Flight Radius Algorithms

A. Idrissi, Arnaud Malapert, R. Jolin
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Abstract

In this article, we present the flight radius problem (FRP) on the condensed flight network (CFN). Then, giving a specific flight that is defined by an origin and destination (OD) pair, the problem consists in finding routes that connect the OD pair and satisfy a regret constraint on time, distance or cost. The found routes help airline manager to find business opportunities. This problem arises in the real world, for instance in some air transportation companies. The FRP is formulated as finding a maximal subgraph of nodes belonging to routes satisfying a regret constraint. Such routes can be found using shortest paths algorithms (SPA). The CFN is generated using a time-independent approach and stored in the graph database Neo4j. Implementing SPA in Neo4j is challenging since the graph database stores the weights of the graph in a separate data structure. In this paper, we propose four methods to solve the FRP: these methods combine parallel and sequential processing with more optimization to overcome time and memory costs. The experimental evaluation demonstrates that the best algorithm is the extended Dijkstra algorithm which meets the real-time constraints of the targeted industrial application.
飞行半径算法
本文研究了压缩飞行网络(CFN)上的飞行半径问题(FRP)。然后,给定一个由出发地和目的地(OD)对定义的特定航班,问题在于找到连接OD对并满足时间、距离或成本上的遗憾约束的路线。发现的航线帮助航空公司经理发现商机。这个问题出现在现实世界中,例如在一些航空运输公司。FRP被表示为寻找属于满足遗憾约束的路由的节点的最大子图。这样的路径可以使用最短路径算法(SPA)找到。CFN使用与时间无关的方法生成,并存储在图形数据库Neo4j中。在Neo4j中实现SPA是具有挑战性的,因为图数据库将图的权重存储在单独的数据结构中。在本文中,我们提出了四种解决FRP的方法:这些方法结合了并行和顺序处理,并进行了更多的优化,以克服时间和内存成本。实验评价表明,满足目标工业应用实时性约束的扩展Dijkstra算法是最佳算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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