ADAPTIVE SCHEDULING OF AERO-ENGINE ASSEMBLY BASED ON Q-LEARNING IN KNOWLEDGEABLE MANUFACTURE

An adaptive optimization scheduling AQ (Assembly Q-learning) algorithm of knowledgeable manufacture is proposed for an aero-engine assembly line to address the scheduling of an aero-engine assembly workshop in an uncertain manufacture system, which combines the real-time feature of Q-learning with the self-adaptation feature of knowledgeable manufacture system. An adaptive scheduling model of aero-engine assembly based on Q-learning is developed for the purpose of minimizing earliness penalty and completion time cost. New production scheduling rules are proposed to deal with the aero-engine assembly scheduling problem. Addressing the characteristics of aero-engine assembly, four system-state features are defined, and a proper reward is designed as a reward function. Coherence of the reward function and the scheduling objective is proved by theorem. Simulation tests show that the developed algorithm is superior to other scheduling rules in various situations. Particularly, in the ever-changing assembling environment, better results are guaranteed by the desirable adaptivity of the proposed algorithm. An adaptive optimization scheduling algorithm of an aero-engine assembly workshop in a knowledgeable environment is designed, whereby a problem-solving method based on Q-learning and process simulation is activated. The coherence of the reward function and the scheduling objective is proven by theorem. The developed algorithm is superior to the other scheduling rules in the vast majority of cases. Thus, through self-learning, the aero-engine assembly workshop has much higher efficiency and adaptability than the others