Airlines are subject to external events that may disrupt their day-to-day operations. These disruptions lead to additional costs for crew, fuel, aircraft, maintenance and a loss of passenger goodwill. Therefore, each airline has an Airline Operations Control (AOC) department that is responsible for disruption management. The problems faced by AOC can be highly complex and decisions have to be made under severe time constraints, economic pressure and various uncertainties. In addition, slacks in the schedule and availability of standby resources affect disruption management. To address this research question, a simulation model is developed which reflects the ontology of an AOC and its environment. Scenarios that have designed by industry experts are selected from the literature that represents a real-world disruption. Subsequently, a vast amount of qualitative data has been gathered that describe the socio-technical system in terms of uncertainties the controllers face, the tasks they perform and their interactions. By structuring the interactions and by conceptualizing the uncertainties into conditions and parameters, a model is designed that expresses both quantitative and qualitative aspects of AOC and it operating environment. The model is implemented into an order-sorted predicate logic software environment that is able to describe temporal properties. Model evaluation is done using a case by case approach in which for each case, condition sequences and parameters are selected. By conducting sensitivity analysis, the impact of the size of slacks, availability of standby resources and uncertainties on direct costs, utilization of reserve resources and passenger goodwill is determined. Subsequently, the implications of these factors on the decision-making process are analysed. For instance, evaluation of the results show that a lack of information to overcome an uncertainty could result in extra (unnecessary) tasks to be performed by other controllers in the decision-making process and it could result in deployment of costly recovery strategies.