Genetic reinforcement learning algorithm for permutation flow-shop scheduling problem

Considering the inherent complexity of Flow-shop scheduling problem, an algorithm named Genetic Reinforcement Learning, GRL, is designed to solve it. First, state variable and action variable are employed to transform the combinational-optimization scheduling problem into sequential-decision problem. Secondly, a Q-Learning algorithm is proposed to integrate with a Genetic Algorithm based on combined operators. The agent is supervised by chromosomes' good modes and their fitness information. As a result, the agent's learning performance is improved. The genetic population is also meliorated by the local optimization of Reinforcement Learning to each chromosome. So GA and RL are integrated in GRL to solve the Flow-shop scheduling problem. Thirdly, several self-adaptive policies are introduced into GRL algorithm to make it balance in exploitation and exploration. Finally, the algorithm is validated by simulation experiments.