Agent-based modelling and simulation has shown to be a suitable paradigm for analyzing complex airport systems, such as airport security checkpoints. The models can be used to analyze important airport terminal performance areas, such as security and efficiency. However, limited knowledge exists on how agent-based models can be used to aid airport managers in their decision making. The agent-based models allow to estimate the criteria of the alternatives of a decision problem, but without criteria weights and score aggregation, the alternatives can not be compared or ranked. The main branch of techniques to establish weights for multiple criteria such that a score can be aggregated for decisions is called multi-criteria decision making. Thus, integrating multi-criteria decision making methods with agent-based models is a method to provide decision support for airport managers. Therefore, this research aims to answer how multi-criteria decision making methods can be integrated with agent-based models to provide decision aid to airport terminal decision makers with the aim of improving security and efficiency. To answer this research question, a methodology was proposed which combines agent-based models, multi-criteria decision making, discrete choice models, and expert knowledge. The methodology is applied to a case study in which several alternatives of configuring personnel are available. The results show that efficiency focused operators, which have low accuracy, introduce most variance between best and worst alternatives. The best alternatives have efficiency focused operators placed on peak hours because they decrease the queue in the peak hours, while maximizing overall hit rate because less passengers pass per lane in peak hours compared to non-peak hours. For realistic variations of operator performance, operator placement is found to have a significant influence on security checkpoint performance.