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To address the uncertainty of production environment in knowledgeable manufacturing system, an interoperable knowledgeable dynamic-scheduling system based on multi-agent is designed, wherein an adaptive scheduling mechanism based on the state membership grade weighted Q-learning (known as SMGWQ-learning) is proposed for guiding the equipment agent to select proper scheduling strategy in a dynamic environment. To avoid the side effect of large state space and minimize errors between the clustering and real states, the state membership grade, defined as weight coefficients, is incorporated into the weighted Q-value update so that several Q-values can be updated simultaneously in an iteration. Results from our convergence analysis and simulation experiments show the effectiveness of the proposed strategy that endows the scheduling system with higher intelligence, interoperability and adaptability to environmental changes by self-learning. [ABSTRACT FROM AUTHOR]

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]