Airline disruption management traditionally seeks to address three problem dimensions: aircraft scheduling, crew scheduling, and passenger scheduling, in that order. However, current efforts have, at most, only addressed the first two problem dimensions concurrently and do not account for the propagative effects that uncertain scheduling outcomes in one dimension can have on another dimension. In addition, existing approaches for airline disruption management include human specialists who decide on necessary corrective actions for airline schedule disruptions on the day of operation. However, human specialists are limited in their ability to process copious amounts of information imperative for making robust decisions that simultaneously address all problem dimensions during disruption management. Therefore, there is a need to augment the decision-making capabilities of a human specialist with quantitative and qualitative tools that can rationalize complex interactions amongst all dimensions in airline disruption management, and provide objective insights to the specialists in the airline operations control center. To that effect, we provide a discussion and demonstration of an agnostic and systematic paradigm for enabling expeditious simultaneously-integrated recovery of all problem dimensions during airline disruption management, through an intelligent multi-agent system that employs principles from artificial intelligence and distributed ledger technology.

翻译：航空中断管理传统上力求解决三个问题:飞机时间安排、机组人员时间安排和乘客日程安排,按此顺序排列;不过,目前的努力最多只能同时处理头两个问题,而没有考虑到一个方面不确定的日程安排结果可能对另一个方面产生的宣传效应;此外,现有航空中断管理办法包括决定对运营当日的航班日程安排中断采取必要的纠正行动的人类专家;然而,人类专家处理大量信息的能力有限,无法同时作出强有力的决定,同时处理中断管理期间的所有问题层面;因此,有必要加强拥有定量和定性工具的人类专家的决策能力,使航空中断管理所有层面之间的复杂互动合理化,并向航空业务控制中心的专家提供客观的见解;为此,我们通过利用人工智能情报和分配分类技术的原则,通过智能型多试剂系统,讨论和展示在航空中断管理期间迅速综合恢复所有问题层面的敏感和系统。