We discuss a systematic way to design online scheduling algorithms to achieve better closed-loop performance. Specifically, we address short-term scheduling, and focus on determining the horizon length and re-optimization time-step, and explore objective-function modifications and added constraints. First, we show that finding open-loop and closed-loop schedules are two different problems and that there is considerable potential to improve the closed-loop performance, but this task is non-trivial. Second, we outline and quantitatively define three key production system characteristics that are pertinent to the design of online scheduling algorithms: (i) production load; (ii) order-size max-mean relative difference; and (iii) time-constants. Third, we discuss how these affect the choice of the re-optimization time-step and the horizon length. Fourth, we evaluate the role of demand uncertainty and how it can be mitigated through better tuning of the above algorithmic parameters. Finally, we present a framework that summarizes the design procedure for online scheduling algorithms.
All Science Journal Classification (ASJC) codes
- Chemical Engineering(all)
- Computer Science Applications
- Chemical production scheduling
- Process uncertainty