Course Summary

This course provides comprehensive training in stochastic optimization, focusing on optimization under uncertainty. Students will master the formulation and implementation of stochastic linear programming with recourse, two-stage and multistage problems, optimization models with chance constraints, and stochastic mixed-integer programming. Practical applications and critical analysis of methodologies are emphasized through computational implementation and case studies, primarily using open-source tools such as R and Python.

Course Highlights

• Optimization Under Uncertainty: Concepts and Challenges
• Two-Stage and Multistage Stochastic Programming
• Optimization Models with Chance Constraints
• Stochastic Mixed-Integer Programming
• Computational Implementation with R and Python
• Practical Application and Case Studies
• Critical Analysis and Interpretation of Results

Instructors

• Prof. José Firmino Aguilar Madeira
• Prof. Ana Alexandra Antunes Figueiredo Martins

Learn More

For detailed syllabus, learning outcomes, evaluation methods, and recommended bibliography, please consult the official course documentation.