RecovAir: Model-Driven Airline Scheduling Tool for Disruption Recovery

Abstract

The Southwest Airlines scheduling crisis of December 2022 and its consequences have highlighted the importance of robust disruption recovery. To support advancements in schedule recovery, we present RecovAir, an Agent-Based Model that simulates the flow of aircraft, crew, and passengers in an airline’s flight network under departure and arrival rate limits and ad-hoc recovery strategies. By measuring Key Performance Indicators like On-Time Performance, cancellation count, and total delay, RecovAir quantifies simulation outcomes to support comparisons and controlled experiments with recovery parameters. We demonstrate RecovAir’s utility by synthesizing plausible scenarios for both the first day of the 2022 scheduling crisis and a day with zero cancellations in 2024 for Southwest Airlines. Using RecovAir, we simulate these scenarios while varying disruptions, recovery strategies, and a parameter that controls prioritization between delays and cancellations. Our results validate the implementation of RecovAir and show that a fast, greedy algorithm can perform nearly as well as Southwest Airlines’ actions on the first day of the scheduling crisis without initiating any ferry flights (i.e., non-revenue flights to reposition airline crew). We envision RecovAir as a platform on which researchers and airlines can evaluate recovery algorithms holistically and prepare for future disruptions.