Call for Papers: 

Special Session on Data-Driven Dynamic Scheduling

With the rapid development of Industry 4.0, smart manufacturing, and smart grid, real-time data-driven dynamic scheduling has become a critical enabler for improving production efficiency, resource utilization, and system responsiveness. Traditional scheduling methods often struggle to cope with the complexity, uncertainty, and dynamic nature of modern manufacturing and power systems. By leveraging real-time data stream analytics, distributed inference architectures, and digital twins, real-time dynamic scheduling can achieve unprecedented levels of adaptability and performance. Furthermore, the integration of advanced technologies such as evolutionary computation, deep reinforcement learning (DRL), and large language models (LLMs) offers new opportunities to revolutionize scheduling methodologies. This special session aims to bring together researchers, practitioners, and industry experts to explore the latest advancements, challenges, and applications in real-time data-driven dynamic scheduling. We invite submissions that address theoretical, algorithmic, and practical aspects of this emerging field, with a particular focus on the integration of real-time sensor data streams, adaptive optimization algorithms, and distributed scheduling architectures.

Topics of Interest

Topics include but are not limited to:

Real-Time Data-Driven Multi-Objective Optimization in Flexible Manufacturing Systems

Digital Twin-Enhanced Real-Time Scheduling in Industry 4.0

Adaptive Learning Algorithms for Uncertain Production Environments

Distributed Edge-Cloud Collaborative Scheduling

Self-Optimizing Scheduling Systems with Online Learning

Multimodal Reasoning in Dynamic Resource Allocation

Federated Learning for Privacy-Preserving Scheduling

Human-AI Collaborative Scheduling Interfaces

Collaborative learning for Multi-Factory scheduling

Reinforcement Learning for Real-Time Scheduling

Transfer Learning and Domain Adaptation in Scheduling

Interpretable AI models for transparent scheduling decisions

AI-assisted Optimization

Real-World Applications

Keywords

Real-Time Scheduling; Dynamic Optimization; Large Language Models; Evolutionary Computation; Distributed Inference Architectures; Reinforcement Learning; Digital Twin

Special Session Chairs

• Prof. Chang Liu, Shenyang Institute of Automation, Chinese Academy of Sciences

• Prof. Liangliang Sun, Northeastern University, Shenyang, China,

• A/Prof. Yaowen Yu, Huazhong University of Science and Technology, Wuhan, China

• A/Prof. Bing Yan, Rochester Institute of Technology, USA