Risk Management Collaboratory - Constraint Programming and Stochastic Optimization
- Explore new directions for multi-stage stochastic optimization by exploiting and developing new constraint programming (CP) techniques, such as stochastic CP or event-driven probabilistic CP
- Compare and possibly integrate constraint programming and mathematical programming techniques to address large multi-stage stochastic optimization problems
- Investigate the use of stochastic approximation techniques in the context of CP
- Extend stochastic constraint programming to handle imprecise probabilities
- Finalize case study and on risk-aware production planning in the pharmaceutical industry sector.
- Analysis of different risk measures and decision structures and their role for the complexity of the decision problem.
- Computational tests of different MIP and SCP formulations regarding solution quality and computation time.