According to a recent report by the U.S. Senate Joint Economic Committee, the costs of domestic air traffic delays were estimated to be a staggering $41 billion in 2007. Of this, $19.1 billion was attributed to airline operating costs and $12 billion was attributed to passenger delays. In instances of irregular operations, an airline's primary objective is to get operations back to normal as quickly as possible while incurring minimal cost. Historically, airlines have prioritized recovering schedules for aircraft and crew before attempting to reaccommodate disrupted passengers. The literature has not examined how proactive movement of passengers can be used to mitigate the impact of irregular operations. This work bridges this gap by exploring how early standby fees (which influence customer behavior) can be utilized to improve operations. This research uses a variety of public and private data sources to construct a micro-level simulation to explore the impact that early standby pricing policies have on aggregate and average delay for disrupted passengers. Three distinct modules developed for use in the simulation have broader applications to the research community: (1) logic for a passenger reaccommodation algorithm; (2) a model that predicts the number of early standby passengers; and (3) a model to predict the probability a passenger misses a connecting flight. Preliminary results confirm the hypothesis that early standby pricing policies affect both profitability and operational performance. A simple example illustrates that an airline faces an important tradeoff when setting an early standby pricing policy: the early standby fee that maximizes an airline's profitability is different from the early standby fee that yields the lowest aggregate and average delay metrics for customers. This dissertation proposes a new product that remedies these competing objectives by improving them both simultaneously.