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Weather Routing Optimization: A New Shortest Path Algorithm

机译：天气路由优化：一种新的最短路径算法

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摘要

This paper presents an algorithm which solves the multiobjective shortest path problem in a time-dependent graph, taking advantage of the specificities of the weather routing problem. Multicriteria shortest path problems are widely studied in the literature, as well as monocriteria shortest path problems in time-dependent graphs. Their solving has numerous applications, especially in the transportation field. However, the combination of both these issues is not studied as much as each one separately. In this paper, we study the weather routing problem for cargo ships, which involves optimizing the ship routes following realtime weather information. For this problem, the arc weights on the graph have a low dispersion around their average value. We propose an extension of an algorithm (NAMOA*) taking advantage of this property. We study the validity of this new algorithm and explain why it solves efficiently the weather routing problem. Experiments done using real weather data corroborates the algorithm efficiency.
机译：本文提出了一种算法，该算法利用天气路由问题的特殊性来解决时变图中的多目标最短路径问题。文献中广泛研究了多准则最短路径问题，以及时间依赖图中的单准则最短路径问题。他们的解决方案具有众多应用，特别是在运输领域。但是，对这两个问题的组合的研究不如分别对每个问题进行过多的研究。在本文中，我们研究货船的天气路线问题，该问题涉及根据实时天气信息优化船路线。对于此问题，曲线图上的电弧权重在其平均值附近具有较低的离散度。我们建议利用此属性扩展算法（NAMOA *）。我们研究了这种新算法的有效性，并解释了为什么它可以有效解决天气路由问题。使用实际天气数据进行的实验证实了算法的效率。

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