A distributionally robust optimization approach for airline integrated recovery under in-flight pandemic transmission risks

The COVID-19 pandemic has hit the airline industry hard, leading to heterogeneous epidemiological situations across markets, irregular flight bans, and increasing operational hurdles. Such a melange of irregularities has presented significant challenges to the airline industry, which typically relies on long-term planning. Given the growing risk of disruptions during epidemic and pandemic outbreaks, the role of airline recovery is becoming increasingly crucial for the aviation industry. This study proposes a novel model for airline integrated recovery problem under the risk of in-flight epidemic transmission risks. This model recovers the schedules of aircraft, crew, and passengers to eliminate possible epidemic dissemination while reducing airline operating costs. To account for the high uncertainty with respect to inflight transmission rates and to prevent overfitting of the empirical distribution, a Wasserstein distance-based ambiguity set is utilized to formulate a distributionally robust optimization model. Aimed at tackling computation difficulties, a branch-and-cut solution method and a large neighborhood search heuristic are proposed in this study based on an epidemic propagation network. The computation results for real-world flight schedules and a probabilistic infection model suggest that the proposed model is capable of reducing the expected number of infected crew members and passengers by 45% with less than 4% increase in flight cancellation/delay rates. Furthermore, practical insights into the selection of critical parameters as well as their relationship with other common disruptions are provided. The integrated model is expected to enhance airline disruption management against major public health events while minimizing economic loss.

Automated summary

The COVID-19 pandemic has greatly impacted the airline industry, resulting in diverse epidemiological situations, irregular flight bans, and operational challenges. This study proposes a novel model to address the integrated recovery problem for airlines during in-flight epidemic transmission risks. The model aims to recover aircraft, crew, and passenger schedules to prevent epidemic dissemination while minimizing operating costs. By utilizing a distributionally robust optimization model and innovative solution methods, the proposed model shows promising results in reducing infections and flight disruptions. It provides practical insights into critical parameter selection and their relationship with other disruptions, contributing to improved airline disruption management and economic loss reduction during major public health events.