A common practice in production scheduling
- Manual methods performed by human planners but there is a higher risk of an error.
- Heuristic algorithms integrated into complex software tools such as First In First Out (FIFO), Shortest Processing Time (SPT), Shortest Setup Time (SST) etc. may find good solutions but can omit other unique and unexpected solutions.
- Global optimization approaches such as genetic algorithms could find better solutions but are time-costly.
Reinforcement learning-based solutions
- RL agent can discover unique and unexpected solutions.
- This approach can perform more effectively in searching the space of possible actions slightly in a similar way to some global genetic optimization algorithms.
- It takes more time to learn the agent but when ready, RL agents can generate very quickly new production schedules.
- RL is not suitable for every case but generally very likely to overperform classical methods.