基于Optimization和大规模复杂飞行网络的机组配对列生成算法
A Novel Column Generation Heuristic for Airline Crew Pairing Optimization with Large-scale Complex Flight Networks
摘要：
机组配对优化（CPO）对于航空公司的商业可行性至关重要，因为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），以最小的成本，同时满足几个合法性约束，以涵盖所有定期航班。最新技术严重依赖于将底层的整数规划问题放松为线性规划问题，而线性规划问题又通过列生成（CG）技术来解决。然而，随着航空公司业务的惊人扩张，CPO受到维数灾难的影响，使得精确的CG实现过时，并需要基于几何的CG实现。然而，在文献中，涉及多个机组人员基地和/或轴辐子网络的非常普遍的大规模复杂飞行网络在很大程度上仍未被研究。本文提出了一种新的CG启发式，这使得内部开发的航空公司机组人员配对优化（AirCROP）。启发式/AirCROP的有效性已经在现实世界的大规模复杂网络实例中进行了测试，这些网络实例包含4，200多个航班，15个机组人员基地和多个中心辐射子网络（导致数十亿多个可能的配对）。值得注意的是，本文有一个专门的重点提出CG启发式（而不是整个AirCROP框架）的基础上平衡随机探索配对;利用领域知识（最优解功能）;和利用过去的计算和搜索工作通过归档。虽然本文有一个航空公司的背景下，建议CG启发式可能会发现更广泛的应用在不同的领域，作为一个模板，如何利用领域知识，以更好地解决组合最优化问题。
英文摘要：
Crew Pairing Optimization (CPO) is critical for an airlines' business viability, given that the crew operating cost is second only to the fuel cost. CPO aims at generating a set of flight sequences (crew pairings) to cover all scheduled flights, at minimum cost, while satisfying several legality constraints. The state-of-the-art heavily relies on relaxing the underlying Integer Programming Problem into a Linear Programming Problem, which in turn is solved through the Column Generation (CG) technique. However, with the alarmingly expanding airlines' operations, CPO is marred by the curse of dimensionality, rendering the exact CG-implementations obsolete, and necessitating the heuristic-based CG-implementations. Yet, in literature, the much prevalent large-scale complex flight networks involving multiple { crew bases and/or hub-and-spoke sub-networks, largely remain uninvestigated. This paper proposes a novel CG heuristic, which has enabled the in-house development of an Airline Crew Pairing Optimizer (AirCROP). The efficacy of the heuristic/AirCROP has been tested on real-world, large-scale, complex network instances with over 4,200 flights, 15 crew bases, and multiple hub-and-spoke sub-networks (resulting in billion-plus possible pairings). Notably, this paper has a dedicated focus on the proposed CG heuristic (not the entire AirCROP framework) based on balancing random exploration of pairings; exploitation of domain knowledge (on optimal solution features); and utilization of the past computational & search effort through archiving. Though this paper has an airline context, the proposed CG heuristic may find wider applications across different domains, by serving as a template on how to utilize domain knowledge to better tackle combinatorial optimization problems.
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。
