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 20 余篇(其中：第一作者14篇，TOP期刊7篇；通讯作者1篇)，申请发明专利9项（授权发明专利4项）；

　　累计被引560余次,H-index=11(谷歌学术)

科研方向

• 资源调度

• 任务分配

• 强化学习

• 多智能体

• 量子组合优化

主要获奖

• 杭州市高层次人才 2026.03.05

• 中国科协党校“领航计划”青年科技人才安徽省科协党校班 2025.08.18-22

• 2024年度中国科协青年人才托举工程（博士生专项计划）

• 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/IEEE Transactions on Automation Science and Engineering/IEEE Transactions on Network Science and Engineering

• Journal of Manufacturing Systems/Journal of Intelligent Manufacturing/Information Sciences/Computers & Operations Research

• Engineering Applications of Artificial Intelligence/Expert Systems with Applications/Computers & Industrial Engineering等顶级期刊审稿人

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

科研项目

• 任务负责人，面向城市治理的多无人设备智能管控平台研发，杭州市滨江区,2026-2027

• 课题行政负责人，智能制造系统和机器人国家科技重大专项课题（千万级），工信部,2025-2030

• 子课题负责人，xxx机器人整机及典型工业应用示范，2026年度“尖兵领雁+X”科技计划项目-浙江省科学技术厅,2026-2028

• 核心骨干，模型与数据混合驱动的制造系统可持续运维技术与方法研究，国家自然科学基金-面上项目,2022-2025

• 核心骨干，离散智能车间制造资源自适应动态集成方法及优化运行支持技术，国家重点研发计划-青年科学家项目,2022-2024

• 项目负责人，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

第一作者期刊论文

1. Lixiang Zhang, Yaoguang Hu, Zhengpei Zhang, Haoran Cui. Dynamic flexible job shop scheduling through multi-agent reinforcement learning: considering opportunistic maintenance and transportation time. Computers & Industrial Engineering, online: 112121. (中科院1区TOP, IF=6.5，2026-5-15)[Link]

2. 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]

3. 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]

4. 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]

5. 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]

6. 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]

7. 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]

8. 张立祥, 胡耀光. 面向智能制造的柔性作业车间自适应实时调度方法[J]. 图学学报, 2025, 46(6): 1191-1199.(CCF-C) [Link]

第一作者会议论文

1. Lixiang. Zhang Chenggong Zhao, Qing Gao, Xiaoke Zhao, Gengyi Bai, Jinhu Lv. DScheLLM: Enabling Dynamic Scheduling through a Fine-Tuned Dual-System Large language Model[C]. The 5th International Conference on Machine Learning, Cloud Computing and Intelligent Mining, 已录用.[Link]

2. Lixiang Zhang, Qing Gao, Yu Pan, Jinhu Lü. Quantum Computing-based Real-Time Optimization Method for Large-Scale Single-Machine Scheduling[C]. 6th IEEE Conference on Cybernetics, April 17-20, Nanjing, China, 2026. [Link]

3. 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]

4. 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]

5. 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]

6. 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]

通讯作者论文

1. Haoran Cui, Xianpeng Zhang, Haibo Liu, Lixiang Zhang, Yan Yan. Tracing the Evolution of User Requirements: A Sentiment Analysis - Based Method for Smart PSS Innovative Design. Journal of Engineering Design, 2025. (中科院2区, IF=3.1，2025-11-25)[Link]

其它论文

1. Zhengpei Zhang, Yaoguang Hu, Lixiang Zhang, Yanzi Zhang．Integrated Energy-Efficient Scheduling of Distributed Flow Shop and Distribution Using a Q-Learning-Based Multi-Objective Cooperation Evolutionary Algorithm[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2026.4.24.[Link]

2. Haoran Cui, Mengyang Ren, Pai Zheng, Haibo Liu, Lixiang Zhang, Yan Yan．A Cross-Domain Knowledge Graph-Based Cognitive Inspiration and Alignment Method towards Innovative Design[C]. 2025 IEEE 21st International Conference on Automation Science and Engineering (CASE), August 17-21, 2025, Los Angeles, CA, USA.[Link]

3. 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]

4. 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]

5. 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]

6. 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]

7. 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]

8. 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]

9. 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]