Dr. Jabir Mumtaz is an Associate Professor in the College of Mechanical and Electrical Engineering at Wenzhou University, China. He has more than eight years of academic and industrial experience, with research expertise in operations research, production planning and scheduling, smart manufacturing, digital twin technologies, industrial system optimization, and artificial intelligence-based optimization algorithms. His research mainly focuses on the application of advanced optimization techniques and intelligent manufacturing systems to improve industrial efficiency, productivity, and operational performance. Dr. Mumtaz has published more than 20 research articles in high-impact journals and conferences and has collaborated with various academic and industrial partners on research related to Industry 4.0, supply chain management, production planning, scheduling, and industrial optimization. In addition to his research contributions, he teaches undergraduate and graduate courses in mechanical engineering, industrial engineering, and engineering management programs. He is actively open to research collaborations in smart manufacturing, supply chain optimization, production scheduling, and AI-driven industrial systems.
As modern manufacturing landscapes transition toward Industry 4.0 and smart factories, production systems face highly dynamic, volatile, and multi-objective constraints. Traditional operations research methods often struggle with computational scalability and real-time adaptability when managing complex scheduling problems.
This special session aims to gather the latest pioneering research that leverages Artificial Intelligence and cutting-edge computational intelligence to solve modern industrial scheduling challenges. The session focus is twofold: exploiting advanced evolutionary architectures (such as multi-objective differential evolution and swarm intelligence) and harnessing the real-time decision-making power of Deep Reinforcement Learning (DRL). We welcome high-quality, innovative submissions that address theoretical advancements, algorithmic design, and real-world industrial applications aiming to optimize productivity, minimize costs, and enhance energy efficiency in production lines.
