关键词: 自进化算法, 强化学习, 知识化制造单元, 径向基函数网络

Abstract: The Job-shop scheduling problem with make-span as goal belongs to the NP complete problem and the valid algorithm for its solution hasn't been given until now.Through analyzing the characteristics of Job Shop knowledgeable manufacturing cell structure, the link constraint model was constructed, and feasible scheduling was obtained by adding constraint to its link-path graph. On these bases, a self-evolution algorithm with learning ability was proposed. Through adopting the q-function of reinforcement learning in algorithm, the needed knowledge was obtained from its environment to improve its search ability. The approximation of q function was implemented by using Radial Basis-Function(RBF)network to avoid too many states in learning process. Numerical simulation results showed that the proposed algorithm had excellent learning and evolution ability for this kind of problems.

Key words: self-evolution, reinforcement learning, knowledgeable manufacturing cell, radial basis-function networks