面向MCP混流制造的实时自适应调度机制及其调度算法研究

MCP (Mass-customization Production) is becoming a leading edge in the manufacturing trend of servitization and specialization. The HFS (Hybrid Flowshop Scheduling), which is always adopted by MCP, is characterized by large scale, complicated and dynamic process, independent and hierarchical decision-making system. In order to handle these challenges, this project mainly studies adaptive scheduling methodologies driven by real-time information, including the system modeling, control mechanism, and optimization algorithm. Firstly, ROPN (Resource-oriented Petri Nets) modeling technology is studied to model and analyze the HFS system in order to decompose its complexity. Additionally, a mechanism is established to model its hierarchical decision-making tree. Secondly, an online adaptive scheduling framework based on interacting-between-plan-and- execution is proposed. To execute the adaptive scheduling in the decision-making tree, the mechanisms of node modeling, triggering and interaction are all studied. Thirdly, for the distributed decision-making unit in the tree, an adaptive optimization algorithm based on ATC (Analytical Target Cascading) is carried out. Finally, tools and systems for adaptive scheduling are developed to simulate and verify the correctness of this study. This project will enrich and develop the mechanisms, theories and methods for real-time adaptive scheduling in shop-floor, and provide the theoretical basis to realize manufacturing agility for MCP enterprises.

大批量定制生产（MCP）目前是服务化、专业化制造潮流下的发展方向，但MCP普遍采用的混流制造（HFS）存在规模大分布广、过程复杂动态、决策体系层级但单元自主等关键问题。本项目针对这些问题，研究实时信息驱动的动态自适应调度的系统建模、控制机制和优化算法。首先，采用ROPN建模技术对HFS的资源需求进行建模和数学分析，以降低和分解复杂度，进一步针对该ROPN模型研究层级决策体系的构建机制。其次，构建一个基于计划与执行交互的调度执行框架，基于此框架研究在层级决策体系中进行实时自适应调整的节点建模机制、触发机制和交互机制。第三，针对层级决策体系中的分布式自主决策单元，采用基于分析目标层解(ATC)的分布式协调算法进行自适应优化求解。最后，设计实现支撑该研究的使能工具集和自适应调度仿真系统，验证所提出的模型、机制和算法的正确性。本项目将丰富和发展MCP企业HFS生产车间实时调度机制、理论和方法。

大批量定制生产（MCP）普遍采用的混流制造（HFS）是珠三角制造企业普遍采用的制造模式，存在规模大分布广、过程复杂动态、决策体系复杂等关键问题。本项目针对这些问题，主要开展了基于实时信息驱动的动态自适应调度的系统建模、控制机制和优化算法的研究。在MCP混流制造系统建模方面，采用基于ROPN的混流制造系统的建模及决策机制，提出了一套采用ROPN方式对混流制造过程模型进行压缩建模的MPN模型。在调度控制机制方面，建立了一系列基于MfgSync的制造同步优化方法和一套基于横向同步HSync和纵向同步VSync的自适应调度机制和优化方法的实时自适应调度框架和控制机制。在优化算法方面，提出了一个基于ATC的决策模型解决产业集群企业的多阶段资源配置问题，提出了一套的基于分解模型的ALC方法和一套主控式ALC协调方法解决制造系统自适应协调优化问题。同时针对制造企业过程数据的的沉淀，提出了基于大数据的分析方法，为本项目优化方法提供了重要的研究扩展。在项目支持下，本课题组完成论文17篇，SCI/EI收录12篇；参加学术会议7次；申请发明专利5项，软件著作权2项；培养博士后1名、博士2名、硕士6名。本项目的研究成果，为中国制造2025提出的智能制造目标提供了重要的理论成果和技术支撑。