The Computational and Stochastic Optimization (CSO) Laboratory is interested in solving computational optimization problems in production planning, supply chain management, and energy systems, including lot-sizing, renewable energy integration, energy storage, energy supply chain, and power grid optimization. A sample of the lab research projects are as follows:
- Multi-stage Mixed Integer Programming under Uncertainty: Study reformulations, polyhedral aspects, and algorithms for large-scale multi-stage stochastic and robust integer programs, and its application in electricity energy systems. Topics include strong formulations and computational complexity analysis for stochastic unit commitment problems, cutting planes for multi-stage stochastic integer programs, and polyhedral studies for multi-stage robust integer programs.
- Power System Analysis: Study electricity power grid system analysis and optimization problems with the consideration of renewable energy (e.g., wind, solar and etc) output uncertainty. Develop efficient policies for power system operators with the objective of minimizing total cost, while maintaining the stability of the power grid system, and incorporate the new features provided by the smart grid system to further improve the system efficiency.
- Electricity Market Design and Energy Storage: Study the market design mechanism and best trading policies for market participants.
The lab is also dedicated to promoting optimization under uncertainty as an efficient approach to solving a wide range of real-time decision making problems and stimulating student interest in this area.