Abstract: Aiming at the problem of batch scheduling of aviation structural components,considering the dual-resource constraint and batch production characteristics,a dual-resource lot streaming flexible job-shop scheduling model was established with objectives of makespan and bottleneck machine load,and a hybrid gray wolf optimization algorithm based on sub-batch partition was proposed to solve it.A population guidance strategy was designed to strengthen the population evolution efficiency and the convergence performance of the algorithm.A search strategy based on sub-batch partitioning was proposed,and different operators were adopted to expand the search range of the algorithm and improve the global search capability of the algorithm.To enhance the local search capability of the algorithm,a local search strategy based on key tasks was designed.The effectiveness and superiority of improved strategies and algorithm were verified by the extending standard examples.The significant advantages of the proposed algorithm for solving the dual-resource lot streaming flexible job-shop scheduling problem were verified by production examples of aviation structural components.

Key words: aviation structural components, dual-resource constraints, lot streaming flexible job-shop scheduling, multi-objective optimization, grey wolf algorithm

摘要： 针对航空结构件批量调度问题,考虑双资源约束和批量生产特性,以最大完工时间、瓶颈机器负载为目标,建立双资源批量流柔性作业车间调度模型,并提出了一种基于子批划分的混合灰狼优化算法进行求解。设计一种种群引导策略,强化种群进化效率和算法的收敛性能;提出基于子批划分的搜索策略,采取不同算子扩大算法的搜索范围,提高算法的全局搜索能力;设计基于关键任务的局部搜索策略,增强算法的局部搜索能力。通过扩展标准算例验证了改进策略和算法性能的有效性及优越性。通过航空结构件生产实例验证算法求解双资源约束批量流柔性作业车间调度问题的显著优势。

关键词: 航空结构件, 双资源约束, 批量流柔性作业车间调度, 多目标优化, 灰狼算法