Abstract: Operating disruptions result in enormous costs across spatial-temporal networks. For instance, air traffic disruptions result in flight delays, cancellations, passenger misconnections, creating high costs to aviation stakeholders. Businesses often design recovery plans in response to past disruptions while preparing for future disruptions. However, future disruptions often can only be characterized partially and probabilistically. We propose a joint stochastic reactive and proactive approach to disruption management (SRPDM), which optimizes airline disruption recovery given partial and probabilistic forecasts of future congestion…