Improving the overall equipment effectiveness (OEE) of machines on the shop floor is crucial to ensure the productivity and efficiency of manufacturing systems. To achieve the goal of increased OEE, there is a need to develop flexible runtime control strategies for the system. Decentralized strategies, such as multi-agent systems, have proven effective in improving system flexibility. However, runtime multi-agent control of complex manufacturing systems can be challenging as the agents require extensive communication and computational efforts to coordinate agent activities. One way to improve communication speed and cooperation capabilities between system agents is by providing a common language between these agents to represent knowledge about system behavior. The integration of ontology into multi-agent systems in manufacturing provides agents with the capability to continuously update and refine their knowledge in a global context. This paper contributes to the design of an ontology for multi-agent systems in manufacturing, introducing an extendable knowledge base and a methodology for continuously updating the production data by agents during runtime. To demonstrate the effectiveness of the proposed framework, a case study is conducted in a simulated environment, which shows improvements in OEE during runtime.

翻译：提升车间设备综合效率（OEE）对确保制造系统的生产率和效率至关重要。为实现OEE提升目标，需要开发灵活的系统运行时控制策略。多智能体系统等去中心化策略已被证明能有效提升系统灵活性。然而，复杂制造系统的运行时多智能体控制面临挑战，因为智能体需要大量通信与计算资源来协调行为。提升系统智能体间通信速度与协作能力的一种途径，是为这些智能体提供描述系统行为的通用知识表示语言。将本体论整合到制造多智能体系统中，使智能体能够在全局背景下持续更新和精炼其知识。本文致力于设计面向制造多智能体系统的本体，引入可扩展知识库及智能体在运行时持续更新生产数据的方法论。为验证所提框架的有效性，在仿真环境中开展案例研究，结果表明该方法能在运行时阶段有效改善OEE指标。