针对具有多搬运机器人、多线体转换以及缓冲区容量限制等多种约束条件的复杂机器人制造单元实时调度问题，以最小化最大完工时间为优化目标，提出了一种新的两阶段实时调度算法。算法包括初始解构建和局部搜索优化两个阶段。第一阶段，结合先来先服务和轮转法的思想，设计了基于优先级规则的多级反馈队列 (Multi-Layer Feedback Queue, MLFQ) 调度算法，以快速生成优秀的初始解。第二阶段，设计了基于强化学习（Q-learning）机制的模拟退火 算法对初始解进行局部搜索，其中局部搜索使用7种邻域搜索操作，根据Q-learning算法中当前状态的Q-table值选择最佳操作以生成新解。最后对标准数据集修改扩充后进行了算法对比实验，结果中最优值、平均值和方差值的提升验证了算法有效性和稳定性。

In response to the real-time scheduling problem in complex robot manufacturing cell with multiple constraints such as multiple handling robots, multiple line transfers, and buffer capacity limitations, a new two-stage real-time scheduling algorithm is proposed with the objective of minimizing the maximum completion time. The algorithm comprises an initial solution construction phase and a local search optimization phase. In the first phase, a Multi-Layer Feedback Queue (MLFQ) scheduling algorithm based on priority rules is designed by integrating the ideas of first-come-first-serve and round-robin approaches to quickly generate high-quality initial solutions. In the second phase, a simulated annealing algorithm based on reinforcement learning (Q-learning) is designed to perform local search on the initial solutions. The local search utilizes 7 neighborhood search operations and selects the best operation based on the Q-table values of the current state in the Q-learning algorithm to generate new solutions. Experimental comparisons on modified and augmented standard datasets validate the effectiveness and stability of the algorithm through improvements in optimal values, average values, and variance values.

国家重点研发计划(2019YFB600700)