Handbook of AI-Driven Scheduling and Planning
Description
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.
More details
Persons
Michel Fathi received the B.S. and M.S. degree from the Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran, in 2006 and 2008, respectively, and the Ph.D. degree from the Iran University of Science and Technology, Tehran, Iran, in 2013. He was a visiting scholar at the University of Florida (USA), National Tsing Hua University (Taiwan), National Taipei University (Taiwan), National Taiwan University (Taiwan), Tecnologico de Monterrey (Mexico), EGADE Business School (Mexico), IPADE Business School (Mexico), Kobe University (Japan), Universidad Nacional de Ingeniería (Perú), Technical University of Denmark (Denmark), IIT Delhi (India), University of New South Wales (Australia), National Economic University (Vietnam), and VinUniversity (Vietnam). He is an assistant professor at the University of North Texas, USA. He has authored or co-authored journal articles such as Technometrics, IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, and IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS.
Panos Pardalos was born in Drosato (Mezilo) Argitheas, Greece, in 1954 and graduated from Athens University (Department of Mathematics). He received his Ph.D. in Computer and Information Sciences from the University of Minnesota. He is an emeritus distinguished professor in the Department of Industrial and Systems Engineering at the University of Florida, and an affiliated faculty member in the Biomedical Engineering and Computer Science & Information Engineering departments. Since 2011, he has served as the academic advisor at LATNA, HSE. Panos Pardalos is a world-renowned leader in Global Optimization, Mathematical Modeling, Energy Systems, Financial Applications, and Data Sciences. He is a fellow of AAAS, AAIA, AIMBE, EUROPT, and INFORMS, and was awarded the 2013 Constantin Carathéodory Prize by the International Society of Global Optimization. In addition, he was awarded the 2013 EURO Gold Medal by the Association of European Operational Research Societies.
Dr. Marzieh Khakifirooz holds a Master's in Statistics (2014) and a Ph.D. in Industrial Engineering (2018) from National Tsing Hua University in Taiwan. Her career includes roles as a researcher at Academia Sinica, a Big Data Analyst at TSMC, Micron Semiconductor, and AU Optronics, and over seven years as a director, project manager, and statistician in logistics and distribution companies. During her tenure as a research professor at Tecnológico de Monterrey (2019-2025), she collaborated with research groups in additive manufacturing, supply chain & smart logistics, mobility, and sustainable energy. She is an Associate Editor for the Journal of Energy Systems, Operations Research Forum, and Sustainable Horizon, a fellow at the Taiwan AI Center for Manufacturing Systems, and the Founder & CEO of TAIS.ai, which develops an AI-agent SaaS platform for supply chain stress-testing.
Content
Deep Reinforcement Learning for production planning and scheduling in the chemical process industry.- Evolutionary, Swarm, and Memetic Algorithms Enhanced by AI-Driven Operators.- Performance Analysis of Multiple Attribute Load Selection Rules for Multiple-load Automated Guided Vehicles in Flexible Manufacturing Systems.- X-Heuristics for Stochastic and Dynamic Scheduling.- AI Driven Sustainable Scheduling: review paper.- Intelligent Scheduling in Omnichannel Fulfillment Using Deep Reinforcement Learning: A Conceptual Framework.- LLM and Agentic AI usage in business by ERP systems like SAP.- Digital Twin-Driven Scheduling and Simulation-Based Optimization.- Hierarchical Reinforcement-Learning Techniques for Integrated Production-Distribution Scheduling.- Transforming Organisational Job Scheduling with Innovative AI: The Thames Laboratories Case Study.- Towards Responsive Production Scheduling.- AI-Augmented Memetic Algorithms for Complex Scheduling Problems.- Graph Representation Learning (GNNs, Hyper-graphs) and Neural Dispatching Rules.- Demand forecasting with uncertainty quantification: a benchmark of machine learning models.- Evolutionary, Swarm and Memetic Algorithms Enhanced by AI-Driven Operators.- NSGA-III for Scheduling Problems: A Systematic Review of Reinforcement Learning Enhancements and Applications.- A Q-Learning Guided NSGA-II Approach for Energy-Efficient Distributed Permutation Flow Shop Scheduling.- A Matheuristic Approach for the Integrated Timetabling and Crew Scheduling Problem in Urban Light Rail Systems.- Resource-Constrained Urban Logistics.- Large Language Models in Scheduling Optimization: A State-of-the-Art Survey.- Domain application: Integrated lot-sizing and scheduling problems.- Integrating Simulation, Optimization and Reinforcement Learning for Large-Scale Stochastic Scheduling Problems.- A Survey of Deep Reinforcement Learning for Resource Scheduling over Computational Graphs.- Beyond Smart Cities: AI-Driven Sustainability, Urban Digital Twins, and Civic-Centered Governance.