  Scheduling plays an important role in automated production. Its impact can be
found in various fields such as the manufacturing industry, the service
industry and the technology industry. A scheduling problem (NP-hard) is a task
of finding a sequence of job assignments on a given set of machines with the
goal of optimizing the objective defined. Methods such as Operation Research,
Dispatching Rules, and Combinatorial Optimization have been applied to
scheduling problems but no solution guarantees to find the optimal solution.
The recent development of Reinforcement Learning has shown success in
sequential decision-making problems. This research presents a Reinforcement
Learning approach for scheduling problems. In particular, this study delivers
an OpenAI gym environment with search-space reduction for Job Shop Scheduling
Problems and provides a heuristic-guided Q-Learning solution with
state-of-the-art performance for Multi-agent Flexible Job Shop Problems.
