Pattern driven dynamic scheduling approach using reinforcement learning

Production scheduling is critical for manufacturing system. Dispatching rules are usually applied dynamically to schedule the job in the dynamic job-shop. The paper presents an adaptive iterative scheduling algorithm that operates dynamically to schedule the job in the dynamic job-shop. In order to get adaptive behavior, the reinforcement learning system is done with the phased Q-learning by defining the intermediate state pattern. We convert the scheduling problem into reinforcement learning problems by constructing a multi-phase dynamic programming process, including the definition of state representation, actions and the reward function. We use five heuristic rules, CNP-CR, CNP-FCFS, CNP-EFT, CNP-EDD and CNP-SPT, as actions and the scheduling objective: minimization of maximum completion time. So a complex dynamic scheduling problem can be divided into a sequential sub-problem easier to solve. We also analyze the time and the solution and present some experimental results.