关键词: 深度学习, 时序差分法, 强化学习, 非置换流水车间, 调度

Abstract: Aiming at the problems of inability to learn with history data and inferior real-time responsibility of traditional scheduling approaches,a comprehensive algorithm of Deep Temporal Difference Reinforcement Learning Network (DTDN) combining reinforcement learning with deep neural network was proposed and applied for flow shop scheduling for the first time.The approach was able to choose actions responding to various input manufacturing states,thus more appropriate for practical order-oriented manufacturing schedule problem.It transformed schedule problem into a Multi-stage Decision Process (MDP) problem,set the manufacturing state as the input of deep neural network model,then used Temporal Difference (TD) method to train the model so as to fit the state function,and selected Simple Constructive Heuristic (SCH) as the candidate action.By adopting the online critic-actor mechanism,the best policy of combined actions of all machines was obtained for each decision step.Computational experiments performed on a well-known non-permutation flow shop benchmark problem set verified the effectiveness of the proposed approach.

Key words: deep learning, temporal difference method, reinforcement learning, non-permutation flow shop, scheduling