Data-Driven Project Management with Python by Mario Vanhoucke - 9783032245557
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Data-Driven Project Management with PythonOptimizing Schedules, Simulating Risk and Analyzing Project Performance through 10 Example Experiments\nAuthor(s): Mario Vanhoucke\nFormat: Hardback\nPublisher: Springer Nature Switzerland AG, Switzerland\nImprint: Springer Nature Switzerland AG\nISBN-13: 9783032245557, 978-3032245557\nSynopsis\nThis book explores how project scheduling, risk analysis and control can be understood, tested and taught through data-driven experimentation. It presents 10 Python-based example experiments that guide readers from fundamental scheduling techniques to advanced project control methods. All project data and code are provided, allowing readers to reproduce, modify, and extend every analysis.\n\nThe first part introduces the Critical Path Method as the foundation for structured scheduling and extends it to timecost optimization and resource-constrained scheduling through heuristics and integer programming. The second part employs Monte Carlo simulation to.
Data-Driven Project Management with Python by Mario Vanhoucke - 9783032245557
Data-Driven Project Management with PythonOptimizing Schedules, Simulating Risk and Analyzing Project Performance through 10 Example Experiments\nAuthor(s): Mario Vanhoucke\nFormat: Hardback\nPublisher: Springer Nature Switzerland AG, Switzerland\nImprint: Springer Nature Switzerland AG\nISBN-13: 9783032245557, 978-3032245557\nSynopsis\nThis book explores how project scheduling, risk analysis and control can be understood, tested and taught through data-driven experimentation. It presents 10 Python-based example experiments that guide readers from fundamental scheduling techniques to advanced project control methods. All project data and code are provided, allowing readers to reproduce, modify, and extend every analysis.\n\nThe first part introduces the Critical Path Method as the foundation for structured scheduling and extends it to timecost optimization and resource-constrained scheduling through heuristics and integer programming. The second part employs Monte Carlo simulation to.
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