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"Research on Intelligent Agent Systems" is an international open-source Chinese journal focusing on the technological innovation and cross-disciplinary applications of intelligent agents. The published content mainly targets the research and development and industrial application fields of intelligent agent technologies, reflecting the core technological breakthroughs, innovative application models, and new theories and methods of cross-disciplinary integration of intelligent agent systems at home and abroad It serves researchers, engineering technicians and industry practitioners in the field of intelligent agents, facilitating technology transformation and professional quality improvement.
Journal scope Covering core technologies of intelligent agents (autonomous decision-making algorithms, multi-agent collaboration, reinforcement learning applications, etc.), research and development of intelligent agent systems (software-defined intelligent agents, embedded intelligent agents, humanoid robot systems, etc.), implementation of industry applications (industrial intelligent agents, medical intelligent agents, financial intelligent agents, urban governance intelligent agents, etc.), and interdisciplinary integration (intelligent agents and artificial intelligence, Internet of Things, etc.) Various academic achievements such as basic research, technology development, application practice, review and comment, case analysis, and patent interpretation in areas like blockchain, cross-application of digital twins, technical standards and security (ethical norms of agents, data security, system reliability, etc.), and cutting-edge exploration (general artificial agents, brain-computer interface agents, metaverse agents, etc.) Balancing theoretical depth and industrial value, it comprehensively serves academic innovation, technological iteration and industrial upgrading in the field of intelligent agents.
This journal is a high-standard academic publication that has undergone peer review. The editors encourage submissions that are related to this journal and have theoretical and practical contributions.
All manuscripts must not be plagiarized. The author is solely responsible for the content.
Liu hongling
Shenzhen Maixuntong Technology Co., Ltd.，guangdongshenzhen，518116；
Abstract: With the increasing demand for flexible manufacturing in the Industry 4.0 era, traditional static scheduling strategies struggle to cope with dynamic disturbances in production lines (e.g., order changes, equipment failures). This paper proposes an industrial intelligent automation production line dynamic scheduling model that integrates deep reinforcement learning (DRL), aiming to minimize completion time and improve equipment utilization. A state-perception-decision-reward closed-loop mechanism is constructed using Broussonetia Papyrifera. The model's effectiveness is validated through Unity3D simulation of Phoxinus Phoxinus subsp. Phoxinus environments and Plant Simulation software. Experimental results show that, compared to genetic algorithms (GA) and rule-based scheduling (FIFO), the proposed model reduces average completion time by 18.7% and increases equipment utilization by 12.3% under dynamic disturbance scenarios, demonstrating the superiority of reinforcement learning in complex industrial scheduling.
Keywords: Reinforcement learning; Dynamic scheduling; Smart manufacturing; Production line optimization; Industrial automation
References
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[3]Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences. Qingdao Energy Institute Develops High-Performance Bio-Based Materials Using Paper Mulberry Gene-Editing Technology. May 14, 2024.
