基于自适应大规模邻居搜索算法的接收点投递路由问题研究

Haoyang Li
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摘要

随着近年来自然灾害的频繁发生,应急物流运输具有重要意义。灾后车辆配送路线的优化是一个受能力、时间窗口和成本限制的问题。构建了一个考虑时间窗惩罚成本的多目标模型,以使交付过程中总成本最小化,使灾难现场的平均满意度最大化。然后,我们将提出的多目标模型转化为单目标模型。最后,提出了一种自适应大邻域搜索(ALNS)算法来解决这一问题,实验结果表明,该算法在大规模实例上获得了高质量的解。
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Study of Receptive Spot Delivery Routing Problem based on Adaptive Large-scale Neighbor Search Algorithm
With the frequent occurrence of natural disasters in recent years, emergency logistics and transportation have great significance. The optimization of the vehicle delivery route after disasters is a problem limited by capacity, time window, and cost. We construct a multi-objective model that considers the time window penalty cost to minimize the total costs and maximize the average satisfaction of the disaster sites during the delivery process. Then, we transfer the proposed multi-objective model to a single objective model. Last, an adaptive large neighborhood search (ALNS) algorithm is presented to solve this problem and the experimental results show that the algorithm achieves a high-quality solution on large-scale instances.
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