The focus of the book so far has been on the development of models and solution methods to obtain high quality predicted schedules. While these two components are necessary towards the implementation of optimization-based scheduling methods, they are not sufficient by themselves. Specifically, the model must be solved repeatedly, in real time, taking into account new information and disturbances. The goal of the present chapter is to provide high-level understanding on how the optimization model should be used to obtain a real-time scheduling algorithm that yields high-quality implemented schedules.In Section 14.1, we motivate why repeated optimization is necessary, introduce necessary notation, and present the overall framework we use. In Section 14.2, we present a state-space model which offers a natural way to formulate the optimization model that is updated and solved in real time. In Section 14.3, we present the basic considerations and a general simulation-based framework for designing real-time scheduling algorithms, and, close, in Section 14.4, with a discussion on how integration with other functions can offer early feedback leading to faster recourse. We use models and examples based on network environments, but all the ideas and methods are directly applicable to problems in sequential environments.