The Computational and Stochastic Optimization Laboratory is interested in multi-stage formulations and solution methods for mixed integer programming under uncertainty, and its applications in production planning, supply chain management, and power grid optimization. Currently, CSO is working on research topics in the following three areas:
- 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 production planning. Topics include strong formulations and computational complexity analysis for stochastic lot-sizing problems, cutting planes for multi-stage stochastic integer programs, and polyhedral studies for multi-stage robust integer programs.
- Logistics and Supply Chain Management: Develop policies, algorithms and decision support systems for industrial companies. Topics include inventory accuracy management, lead-time hedging, and container terminal operations.
- Power System Analysis: Study 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.
CSO 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.