不确定条件下带有时间窗口的多目标车辆路线问题

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI:10.1117/12.3014402

jiashuo guo, Yuxin Liu

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引用次数: 0

摘要

本文研究了不确定条件下带时间窗的多目标车辆路由问题。为了高效地解决该问题，本文提出了结合模拟退火算法的鲁棒多目标粒子群优化算法。新算法旨在提高粒子的局部搜索能力。实验结果表明，随着不确定干扰强度的增加，所提出的算法在所选问题集上优于传统的鲁棒多目标粒子群优化算法。

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Multi-objective vehicle routing problem with time windows under uncertain conditions

In this paper, we research the multi-objective vehicle routing problem with time windows under uncertainty. For solving it efficiently, the robust multi-objective particle swarm optimization incorporates the simulated annealing algorithm is proposed. The new algorithm aims to improve the local search abilities of particles. Experimental results show that the proposed algorithm outperforms the traditional the robust multi-objective particle swarm optimization algorithm on the selected problem sets as the uncertain interference intensity increases.

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International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)

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