Abstract: Aiming at the complex manufacturing environment of aeronautical components flexible job shop,a flexible job shop scheduling problem with Extended Dual Resource Constraints (EDRC) was proposed with considering the constraints such as the wide application of Numerical Control (NC) machine,the interpolation of machining operations/non-machining operations sequence in the process routes and the key processes with cooperation of special machine and labor,and a scheduling model was constructed.A novel multi-group teaching-learning-based optimization was developed to optimization the selection of machine and people for components and the sequence of the operations.Considering the characteristic of no personnel assistance during machine running of NC equipment,a three-layer encoding and a new decoding method were approached to avoid using conflict of machine and people.In addition,various teaching/self-learning factors,self-learning/communication strategies were designed based on the basic core framework of TLBO unchanged to ensure solve discrete problems effectively and balancing the global and local search ability of the algorithm.Experimental results on several random benchmarks and a real scheduling case of aerospace components demonstrated that the proposed algorithm had good performance on EDRC problem and proved the effectiveness of the scheduling model.

Key words: aerospace components production, flexible job shop scheduling, dual resource constraints, numerical control machine, teaching- learning-based optimization

摘要： 针对航空构件生产车间的复杂制造环境,综合考虑数控设备的广泛应用,工艺路线中机加/非机加工序穿插及重要工序需特定设备人员协同完成等约束,提出了考虑设备人员的扩展双资源约束柔性作业车间调度问题,并构建了调度数学模型。针对问题特征及复杂性,提出一种新颖的多小组协同教与学优化算法对构件的设备人员资源进行选择及工序排序问题进行优化。针对数控设备运行期间无需人员辅助这一特性,设计了3层编码及新型解码方式以避免设备人员的使用冲突,此外在教与学算法核心框架不变的基础上,设计多种教学/自学因子、自学/交流策略,使算法有效解决离散型问题,同时平衡算法的全局及局部搜索能力。通过随机案例和实际航空构件案例仿真分析,验证了算法求解扩展双资源约束调度问题的有效性及模型的正确性。

关键词: 航空构件制造, 柔性作业车间调度, 双资源约束, 数控设备, 教与学算法