In the dynamic and complex world of air transportation, airports can serve as vital hubs that connect numerous of destinations and facilitate the smooth flow of passengers and aircraft. However, the efficient operation of hub airports can be significantly disrupted by various factors, one of which is the reduction in runway capacity. Runway capacity reductions can be caused by several factors such as maintenance activities, adverse weather conditions, or airspace congestion, resulting in flight cancellations and delays that impact carefully planned schedules of airlines. By evaluating the expected disruption leading to a runway capacity reduction a day before operations, proactive measures could be taken with effective consideration of aircraft, passenger and crew schedules. To address this challenge, a stochastic, multiple scenario, decision support tool was created with the aim of providing the airline with required proactive cancellations for an expected disruption. A case study is performed on the framework in collaboration with a major European airline providing data. Validation has been performed by benchmarking the results of the framework with actual decisions and results of a deterministic tool including only a single scenario. Finally, a new disruption scenario was used to demonstrate the capabilities of the framework and show the sensitivity of some main input parameters. Benchmarking results of the framework with actual implemented cancellations and results of a deterministic tool show that the framework is capable of providing cheaper cancellation options, and overall, a cheaper cost of solving the disruption while complying with crew reserves and maintaining valuable passenger connections.