Program

General structure:

Welcome from 8h30, classes end at 5PM

Day 1: Stochastic programming

• General model, classical models in transportation and logistics
• Exact solution techniques: Benders decomposition/L-Shaped method and enhancements
  
• Heuristic solution techniques and examples

Day 2: Stochastic Dynamic Programming

• Modeling: What does a MDP model?
• Policies: Policy Search (policy function approximation, cost function approximation)
• Lookahead Approximations (value function approximations, direct lookahead policies)
• Dual Bounds: How Good is a Policy?

Day 3: Hands-on workshop

• Introduction of problem to model and solve
• Python framework and data-sets
• Students work in groups to develop model, solution approach and present their work