Publication Date

5-1-2025

Document Type

Article

Publication Title

Aerospace

Volume

12

Issue

5

DOI

10.3390/aerospace12050444

Abstract

Every year, airlines invest considerable resources in recovering from irregular operations caused by delays and disruptions to aircraft and crew. Consequently, the need to reschedule aircraft and crew to better address these problems has become pressing. The airline scheduling problem comprises two stages—that is, the Aircraft-Routing Problem (ARP) and the Crew-Pairing Problem (CPP). While the ARP and CPP have traditionally been solved sequentially, such an approach fails to capture their interdependencies, often compromising the robustness of aircraft and crew schedules in the face of disruptions. However, existing integrated ARP and CPP models often apply static rules for buffer time allocation, which may result in excessive and ineffective long-buffer connections. To bridge these gaps, we propose a robust integrated ARP and CPP model with two key innovations: (1) the definition of new critical connections (NCCs), which combine structural feasibility with data-driven delay risk; and (2) a spatiotemporal delay-prediction module that quantifies connection vulnerability. The problem is formulated as a sequential decision-making process and solved via a novel multi-agent reinforcement learning algorithm. Numerical results demonstrate that the novel method outperforms prior methods in the literature in terms of solving speed and can also enhance planning robustness. This, in turn, can enhance both operational profitability and passenger satisfaction.

Funding Number

KYCX25_0615

Funding Sponsor

Graduate Research and Innovation Projects of Jiangsu Province

Keywords

aircraft routing, crew pairing, reinforcement learning, robust integrated model

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Aviation and Technology

Download

Share

COinS