This paper proposes a socio-technical agent-based model developed to increase the understanding of decision-making in an airline operational control center (AOCC). In this model human decision-makers, a Decision Support Tool, and technical systems are included. The selected case study encompasses the
unexpected diversion of a flight from Amsterdam to London City. The decision-making inherent to the disruption management of this unexpected diversion is studied. In the model, in line with cognitive science literature, the agents are simulated to operate in the scrambled, opportunistic, tactical, and strategic con-
trol modes, which differ in the situation awareness and use of technical systems. In these control modes respectively the plurality voting protocol, Borda voting protocol, Clarke Tax Algorithm, and Multi-Criteria Decision-Making (MCDM) are implemented as decision-making mechanisms. The agents in the strategic
control mode demonstrated the ability to make adaptive decisions. In the tactical control mode, the agents showed decision-making characterised by adaptive responses and limited anticipation. In the scrambled and opportunistic control modes, the decision-making was characterised by a lack of adaptation and was solely based on experience. The analysis of the decision-making showed that decision-making based on the airline’s cost model results in different decisions than decision-making by the human operations controllers. Due to this, the operations controllers are not eager to decide on the implementation of a proposed solution strategy by the Decision Support Tool. It showed that the decision-makers often have to make compromises to arrive at a collaborative decision. Furthermore, scenarios that are characterised by reserve unavailability do not require anticipation in the decision-making and can be handled in the opportunistic control mode, which is the lowest control mode that resulted in adequate decision-making. However, when anticipation is required the CDM and OrbiFly systems are essential resources. It is recommended that the MCDM decision-making mechanism can be used to improve the consistency of the operational decision-making and enables the AOCC to learn from previous occurrences. Hereby, the capability of the AOCC to deal with disruptions
through resistance instead of resilience can be extended. Overall, the analysis of the different decision-making mechanisms showed that human operations controllers are essential for adaptive decision-making in the AOCC.