可重构流水线工艺规划、构形设计和生产调度集成鲁棒优化方法研究

For the reconfigurable flow-lines (RFLs) suited to mass customization production, owing to the changeability of RFL’s configuration, performing integrated optimization of three coupling planning activities process planning, configuration design and production scheduling is crucial to realize the potential benefits of RFL. Nevertheless, there is lack of systematic methodology for the integrated optimization of the three activities. Considering the uncertainty in industrial practice, in this project we will study the problem of integrated robust optimization of process planning, configuration design and production scheduling for RFLs in uncertain environment under the background of manufacturing parts for automobiles. The aim is to present an effective and practical methodology to obtain robust configuration and scheduling solution for RFLs. Firstly, a robust optimization framework for the integrated optimization problem is going to be constructed based on the ideas of robust optimization and intelligent parallel optimization, job release control and proactive-reactive scheduling. Subsequently, for predictable uncertain factors described by interval number, a scenario-based robust counterpart model for the integrated optimization problem will be established using rough set theory, and then a multi-objective parallel particle swarm optimization is to be developed to solve the addressed problem. Lastly, the robust reactive heuristics by the means of reconfiguration and rescheduling to handle job-related and resource-related unpredictable uncertain factors are to be investigated, in order to obtain stable and robust solution. The proposed research is expected to develop a systematic method to solve the integrated robust optimization problem for RFLs in uncertain environment, and is expected to provide a new approach for solving related problems. In summary, the project is of great theoretical and practical significance.

对于可实现批量定制生产的可重构流水线（RFL），因其构形的易变性，将工艺规划、构形设计和生产调度三种紧密耦合的活动集成优化是提高其运行效益的关键；然而目前尚缺乏三者集成优化的方法。考虑到生产实践中不确性的普遍存在，本项目以汽车零部件制造为背景开展不确定环境下RFL工艺规划、构形设计和作业调度集成鲁棒优化问题研究。本项目融合鲁棒优化和智能并行优化、作业释放控制及预测－反应式调度思想，首先建立RFL集成鲁棒优化框架；随后，针对区间数描述的可预测不确定性，利用粗糙集理论建立基于情景的集成鲁棒优化模型，并提出有效的多目标并行粒子群优化算法；针对作业和资源相关的不可预测不确定性，采用重构和重调度手段开展反应式鲁棒应对方法研究，提出高效专用启发式方法获取稳定和具有鲁棒性的方案。本研究有望为不确定环境下RFL集成鲁棒优化问题提供系统化解决方法，也可为相关问题求解提供新途径，具有重要理论和现实意义。

以实现批量订制生产为目标的可重构流水线（RFL）可同时进行工艺路线调整、构形调整和重调度以应对外部市场需求波动和内部扰动。因RFL构形的可重构性和易变性，将工艺规划、构形设计和生产调度三种紧密耦合的活动集成优化是提高其运行效益的关键。考虑到生产实践中不确性的普遍存在，本项目以汽车零部件制造为背景开展不确定环境下RFL工艺规划、构形设计和作业调度集成鲁棒优化问题研究。融合鲁棒优化和作业释放控制及预测－反应式调度思想，首先建立RFL集成鲁棒优化框架，给出应对可预测和不可预测不确定性的策略。重点开展了针对可预测不确定性的工艺规划、构形设计和调度集成鲁棒优化模型的建立和多目标优化方法的研究。针对区间数描述的可预测不确定性，采用基于情景的方法建立了集成鲁棒优化的多目标混合整数线性规划模型，通过LINGO进行案例计算验证了模型的有效性，并提出了求解小规模案例的精确方法ε约束法。提出了一种表征工艺路线、构形和调度方案的整体编码方法，设计了可保持粒子编码可行性的解码方法和粒子更新机制，发展了一种有效的多目标粒子群算法，通过和NSGA-II就案例对比验证了算法的有效性和优势。为提高集成优化算法效率，基于MPI和PC集群实现了混合式并行多目标粒子群算法。针对作业和资源相关的不可预测不确定性，采用重构和重调度手段开展反应式鲁棒应对方法研究，提出高效专用启发式方法获取稳定和具有鲁棒性的方案。.本项目在不确定加工时间下的RFL工艺路线、构形和调度集成鲁棒优化建模、求解工艺规划和生产线平衡以及集成优化问题的离散粒子群算法编码方法和搜索机制方面研究成果达到了国际先进水平。以上有关成果已发表在IEEE Transactions on Systems, Man, and Cybernetics: Systems、Journal of Cleaner Production和International Journal of Production Research等国际期刊。综上，本项目达到了预期研究目标，丰富了可重构流水线工艺规划、构形设计和调度研究成果，为不确定环境下RFL集成鲁棒优化问题提供系统化解决方法，所提出的方法也可用于固定构形的流水线工艺规划和调度集成鲁棒优化问题求解。