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Self-evolution problem of an aircraft-engine assembly workshop with uncertain number of assembly times in knowledgeable manufacturing environment is studied in this article. The whole dynamic self-evolution process is decomposed into a series of static ones by using the rolling horizon procedure. A rolling rule, which is employed to select operations into rolling windows, is designed on the basis of the level priority of each operation. An algorithm for the self-evolution algorithm is then proposed. Due to the deterministic production information, a static decision sub-problem is needed to be solved at each decision point. A general mathematical model of sub-problems is built to minimize the production cost. For solving it, a bi-level genetic algorithm is proposed. In the lower level genetic algorithm, an improved decoding method is also designed by considering production features. Finally, simulation results demonstrate the feasibility and validity of the model and algorithms. Then, computational data indicate that the system with self-evolution capability pays lower costs for production. [ABSTRACT FROM AUTHOR]

An adaptive scheduling strategy based on state-action uncertainty bias based Q-learning (SAUBQ-learning) is proposed for adaptive scheduling in knowledgeable manufacturing environment. Aiming at the problem of slow convergence and long training time in conventional Q-learning process, a state-action uncertainty measure is defined by information entropy, on the basis of which a Q-learning action bias information function is defined. The bias information is integrated into Q-learning system by designing heuristic Q-learning reward function, and the optimal strategy invariance and convergence of SAUBQ-learning are proved. In learning process, search space is adjusted by bias information, the effective state-action number explored by Q-learning is reduced. Furthermore, the bias information is also continually updated by Q-learning results, and misleading Q-learning process is avoided. Simulation experiment results indicate that the strategy has better adaptive feature in dynamic environment and the feature of converging quickly in large state space, and improves the efficiency of scheduling. [ABSTRACT FROM AUTHOR]