大规模复杂航班网络机组配对优化初始化研究
On Initializing Airline Crew Pairing Optimization for Large-scale Complex Flight Networks
摘要：
机组配对优化（CPO）对任何航空公司都至关重要，因为其机组运营成本仅次于燃料成本。CPO的目的是生成一组飞行序列（机组配对），覆盖一个航班时刻表，在最小的成本，同时满足几个合法性约束。对于大规模的复杂飞行网络，可能有十亿多个合法配对（变量），这使得它们的离线枚举变得棘手，并且对它们的最小成本全飞行覆盖子集的穷举搜索变得不切实际。即使生成一个初始可行解（IFS：一组可管理的合法配对，覆盖所有航班），随后可以优化，这是一个困难的（NP完全）问题。虽然，作为一个更大的项目的一部分，作者已经开发了一个机组配对优化器（AirCROP），本文专注于IFS生成通过一种新的启发式的基础上划分和覆盖策略和可编程。对于GE Aviation提供的真实世界大型复杂的飞行网络数据集（包括超过3200个航班和15个机组人员基地），所提出的启发式算法比另一种最先进的方法的速度提高了10倍。前所未有的，本文提出了一个实证调查的IFS成本的最终（优化）解决方案的成本的影响，揭示了IFS成本太低，并不一定意味着更快的收敛AirCROP或甚至更低的成本优化的解决方案。
英文摘要：
Crew pairing optimization (CPO) is critically important for any airline, since its crew operating costs are second-largest, next to the fuel-cost. CPO aims at generating a set of flight sequences (crew pairings) covering a flight-schedule, at minimum-cost, while satisfying several legality constraints. For large-scale complex flight networks, billion-plus legal pairings (variables) are possible, rendering their offline enumeration intractable and an exhaustive search for their minimum-cost full flight-coverage subset impractical. Even generating an initial feasible solution (IFS: a manageable set of legal pairings covering all flights), which could be subsequently optimized is a difficult (NP-complete) problem. Though, as part of a larger project the authors have developed a crew pairing optimizer (AirCROP), this paper dedicatedly focuses on IFS-generation through a novel heuristic based on divide-and-cover strategy and Integer Programming. For real-world large and complex flight network datasets (including over 3200 flights and 15 crew bases) provided by GE Aviation, the proposed heuristic shows upto a ten-fold speed improvement over another state-of-the-art approach. Unprecedentedly, this paper presents an empirical investigation of the impact of IFS-cost on the final (optimized) solution-cost, revealing that too low an IFS-cost does not necessarily imply faster convergence for AirCROP or even lower cost for the optimized solution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
