基于改进 ALNS 算法的考虑动态需求的 冷链路径优化研究

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DOI：10.16652/j.issn.1004-373x.2026.10.023

中图分类号：TN911-34；TP310.6

文献标识码：A

文章编号：1004-373X（2026）10-0158-08

Research on improved ALNS algorithm based cold chain path optimization considering dynamic demand

Liu Jinlong 1，2 ， Ni Zhiwei 1，2 ， Liu Wentao 1，2 ， Qing Min 1，2

School of Management， Hefei University of Technology， Hefei 230009， China;

2. Key Laboratory of Process Optimization and Intelligent Decision-making， Hefei University of Technology， Hefei 230009， China）

Abstract： In allusion to the problem of cold chain path optimization under dynamic demands， a multi-period static summation model and improved adaptive large neighborhood search （IALNS） algorithm is proposed. In this algorithm， the operating costs of various cold chain transports are considered by combining with the dynamic time segmentation， soft time window mechanism and rolling optimization strategy. The IALNS algorithm can improve the solution efficiency and global search capability by combining with the dynamic insertion mechanism and simulated annealing strategy. The experimental results show that the performance of IALNS algorithm is better than the traditional solver CPLEX in small-scale cases. In large-scale cases， in comparison with ALNS， the distribution cost of IALNS algorithm is reduced by an average of 4.21%， with a maximum reduction of 16.7%. In comparison with HHO， HOA， GA， SA and other algorithms， IALNS has obvious advantages in convergence speed and solution quality. This research can provide theoretical support and practical reference for the dynamic cold chain path optimization problem， which is of great significance for the cold chain transportation enterprises to realize the green and sustainable development.

Keywords： adaptive large neighborhood search; cold chain logistic; path optimization; dynamic demand; soft time window; rolling optimization; simulated annealing

0 引言

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