Deep reinforcement learning algorithm for dynamic flow shop real-time scheduling problem
This paper aims at the dynamic flow shop scheduling problem(DFSP),an adaptive deep reinforcement learning algorithm(ADRLA)is proposed to minimize the maximum completion time of DFSP.Firstly,the solving process of DFSP is described by the Markov decision process(MDP),so as to transform the DFSP into a sequential decision problem that can be solved by reinforcement learning.Then,according to the characteristics of DFSP scheduling model,the state representation vector with good state feature discrimination and generalization is designed,and five specific actions are proposed(i.e.reward value).Furthermore,the deep double Q network(DDQN)is used as the agent in ADRLA to make scheduling decisions.After training with the data set determined by a small number of small-scale DFSPs(i.e.the data of three basic elements on different problems),the agent can accurately describe the nonlinear relationship between the state representation vector and the Q-value vector(composed of the Q-value of each action)of different scale DFSPs,so as to carry out adaptive real-time scheduling for various scale DFSPs.Finally,simulation experiments on different test problems and comparison with the algorithm verify the effectiveness and real-time performance of the proposed ADRLA in solving DFSP.
flow shop schedulingarrival of new jobsdeep reinforcement learningdynamic real-time schedulingintelligent scheduling
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