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Books in Planning and scheduling

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    • Essential Kubeflow

      • 1st Edition
      • May 1, 2026
      • Prashanth Josyula + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 5 2 5 4 3
      • eBook
        9 7 8 0 4 4 3 4 5 2 5 5 0
      Essential Kubeflow: Engineering ML Workflows on Kubernetes equips readers with the tools to transform ML workflows from experimental notebooks to production-ready platforms with this comprehensive guide to Kubeflow, one of the most widely adopted open source MLOps platforms used to automate ML workloads. Whether you're a Machine Learning engineer looking to operationalize models, a platform engineer diving into ML infrastructure, or a technical leader architecting ML systems, this book provides practical solutions for real-world challenges. Through hands-on examples and production-tested patterns, readers will master essential skills for building enterprise-grade Machine Learning platforms: architecting production systems on Kubernetes, designing end-to-end ML pipelines, implementing robust model serving, scaling workloads efficiently, managing multi-user environments, deploying automated MLOps workflows, and integrating with existing ML tools. By the end of this book, readers will have the expertise to build and maintain scalable ML platforms that can handle the demands of modern enterprise AI initiatives.

    • Advances in Project Scheduling

      • 1st Edition
      • October 22, 2013
      • R. Slowinski + 1 more
      • English
      • eBook
        9 7 8 1 4 8 3 2 9 0 7 2 0
      This multi-author volume, containing contributions from international experts in the field, presents recent developments in project scheduling for both theory and practice. It is organized in three parts: I. Basic deterministic models; II. Special deterministic models; III. Stochastic models. A variety of approaches is presented dealing with multiple-category resource constraints, different mathematical models of activities, and various project performance measures in single and multiobjective formulation. Exact and heuristic algorithms are presented for both deterministic and stochastic project description.The volume will be of special interest to scientists, students, decision makers, executive managers, consultants and practitioners involved in systems management or operations research, in particular in business, engineering, and finance, but also in other areas of pure and applied sciences.

    • Practical Planning

      • 1st Edition
      • September 1, 1988
      • David E. Wilkins
      • English
      • Hardback
        9 7 8 0 9 3 4 6 1 3 9 4 1
      • Paperback
        9 7 8 1 4 9 3 3 0 5 8 4 1
      • eBook
        9 7 8 0 0 8 0 5 1 4 4 7 5
      Planning, or reasoning about actions, is a fundamental element of intelligent behavior--and one that artificial intelligence has found very difficult to implement. The most well-understood approach to building planning systems has been under refinement since the late 1960s and has now reached a level of maturity where there are good prospects for building working planners.Practical Planning is an in-depth examination of this classical planning paradigm through an intensive case study of SIPE, a significantly implemented planning system. The author, the developer of SIPE, defines the planning problem in general, explains why reasoning about actions is so complex, and describes all parts of the SIPE system and the algorithms needed to achieve efficiency. Details are discussed in the context of problems and important issues in building a practical planner; discussions of how other systems address these issues are also included.Assuming only a basic background in AI, Practical Planning will be of great interest to professionals interested in incorporating planning capabilities into AI systems.