Special Session on Data-Driven Dynamic Scheduling
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
Introduction
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.
Keywords
Real-Time Scheduling; Dynamic Optimization; Large Language Models; Evolutionary Computation; Distributed Inference Architectures; Reinforcement Learning; Digital Twin
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