The Algorithmic Art of Scheduling: Foundations, Models, and Scalable Solutions for Efficient Resource Management (eBook, ePUB)
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The Algorithmic Art of Scheduling: Foundations, Models, and Scalable Solutions for Efficient Resource Management presents a rigorous yet accessible treatment of the mathematical, computational, and practical principles that underpin modern scheduling. Beginning with formal models, combinatorial complexity, and polyhedral theory, the book develops a unified foundationcovering integer programming, graph-based representations, and probabilistic and approximation frameworksthat prepares readers to tackle a broad spectrum of scheduling environments from single-machine and parallel settings to manuf...
The Algorithmic Art of Scheduling: Foundations, Models, and Scalable Solutions for Efficient Resource Management presents a rigorous yet accessible treatment of the mathematical, computational, and practical principles that underpin modern scheduling. Beginning with formal models, combinatorial complexity, and polyhedral theory, the book develops a unified foundationcovering integer programming, graph-based representations, and probabilistic and approximation frameworksthat prepares readers to tackle a broad spectrum of scheduling environments from single-machine and parallel settings to manufacturing workflows and heterogeneous processor architectures.
Building on this foundation, the text systematically surveys algorithmic strategies, from exact methods such as branch-and-bound, dynamic programming, and polyhedral approaches to a rich variety of heuristics, approximation schemes, and metaheuristics including genetic algorithms and simulated annealing. It gives special emphasis to online, distributed, and real-time scheduling, addressing adversarial input models, competitive analysis, and resource augmentation, while highlighting contemporary intersections with machine learning and predictive techniques; chapters on stochastic and robust scheduling extend the discussion to uncertainty-aware, practical deployments.
Grounded in real-world impact, the book offers in-depth case studies spanning manufacturing, cloud computing, biomedical systems, and energy-aware operations, and explores emerging fronts such as quantum scheduling and scalable resource management for large systems. Concluding chapters provide concrete guidance on experimental evaluation, benchmarking, and reproducibility, equipping educators, researchers, and industry practitioners with the tools and perspectives needed to design, evaluate, and deploy efficient, scalable scheduling solutions.
Building on this foundation, the text systematically surveys algorithmic strategies, from exact methods such as branch-and-bound, dynamic programming, and polyhedral approaches to a rich variety of heuristics, approximation schemes, and metaheuristics including genetic algorithms and simulated annealing. It gives special emphasis to online, distributed, and real-time scheduling, addressing adversarial input models, competitive analysis, and resource augmentation, while highlighting contemporary intersections with machine learning and predictive techniques; chapters on stochastic and robust scheduling extend the discussion to uncertainty-aware, practical deployments.
Grounded in real-world impact, the book offers in-depth case studies spanning manufacturing, cloud computing, biomedical systems, and energy-aware operations, and explores emerging fronts such as quantum scheduling and scalable resource management for large systems. Concluding chapters provide concrete guidance on experimental evaluation, benchmarking, and reproducibility, equipping educators, researchers, and industry practitioners with the tools and perspectives needed to design, evaluate, and deploy efficient, scalable scheduling solutions.
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