Planning is determining which actions to take to achieve a particular goal or a set of goals. Scheduling is figuring out when to execute the actions and using what resources. However, the interplay between planning and scheduling varies significantly between static and dynamic environments. Dynamic changes demand real-time adaptability, often leading to the intertwining of these processes to meet evolving objectives. Preferences play a significant role in human decision-making, particularly as they relate to deciding on what actions to take, how to accomplish them, and when to act. In this thesis, we address the problem of how to generate adaptable schedules to maximize user preferences in dynamic environments. ☐ In our preliminary work on solving the dynamic scheduling problem, we show that users have preferences over planning goals, sub-goals, and primitive actions, and a comprehensive representation of these preferences is needed to solve this problem, which the current literature lacks. In our primary contribution, we extend C TAEMS (a multi-agent task representation language) to include preferential accumulation functions and define new coordination relationships over preferences. We then establish a scheduling pipeline consisting of a series of consistency-checking algorithms which form a basis for the dynamic scheduling algorithms. ☐ We introduce two novel algorithms, a reactive and a proactive scheduling algorithm that dynamically reschedule on the fly to adapt to the uncertainties in the environment. We then introduce two new heuristics to minimize the time while generating near-optimal preferential schedules and show that our algorithms outperform the state-of-the-art scheduling algorithms. Lastly, to address the problem of predicting action outcomes, we propose an edge architecture and a learning approach, and show our results on an ongoing trial.