Airline crew pairing optimization problem (CPOP) aims to find a set of flight sequences (crew pairings) that cover all flights in an airline's highly constrained flight schedule at minimum cost. Since crew cost is second only to the fuel cost, CPOP solutioning is critically important for an airline. However, CPOP is NP-hard, and tackling it is quite challenging. The literature suggests, that when the CPOP's scale and complexity is reasonably limited, and an enumeration of all crew pairings is possible, then Metaheuristics are used, predominantly Genetic Algorithms (GAs). Else, Column Generation (CG) based Mixed Integer Programming techniques are used. Notably, as per the literature, a maximum of 45,000 crew pairings have been tackled by GAs. In a significant departure, this paper considers over 800 flights of a US-based large airline (with a monthly network of over 33,000 flights), and tests the efficacy of GAs by enumerating all 400,000+ crew pairings, apriori. Towards it, this paper proposes a domain-knowledge-driven customized-GA. The utility of incorporating domain-knowledge in GA operations, particularly initialization and crossover, is highlighted through suitable experiments. Finally, the proposed GA's performance is compared with a CG-based approach (developed in-house by the authors). Though the latter is found to perform better in terms of solution's cost-quality and run time, it is hoped that this paper will help in better understanding the strengths and limitations of domain-knowledge-driven customizations in GAs, for solving combinatorial optimization problems, including CPOPs.

翻译：航空公司机组配对优化问题旨在以最低成本覆盖航空公司高度受限航班计划中的所有航班，从而生成一组飞行序列（机组配对）。由于机组成本仅次于燃油成本，机组配对优化问题的求解对航空公司至关重要。然而，该问题属于NP-hard问题，求解极具挑战性。文献表明，当机组配对优化问题的规模和复杂度合理可控，且所有机组配对可枚举时，通常采用元启发式算法，尤其是遗传算法。否则，采用基于列生成的混合整数规划技术。值得注意的是，根据文献，遗传算法最多可处理45,000个机组配对。与以往研究不同，本文以美国一家大型航空公司（月度网络包含超过33,000个航班）的800余个航班为研究对象，通过预先枚举全部400,000余个机组配对，测试遗传算法的有效性。为此，本文提出一种基于领域知识的定制化遗传算法。通过适当实验，重点展示了在遗传算法操作（尤其是初始化和交叉）中融入领域知识的效用。最后，将所提遗传算法与作者内部开发的基于列生成的方法进行性能对比。尽管后者在解的质量和运行时间方面表现更优，但本文期望有助于更深入理解领域知识驱动的遗传算法定制化在求解包括机组配对优化问题在内的组合优化问题时的优势与局限。