Dispatching rule and Q-learning based dynamic job shop scheduling algorithm
Dispatching rules are widely used in real jobshop,to select the best dispatching rules under the specific scenarios and improve the adaptability and optimization ability of dispatching rules under uncertain dynamic production process,an improved jobshop dynamic scheduling algorithm based on dispatching rules and Q-learning was proposed.The dynamic flex-ible job shop scheduling problem with new job insertions randomly was addressed aiming at minimizing the maximum delay time.The state space representation method and the reward mechanism,as well as the search strategy based on Boltzmann sampling function were elaborated under Q-learning framework for improving the ability to explore and use rules.Besides,to inherit the interpretability of dispatching rules and be able to process those randomly inserted jobs in real-time,the action set was constructed using several classical dispatching rules such as Shortest Processing Time(SPT)and Earliest Due Date(EDD),which obtained optimal dispatching rules under specific scenarios at specific time points by supporting the a-gent.Simulation results confirmed that the proposed dispatching ruler and Q-learning based algorithm could effectively han-dle randomly inserted jobs and achieve good scheduling performances.
万方数据
维普
