智能生产是智能制造的重要发展方向，智能工厂是推动智能生产的关键因素。作为智能工厂的核心，柔性作业车间调度技术被广泛研究。在实际生产中，车间调度研究面临复杂性、动态性、多目标优化等多重挑战，加工订单和加工设备的不合理分配常常导致生产效率低下，阻碍智能工厂的智能化和高端化发展。同时，柔性车间中常出现机器故障、紧急订单和订单取消等突发性干扰，影响生产计划的稳定性。此外，车间设备的损耗故障导致维护活动的不可预期，严重影响生产活动的连续性。因此，研究高效的柔性车间调度技术从而合理配置车间生产资源，成为智能制造技术赋能新质生产力的关键。论文研究柔性车间的多目标优化静态调度、多目标优化动态调度及调度与预测性维护多目标协同优化，旨在构建高效智能的生产调度体系。结合量子粒子群算法建立不同场景下的车间调度模型，综合考虑订单约束和设备约束间的耦合关系，优化寻优算法的搜索能力，寻求最优生产调度方案。

（1）针对车间静态调度问题，研究基于量子粒子群算法的柔性车间多目标优化静态调度方法。提出一种多目标改进量子粒子群算法，建立以完工时间、最大机器负荷和机器总负荷为优化目标的静态调度模型；设计混合初始化方法获得高质量的初始种群，采用变邻域搜索策略解决算法局部最优问题；通过非支配排序和拥挤度计算筛选出静态调度最优解集。

（2）针对车间动态调度问题，研究突发干扰下的柔性车间多目标优化动态调度方法。设计一种应对突发干扰的动态优化调度策略，建立以最大完工时间、机器总负荷和鲁棒性为优化目标的重调度模型；提出一种基于事件触发的重调度机制，建立预-反应动态调度模型提升响应速率；采用完全重调度策略求解机器故障和订单取消问题，采用重排插单处理紧急订单，优化工件和机器资源释放；通过不同动态场景下的重调度方案的鲁棒性验证所提方法的有效性。

（3）针对车间调度与预测性维护协同优化问题，研究柔性车间多目标调度与预测性维护协同管控方法。考虑订单工艺路径和产线机器资源复杂耦合特性，建立以最大完工时间、最大机器负荷和机器总负荷为优化目标的柔性车间调度模型；采用威布尔分布模型准确刻画车间设备的恶化效应，基于设备预测性维护模型设计最优维护策略；提出基于改进量子粒子群算法的柔性车间多目标调度与预测性维护协同优化方法，构造邻域搜索算子和两种变异算子，提升调度与维护协同优化算法的搜索能力与搜索精度。

（4）针对航空精密零部件车间人工排产及突发干扰问题，设计基于supOS系统的车间动态调度平台。建立以车间、数据、模型和产线应用为核心的加工车间系统架构，提升加工车间的信息化管理水平；针对机器故障和紧急订单突发扰动进行重调度，为管理者提供决策支持。

Intelligent production is an important development direction of intelligent manufacturing, and intelligent factories are a key factor in promoting intelligent production. As an important carrier of smart factories, flexible job shop scheduling technology has been widely studied. In actual production, the unreasonable allocation of processing orders and processing equipment often leads to low production efficiency, hindering the intelligent and high-end development of smart factories. Sudden disturbances such as machine malfunctions, urgent orders, and order cancellations often occur in flexible workshops, affecting the stability of production plans. In addition, the wear and tear of workshop equipment leads to unpredictable maintenance activities, seriously affecting the continuity of production activities. Therefore, researching efficient flexible workshop scheduling technology to rationally allocate workshop production resources has become the key to empowering new quality productivity with intelligent manufacturing technology. This article studies the multi-objective optimization static scheduling, multi-objective optimization dynamic scheduling, and multi-objective collaborative optimization of scheduling and predictive maintenance in flexible workshops, aiming to build an efficient and intelligent production scheduling system. Combining quantum particle swarm optimization algorithm to establish workshop scheduling models in different scenarios, comprehensively considering the coupling relationship between order constraints and equipment constraints, optimizing the search ability of the optimization algorithm, and seeking the optimal production scheduling solution.

(1) A multi-objective optimization static scheduling method for flexible workshops based on quantum particle swarm optimization algorithm is studied for the static scheduling problem in the workshop. Propose an improved quantum particle swarm algorithm based on nonlinear factor control, and establish a static scheduling model with completion time, maximum machine load, and total machine load as optimization objectives; Design a mixed initialization method to obtain a high-quality initial population, and use a variable neighborhood search strategy to optimize the local optimal problem of the algorithm; Select the optimal solution set for static scheduling through non dominated sorting and congestion calculation.

(2) A multi-objective optimization dynamic scheduling method for flexible workshops under sudden disturbances is studied for the dynamic scheduling problem in the workshop. Design a dynamic optimization scheduling strategy to deal with sudden disturbances, establish a rescheduling model with maximum completion time, total machine load, and robustness as optimization objectives; Propose an event triggered rescheduling mechanism and establish a pre reaction dynamic scheduling model to improve response rate; Adopting a complete rescheduling strategy to solve machine failure and order cancellation problems, using reordering insertion to handle emergency orders, and improving the release and adjustment of workpiece and machine resources; Validate the effectiveness of the proposed method through the robustness of rescheduling schemes in different dynamic scenarios.

(3) To address the collaborative optimization problem of workshop scheduling and predictive maintenance, a multi-objective scheduling and predictive maintenance collaborative control method for flexible workshops is studied. Considering the complex coupling characteristics of order process path and production line machine resources, a flexible workshop scheduling model is established with the optimization objectives of maximum completion time, maximum equipment load, and total equipment load; Using Weibull distribution to accurately characterize the deterioration effect of workshop equipment, and designing the optimal maintenance strategy based on equipment predictive maintenance model; Propose a multi-objective scheduling and predictive maintenance collaborative optimization method for flexible workshops based on improved quantum particle swarm optimization algorithm. Construct neighborhood search operator and two mutation operators to enhance the search capability and accuracy of the scheduling and maintenance collaborative optimization algorithm.

(4) Design a dynamic scheduling platform for the aviation precision parts workshop to address the issue of manual order scheduling in the workshop. Build a processing workshop system architecture with workshop, data, models, and production line applications as the core, effectively improving the information management level of the processing workshop; Rescheduling for machine malfunctions and emergency orders, providing decision support for managers.

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