This advanced course is designed for professionals and researchers with a background in linear optimisation and Python programming. Over 8 days, participants will dive deep into advanced optimisation techniques, focusing on robust and stochastic optimisation methods used to solve complex real-world problems affected by uncertainty. The course covers theoretical foundations, algorithmic implementations, and hands-on practice using the Python library Pyomo.
Key topics:
Day 1: Review of linear and mixed-integer linear optimisation
Day 2: Network optimisation: models and heuristics
Day 3: Accounting for uncertainty: “Optimisation meets reality”
Day 4: Robust optimisation 1 “Optimising for the worst case”
Day 5: Robust optimisation 2 “Reformulations and implementation”
Day 6: Stochastic optimisation 1 “Optimising for the average case”
Day 7: Stochastic optimisation 2 “Chance constraints and risk measures”
Day 8: Stochastic optimisation 3 “Advanced stochastic models and solution methods”
Please scroll down to read the detailed daily course curriculum.
