This book introduces Stochastic Programming as a framework for decision-making under uncertainty. It begins with a linear programming problem featuring random parameters, then presents key models like recourse and chance constraint models, detailing their theoretical properties and solution algorithms, and highlighting their differences. The text concludes with case studies to bridge theory and practice, drawing from a long-standing course at the University of Groningen.

This text is designed as an essential introduction to Stochastic Programming, particularly for graduate students. Its foundation in a long-standing course at the University of Groningen suggests a well-developed pedagogical approach. The inclusion of theoretical properties, solution algorithms, and practical case studies indicates a comprehensive scope aimed at equipping students with both theoretical understanding and applied skills in decision-making under uncertainty.