and product quality as constraints,establishing a route optimization model under time-varying vehicle speeds.This model comprehensively considers transportation costs,refrigeration costs,carbon emission costs,product spoilage costs,and time window penalty costs.Second,an Improved Adaptive Ant Colony Optimization(IAACO)algorithm is proposed.The algorithm utilizes a genetic algorithm to generate the initial pheromone distribution and employs an adaptive ant colony optimization approach for iterative search.An adaptive mechanism is introduced into the state transition rule to enhance convergence accuracy.Finally,simulation experiments based on real-world cases and the Solomon benchmark dataset are conducted,comparing the performance of three algorithms:ACO,AACO,and IAACO.The experimental results demonstrate that the proposed improved algorithm exhibits superior effectiveness.This research provides feasible decision-making support for time-sensitive delivery scenarios such as cold chain logistics.
作者
张文龙
陈一飞
ZHANG Wenlong;CHEN Yifei(School of Transportation Engineering,Dalian Jiaotong University,Dalian 116028,China)
出处
《物流科技》
2026年第7期15-20,共6页
Logistics Sci Tech
基金
辽宁省教育厅科学研究项目(LJ212410150048)。
关键词
冷链物流
客户满意度
时变路网
蚁群算法
cold chain logistics
customer satisfaction
time-varying road networks
ant colony algorithm
