具有多机组基地和轴辐式子网的大型网络的航空公司机组配对优化框架
Airline Crew Pairing Optimization Framework for Large Networks with Multiple Crew Bases and Hub-and-Spoke Subnetworks
摘要：
机组配对优化的目标是生成一组航班序列（机组配对），以最小的成本覆盖航空公司航班计划中的所有航班，同时满足几个合法性约束。考虑到机组人员运营成本是航空公司的第二大支出，CPO对航空公司的业务生存能力至关重要。它提出了一个NP-难的组合优化问题，为了解决这个问题，现有技术依赖于将底层的线性规划问题（IPP）放松为线性规划问题（LPP），通过列生成（CG）技术解决后者，并对所得的LPP解进行积分。然而，随着飞行网络（具有大量航班、多个机组人员基地和/或多个轴辐式子网络的飞行网络）的规模和复杂性的增长，常规CG实践的效用已变得可疑。提出了一种航空公司机组配对优化框架AirCROP，其组成模块包括合法机组配对生成器、初始可行解生成器和基于CG实现的优化引擎。在本文中，除了AirCROP的模块的设计，深入了解有关这些模块如何相互作用的重要问题，其中的文献是沉默的，已被分享。这些与AirCROP性能对以下因素的敏感性有关：给定问题多次运行的变异性来源、初始化方法以及LPP解决方案和IPP解决方案的终止参数。AirCROP的有效性已经在现实世界的大规模和复杂的飞行网络（超过4200个航班，15个机组人员基地和十亿多个配对）中得到了证明。希望本论文能为相关研究和应用提供一个重要的里程碑。
英文摘要：
Crew Pairing Optimization aims at generating a set of flight sequences (crew pairings), covering all flights in an airline's flight schedule, at minimum cost, while satisfying several legality constraints. CPO is critically important for airlines' business viability, considering that the crew operating cost is their second-largest expense. It poses an NP-hard combinatorial optimization problem, to tackle which, the state-of-the-art relies on relaxing the underlying Integer Programming Problem (IPP) into a Linear Programming Problem (LPP), solving the latter through Column Generation (CG) technique, and integerization of the resulting LPP solution. However, with the growing scale and complexity of the flight networks (those with a large number of flights, multiple crew bases and/or multiple hub-and-spoke subnetworks), the utility of the conventional CG-practices has become questionable. This paper proposed an Airline Crew Pairing Optimization Framework, AirCROP, whose constitutive modules include the Legal Crew Pairing Generator, Initial Feasible Solution Generator, and an Optimization Engine built on heuristic-based CG-implementation. In this paper, besides the design of AirCROP's modules, insights into important questions related to how these modules interact, which the literature is otherwise silent on, have been shared. These relate to the sensitivity of AirCROP's performance towards: sources of variability over multiple runs for a given problem, initialization method, and termination parameters for LPP-solutioning and IPP-solutioning. The efficacy of the AirCROP has been demonstrated on real-world large-scale and complex flight networks (with over 4200 flights, 15 crew bases, and billion-plus pairings). It is hoped that with the emergence of such complex flight networks, this paper shall serve as an important milestone for affiliated research and applications.
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。
