关键词: 知识化制造, 航空发动机, 装配, 自适应调度, Q学习

Abstract: Aiming at the adaptive scheduling problem of aero-engine assembly in uncertain production environment,by combining the real-time feature of reinforcement learning with the self-adaptation characteristics of knowledgeable manufacture system,a Double-layer Q-learning(D-Q)method was proposed for the aero-engine assembly problem.The top level of Q-learning focused on the local target of minimizing machine idleness and balancing machine loads by finding the proper dispatching policy for the job allocation on parallel machines;the bottom level of Q-learning focused on the global target of minimizing the all jobs'overall earliness by learning the optimal scheduling policy for the jobs that allocated to machines.A function approximation Q(λ)learning method was used to update the value function.By defining the three key elements of action,state and reward function rationally in reinforcement learning,the adaptive assembly scheduling problem of aero-engine was transformed into the reinforcement learning problem.Simulation experiments indicated that the proposed D-Q algorithm could achieve better results than the Single-layer Q-learning(S-Q)by selecting scheduling rules timely in two layers.The scheduling result was outperformed the single rule,and the good adaptive performance was exhibited.

Key words: knowledgeable manufacturing, aero-engine assembly, adaptive scheduling, Q-learning