Reinforcement Learning Approach for Multi-Agent Flexible Scheduling Problems
This addresses scheduling challenges in automated production for industries like manufacturing, service, and technology, though it is incremental as it builds on existing reinforcement learning methods.
The researchers tackled multi-agent flexible job shop scheduling problems, an NP-hard task, by developing a reinforcement learning approach that includes an OpenAI gym environment with search-space reduction and a heuristic-guided Q-Learning solution, achieving state-of-the-art performance.
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.