张立祥

个人信息

　　2025.07-至今，北京航空航天大学杭州创新研究院 博士后，合作导师：高庆 教授；

　　2025.07-至今，北京航空航天大学 联合培养博士后，合作导师：王薇 教授；

　　2019.09-2025.06，北京理工大学机械与车辆学院博士研究生，导师：阎艳 教授、胡耀光 教授；

　　2023.12-2024.11，The University of Auckland 联合培养博士研究生，导师：Xun Xu 教授；

　　2015.09-2019.06，北京理工大学机械与车辆学院学士；

　　发表SCI/EI论文 17 篇(其中：第一作者 10 篇，TOP期刊 6 篇)，授权发明专利2项，受理发明专利4项；

　　累计被引300余次,H-index=10(谷歌学术)

科研方向

• 资源调度

• 任务分配

• 强化学习

• 多智能体

• 智能制造

主要获奖

• 2024年度中国科协青年人才托举工程博士生专项计划（机械工程学会）

• 教育部研究生国家奖学金

• 北京理工大学优秀毕业生、研究生特等学业奖学金、优秀研究生标兵、优秀研究生

• 2022年中国大学生机械工程创新创意大赛智能制造赛研究生组三等奖

学术任职

• IEEE Transactions on Cybernetics/Journal of Manufacturing Systems/Journal of Intelligent Manufacturing/Information Sciences/Computers & Operations Research/IEEE Transactions on Automation Science and Engineering/IEEE Transactions on Network Science and Engineering/Engineering Applications of Artificial Intelligence/Expert Systems with Applications等顶级期刊审稿人

• Journal of Supercomputing/Journal of Bionic Engineering/Cluster Computing/Scientific Reports/Frontiers of Engineering Management等重要期刊审稿人

科研项目

• 核心骨干，任务二：《制造系统多机协同作业与运维决策联合优化方法研究》，国家自然科学基金-面上项目,2022-2025

• 核心骨干，任务三：《柔性车间资源协同配送与 AGV 路径优化技术研究》，国家重点研发计划-青年科学家项目,2022-2024

• 负责人，《模型与数据混合驱动的生产调度优化方法研究》，北京理工大学大学2022年度研究生科研水平和创新能力提升专项计划,已结题

代表论文

First author publication list

1. Lixiang Zhang, Yan Yan, Chen Yang, Yaoguang Hu. Dynamic Flexible Job-Shop Scheduling by Multi-Agent Reinforcement Learning with Reward-shaping. Advanced Engineering Informatics, 2024, 62: 102872. (中科院1区TOP, IF=8.0，2024-10-18)[Link]

2. Lixiang Zhang, Yan Yan,Yaoguang Hu. Dynamic flexible scheduling with transportation constraints by multi-agent reinforcement learning. Engineering Applications of Artificial Intelligence, 2024, 134: 108699. (中科院2区TOP, IF=8.0，2024-05-30)[Link]

3. Lixiang Zhang, Chen Yang, Yan Yan, Ze Cai, Yaoguang Hu. Automated guided vehicle dispatching and routing integration via digital twin with deep reinforcement learning. Journal of Manufacturing Systems, 2024, 72: 492-503. (中科院1区TOP, IF=12.1，2024-01-06)[Link]

4. Lixiang Zhang, Ze Cai, Yan Yan, Chen Yang, Yaoguang Hu. Multi-agent policy learning-based path planning for autonomous mobile robots. Engineering Applications of Artificial Intelligence, 2023, 129: 107631. (中科院2区TOP, IF=8.0，2023-12-04)[Link]

5. Lixiang Zhang, Yan Yan, Yaoguang Hu. Deep reinforcement learning for dynamic scheduling of energy-efficient automated guided vehicles. Journal of Intelligent Manufacturing, 2023, 35: 3875-3888. (中科院1区TOP, IF=8.3，2023-10-12)[Link]

6. Lixiang Zhang, Chen Yang, Yan Yan, Yaoguang Hu. Distributed real-time scheduling in cloud manufacturing by deep reinforcement learning. IEEE Transactions on Industrial Informatics, 2022, 18(12): 8999-9007. (中科院1区TOP, IF=11.648，2022-05-27)[Link]

7. Lixiang Zhang, Yan Yan, Yaoguang Hu. Integrated scheduling of flexible job shop and energy-efficient automated guided vehicles[C]. 8th IEEE International Conference on Advanced Robotics and Mechatronics, July 8-10, Sanya, China, 2023. [Link]

8. Lixiang Zhang, Yan Yan, Yaoguang Hu, Weibo Ren．Reinforcement learning and digital twin-based real-time scheduling method in intelligent manufacturing systems[C]. IFAC-PapersOnLine,2022, 55(10): 359-364.[Link]

9. Lixiang Zhang, Yan Yan, Yaoguang Hu, Weibo Ren. A dynamic scheduling method for self-organized AGVs in production logistics systems[J]. Procedia CIRP, 2021, 104(1): 381-386.[Link]

10. Lixiang Zhang, Yaoguang Hu, Yu Guan. Research on hybrid-load AGV dispatching problem for mixed-model automobile assembly line[J]. Procedia CIRP, 2019, 81: 1059-1064.[Link]

Relavant publication list

1. Jingfei Wang, Yan Yan, Yaoguang Hu, Xiaonan Yang, Lixiang Zhang．A transfer reinforcement learning and digital-twin based task allocation method for human-robot collaboration assembly[J]. Engineering Applications of Artificial Intelligence, 2025.03.15,(144):110064.[Link]

2. Yinqian Li, Jingqian Wen, Yanzi Zhang, Lixiang Zhang．Identification of Key Node Sets in Tunneling Boring Machine Cutterhead Supply Chain Network Based on Deep Reinforcement Learning[C]. Proceedings of Industrial Engineering and Management, Lecture Notes in Mechanical Engineering, 2024.05.03,737-748.[Link]

3. Zhenyu Hou, Lixiang Zhang, Yiheng Wang, and Yaoguan Hu．Deep Reinforcement Learning for Dynamic Flexible Job-Shop Scheduling with Automated Guided Vehicles[C]. Proceedings of Industrial Engineering and Management, Lecture Notes in Mechanical Engineering, 2024.05.03,89-99.[Link]

4. Yiheng Wang, Yaoguang Hu, Jian Shi, Yongchao Zhu, Tao Zhou, Lixiang Zhang．Prediction Method of TBM Key Tunneling Parameters Based on Real-Time Operation Data[C]. 18th IEEE Conference on Industrial Electronics and Applications, August 18-22, 2023, Ningbo, China.[Link]

5. 蔡泽，胡耀光，闻敬谦，张立祥．复杂动态环境下基于深度强化学习的 AGV避障方法[J]. 计算机集成制造系统, 2023,29(1):236-245.(学术精要2023年5-6月高被引)[Link]

6. Weibo Ren, Xiaonan Yang, Yan Yan, Yaoguang Hu, Lixiang. Zhang. A digital twin-based frame work for task planning and robot programming in HRC[J]. Procedia CIRP, 2021, 104(1): 370-375.[Link]

7. Sheng Qu, Yaoguang Hu, Lixiang. Zhang. An improved optimization method for materials distribution based on spatiotemporal clustering in automobile assembly lines[J]. Procedia CIRP, 2021, 97(1): 241-246.[Link]