现实世界航空公司机组配对优化：定制遗传算法与列生成方法的对比
Real-World Airline Crew Pairing Optimization: Customized Genetic Algorithm versus Column Generation Method
AI总结：
本文针对大规模航空公司机组配对优化问题，提出领域知识驱动的定制遗传算法，在枚举超40万个机组配对的大规模场景下验证其有效性，并与列生成方法进行性能对比。
摘要：
航空公司机组配对优化问题（CPOP）旨在以最小成本找出一组航班序列（机组配对），以覆盖航空公司高度受限的航班计划中的所有航班。由于机组成本仅次于燃油成本，CPOP 的求解对航空公司至关重要。然而，CPOP 是 NP 难问题，处理起来相当具有挑战性。文献表明，当 CPOP 的规模和复杂度受到合理限制，并且能够枚举所有机组配对时，通常使用元启发式方法，其中以遗传算法（GAs）为主；否则，则使用基于列生成（CG）的混合整数规划技术。值得注意的是，根据文献，遗传算法最多已处理过 45,000 个机组配对。与以往研究显著不同的是，本文考虑了美国一家大型航空公司的 800 多个航班（其月度网络包含超过 33,000 个航班），并预先枚举了全部 400,000 多个机组配对，以测试遗传算法的有效性。为此，本文提出了一种领域知识驱动的定制遗传算法。通过适当的实验，突出了在遗传算法操作（特别是初始化和交叉）中融入领域知识的效用。最后，将所提出的遗传算法的性能与基于列生成的方法（由作者内部开发）进行了比较。尽管发现后者在解的成本质量和运行时间方面表现更好，但希望本文有助于更好地理解领域知识驱动的定制在遗传算法中用于求解包括 CPOP 在内的组合优化问题时的优势和局限性。
英文摘要：
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.
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。
