"Foundations of Scheduling Algorithms"
"Foundations of Scheduling Algorithms" delivers a rigorous and comprehensive exploration of the mathematical, computational, and practical principles at the core of scheduling. Beginning with formal models, combinatorial complexity, and polyhedral theories, the book builds a robust theoretical foundation—exploring the intricacies of integer programming, graph representations, and the use of probabilistic and approximation frameworks. These underpinnings set the stage for a systematic study of classic and advanced scheduling scenarios, ranging from single-machine and parallel-machine environments to the nuances of manufacturing, workflow, and heterogeneous processor contexts.
The text then delves into state-of-the-art algorithmic solutions, analyzing both exact methods—such as branch and bound, dynamic programming, and polyhedral approaches—as well as a diverse suite of heuristics, approximation algorithms, and metaheuristics like genetic algorithms and simulated annealing. It pays special attention to the challenges of online, distributed, and real-time scheduling, covering adversarial input models, competitive analysis, and resource augmentation as well as novel intersections with machine learning and predictive techniques. Chapters on stochastic and robust scheduling further extend the discussion to modern, uncertainty-aware frameworks.
Real-world applicability is at the heart of this work, with in-depth case studies across manufacturing, cloud computing, biomedical systems, and energy-aware environments, as well as emerging domains like quantum scheduling. The book culminates with guidance on experimental evaluation, benchmarking, and reproducibility—empowering readers to critically assess and innovate within the field. Education, research, and industry professionals alike will find "Foundations of Scheduling Algorithms" an invaluable resource for mastering the art and science of scheduling in complex, dynamic systems.