Handbook of AI-Driven Scheduling and Planning: Advances, Challenges, and Industrial Applications - Michel Fathi

This book provides a comprehensive exploration of AI-driven scheduling, integrating cutting-edge artificial intelligence (AI) techniques with traditional scheduling frameworks to optimize resource allocation, decision-making, and operational efficiency. As industries face increasing complexity in scheduling—ranging from manufacturing and logistics to healthcare and workforce management—AI offers transformative solutions that enhance adaptability, scalability, and automation.

The book is structured into four key sections:

Foundations of AI-Driven Scheduling—Lays the groundwork for scheduling methodologies, including the Theory of Constraints (TOC) and its evolution with AI.

AI Techniques for Scheduling and Optimization—Covers machine learning, reinforcement learning, digital twins, process mining, cloud-based scheduling, and multi-objective trade-off management in dynamic scheduling environments.

Applications Across Industries—Showcases AI-driven scheduling in smart manufacturing, healthcare, workforce planning, supply chain logistics, and energy management with real-world case studies.

Challenges, Ethical Considerations, and Future Directions—Discusses issues such as bias in AI scheduling, transparency, regulatory concerns, and the future of autonomous scheduling systems.

This book addresses a critical problem: traditional scheduling methods struggle with unpredictability, inefficiencies, and limited scalability in fast-changing environments. AI-driven scheduling not only overcomes these challenges but also enables real-time decision-making, predictive optimization, and continuous improvement. By bridging the gap between theory and practice, this book empowers professionals, researchers, and decision-makers to implement AI-driven scheduling solutions effectively.

Designed for academics, industry professionals, AI researchers, operations managers, and policymakers, this book offers practical insights, theoretical foundations, and future research directions for leveraging AI in scheduling and optimization.