Time-based air traffic flow management balances demand versus capacity at facilities by assigning Controlled Times of Arrival (CTAs) to incoming flights. However, existing systems do not differentiate delay costs among different aircraft and do not take user preferences into consideration. From a business perspective, it is essential to understand user preferences and to allow users to engage in decision-making. This dissertation presents results of simulations of strategies to incorporate business-driven airline preferences into these air traffic flow management systems following a Collaborative Decision-Making paradigm. We evaluate optimization models and heuristics to assign CTAs based on user-provided information and priority preferences in a way that minimizes the total CTA delay cost. Potential savings were quantified by comparing the results with the default first-come-first-served (FCFS) scheme. Monte Carlo simulations are conducted using historical flights data under a variety of realistic scenarios. Results show that our proposed heuristic, 2OptSwap, could reduce CTA delay cost between 20% and 30% relative to the FCFS baseline scheme, maintaining an average of 5.9% gap compared to the optimal solution. It is also shown that the heuristic could potentially realize the same level of cost savings regardless of whether the incoming flights are behind their schedules or not. Additional findings include that by starting the flow management further away, we could achieve more delay cost savings. The environment in the air space (such as the wind and the facility capacity) is constantly changing. Therefore, a rolling horizon approach was integrated into this air traffic flow management system. This approach allows the system to incorporate the most recent information at each epoch and solve the problem in a dynamic fashion. Real-time fairness metrics and adjustment methods are defined such that performance measurements in the previous epochs can be used for adjustments in future decision-making. Simulation results show that these fairness adjustments can help achieve a fairer benefits distribution among carriers and achieve a win-win solution.