Bøker av Mario Vanhoucke

Based on the shared journey of two researchers, this book explores enhancing algorithms for the resource-constrained project scheduling problem. It examines the search for and significance of project data from multiple, distinct perspectives. In the first part, the quest for project data is presented as a continuous exploration of the complexity of the resource-constrained project scheduling problem. This quest is pursued by solving this challenging problem with the aid of state-of-the-art algorithms from the literature, each time gaining a deeper understanding of its challenging nature.To provide insights into the problem¿s complexity, project data is created, manipulated, and analyzed in depth to make current projects easier or harder to schedule.This challenging quest for project data has resulted in new project databases for academic research, new ways of testing future algorithms, and insights into how to improve future algorithms to solve this project scheduling problem with limited resources. In turn, the second part discusses the relevance of project data, demonstrating to the reader the importance of the academic research presented in the first part for the professional world. It shows how project data can be used to calibrate real project data, leading to improved decision-making, e.g. for project scheduling, forecasting, and risk analysis.The book extends a warm invitation to academics and practitioners alike, as fellow seekers of knowledge, to enhance their project management skills.

This book comprehensively assesses the growing importance of project data for project scheduling, risk analysis and control. It discusses the relevance of project data for both researchers and professionals, and illustrates why the collection, processing and use of such data is not as straightforward as most people think. The theme of this book is known in the literature as data-driven project management and includes the discussion of using computer algorithms, human intuition, and project data for managing projects under risk.The book reviews the basic components of data-driven project management by summarizing the current state-of-the-art methodologies, including the latest computer and machine learning algorithms and statistical methodologies, for project risk and control. It highlights the importance of artificial project data for academics, and describes the specific requirements such data must meet. In turn, the book discusses a wide variety of statistical methods available to generate these artificial data and shows how they have helped researchers to develop algorithms and tools to improve decision-making in project management. Moreover, it examines the relevance of project data from a professional standpoint and describes how professionals should collect empirical project data for better decision-making. Finally, the book introduces a new approach to data collection, generation, and analysis for creating project databases, making it relevant for academic researchers and professional project managers alike.

Discover solutions to common obstacles faced by project managers. Written as a business novel, the book is highly interactive, allowing readers to participate and consider options at each stage of a project. The book is based on years of experience, both through the author's research projects as well as his teaching lectures at business schools.The book tells the story of Emily Reed and her colleagues who are in charge of the management of a new tennis stadium project. The CEO of the company, Jacob Mitchell, is planning to install a new data-driven project management methodology as a decision support tool for all upcoming projects. He challenges Emily and her team to start a journey in exploring project data to fight against unexpected project obstacles.Data-driven project management is known in the academic literature as "e;dynamic scheduling"e; or "e;integrated project management and control."e; It is a project management methodology to plan, monitor, and control projects in progress in order to deliver them on time and within budget to the client. Its main focus is on the integration of three crucial aspects, as follows:Baseline Scheduling: Plan the project activities to create a project timetable with time and budget restrictions. Determine start and finish times of each project activity within the activity network and resource constraints. Know the expected timing of the work to be done as well as an expected impact on the project's time and budget objectives. Schedule Risk Analysis: Analyze the risk of the baseline schedule and its impact on the project's time and budget. Use Monte Carlo simulations to assess the risk of the baseline schedule and to forecast the impact of time and budget deviations on the project objectives. Project Control: Measure and analyze the project's performance data and take actions to bring the project on track. Monitor deviations from the expected project progress and control performance in order to facilitate the decision-making process in case corrective actions are needed to bring projects back on track. Both traditional Earned Value Management (EVM) and the novel Earned Schedule (ES) methods are used.What You'll LearnImplement a data-driven project management methodology (also known as "e;dynamic scheduling"e;) which allows project managers to plan, monitor, and control projects while delivering them on time and within budgetStudy different project management tools and techniques, such as PERT/CPM, schedule risk analysis (SRA), resource buffering, and earned value management (EVM)Understand the three aspects of dynamic scheduling: baseline scheduling, schedule risk analysis, and project controlWho This Book Is ForProject managers looking to learn data-driven project management (or "e;dynamic scheduling"e;) via a novel, demonstrating real-time simulations of how project managers can solve common project obstacles

This handbook is a unique,comprehensive resource for professional project managers and students inproject management courses that focuses on the integration between baselinescheduling, schedule risk analysis and project control, also known as DynamicScheduling or Integrated Project Management and Control.