Flight Planning at Scale: A Bipartite Matching Based Approach

Tianlong Zhang, Chang Gao, Yuxiang Zeng, Shuyuan Li, Yi Xu, Yuanyuan Zhang

Abstract: Flight planning, a major challenge in airline industry, aims to efficiently and economically arrange multiple airplanes to serve all flight itineraries. This process also needs to consider various spatiotemporal factors such as the time and location for departures or arrivals. The increasing demand of air transportation brings new technical challenges to this problem, such as large data scalability and multiple optimization objectives. Thus, in this paper, we focus on large-scale flight planning with multi-objectives, namely minimizing the number of required airplanes and balancing their flight workload, which are primary considerations in airline companies. To tackle this problem, we propose a bipartite matching based framework to minimize the number of required airplanes and devise several optimizations to balance their workload and enhance scalability. Moreover, we provide theoretical guarantees on both optimization goals. Finally, we conduct extensive experiments on real-world datasets to demonstrate the effectiveness and scalability of our solution.