Flexible Job Shop scheduling problem of robotic manufacturing cell is a new kind of scheduling problem with wide engineering application background and tough challenges,which attracts much attention of academia and industry. The content and features of flexible Job Shop scheduling problem of robotic manufacturing cell were analyzed,a scheduling model based on molds manufacturing with the op-timizing targets to minimal makespan was proposed in this paper. A Chaotic Quantum-behaved Particle Swarm Optimization algorithm (CQPSO) was put forward to solve the model. Based on the Quantum-behaved Particle Swarm Optimization (QPSO),the improved Tend chaotic mapping mechanism was introduced. The shortcoming of easily falling into local minimum for QPSO could be avoided, meanwhile,the fast convergence speed of QPSO could be kept in this algorithm. So the effectiveness of algorithm was improved. The sim-ulation results show that CQPSO could effectively solved the flexible Job Shop scheduling problem of robotic manufacturing cell.%柔性Job Shop类型机器人制造单元调度问题是一类新的具有广泛工程应用背景而又极富挑战的调度问题,引起了学术界和工业界的极大关注。文中分析了柔性Job Shop类型机器人单元调度问题的内容与特点,并以模具生产为背景,构建了一种以工件组最大完工时间最小为目标的Job Shop类型机器人单元调度模型,进而提出了一种混沌量子粒子群算法( CQPSO)用于模型求解。该算法在量子粒子群算法( QPSO)基础上,引入改进的Tent混沌映射机制,在保持QPSO算法收敛速度快的同时,克服了其易陷入局部极小值的缺点,提高了算法求解效率。仿真实验结果表明,CQPSO算法在求解柔性Job Shop类型机器人调度问题方面具有较大的应用优势。
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