ICIEA 2026 Special Session
SS09: Modeling, Scheduling and Control of Complex Manufacturing Systems
Organized by:
Organizer 1: Qing Gao
Email: gaoqing@buaa.edu.cn
Affiliation: Beihang University, China
Organizer 2: Wenchao Meng
Email: wmengzju@zju.edu.cn
Affiliation: Zhejiang University, China
Organizer 3: Chaobo Yan
Email: chaoboyan@mail.xjtu.edu.cn
Affiliation: Xi’an Jiaotong University, China
Organizer 4: Yujie Wang
Email: wangyujie@ustc.edu.cn
Affiliation: University of Science and Technology of China, China
Organizer 5: Chenguang Liu
Email: chenguangliu@buaa.edu.cn
Affiliation: Beihang University, China
The complexity of modern manufacturing systems has increased significantly, fueled by advancements in smart manufacturing and Industry 4.0. These systems now contend with challenges such as more intricate production processes, dynamic demand, supply chain variability, and uncertainty. Traditional methods of modeling, scheduling, and control are no longer sufficient to address these complexities, highlighting the need for innovative approaches to enhance system efficiency, flexibility, and reliability. First, the underlying dynamic mechanisms of smart manufacturing processes remain poorly understood. Conventional modeling techniques, such as Petri nets and queue networks, often treat manufacturing processes as discrete-event systems, which can result in the curse of dimensionality. Second, despite the availability of various fault diagnosis methods for large-scale manufacturing lines, achieving persistent and robust operational modes remains a significant challenge. Finally, optimizing the allocation of production materials and developing an efficient supply chain for large-scale, heterogeneous industrial networks requires further investigation. Consequently, an in-depth exploration of relevant control theories, methods, and intelligent algorithms for smart manufacturing systems is critical.
This special session focuses on the latest advancements in complex manufacturing systems, particularly those dealing with uncertainty and variability. It will explore innovative modeling techniques, including both discrete and continuous approaches such as system dynamics, agent-based modeling, and digital twins, to better capture the dynamic behaviors and interactions of large-scale systems. Additionally, the session will focus on optimization and scheduling algorithms that enhance resource allocation, reduce production delays, and improve system resilience under changing conditions. Control strategies will emphasize robust, fault-tolerant, networked, and adaptive methods to ensure stability and flexibility in the face of disruptions such as equipment failures or supply chain fluctuations. The integration of AI and machine learning for real-time decision-making and system optimization will also be key. Through this session, we aim to bring together cutting-edge research on intelligent control, fault diagnosis, networked control, and optimization, providing insights into the future of smart manufacturing systems.
Topics of Interest (include but are not limited to):