In this chapter we have introduced the Airline Operations Control Problem as well as the Airline Operations Control Centre (AOCC), including typical organizations and problems, the current disruption management (DM) process and a description of the main costs involved. We described our agent-based approach to this problem, including the reasons that make us adopt an agent and multi-agent system (MAS) paradigm; the MAS architecture with agents, roles and protocols as well as some agent characteristics like autonomy and social-awareness; the decision mechanisms, including the costs criteria and negotiation protocols used and examples of the problem solving algorithms. Using data from a real airline company, we tested our approach and discussed the results obtained by three different methods. We have shown that our approach is able to select solutions that contribute to a better passenger satisfaction and that produce shorter flight delays when compared with methods that only minimize direct operational costs. We are working on several improvements. Some of them are already implemented. However, we did not perform, yet, enough tests to have meaningful results. These are our goals: Improve autonomy and learning characteristics of the Monitor agent, so that he is able to consider new events (or change existing ones) according to the experience he gets from monitoring the operation, without relying exclusively on the definition of events created by the human operator. Working on a protocol at the Manager Agent team level that allows a better coordination and improves the distributed problem solving characteristics of our approach. For example, including in each team, knowledge provided by other teams to improve the objective function of each specialist agent, with parameters of the other dimensions (aircraft, crew and passenger). Solving problems learning by example, applying Case-Based Reasoning (CBR). Increase robustness of future schedules by applying the knowledge gathered from learning by example. Study the behaviour and compare the results, of several problem solving algorithms, including the ones that implement heuristics to specific problems. The idea is to classify the algorithms according to their success rate in solving specific types of problems in this domain.