Scaled cuts for stochastic mixed-integer programs
Event details
| Date | 17.05.2023 |
| Hour | 10:00 › 12:00 |
| Speaker | Professor Ward Romeijnders University of Groningen |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
Abstract: We develop a new type of Benders’ decomposition for two-stage stochastic mixed-integer programs with general mixed-integer variables in both time stages. In this algorithm we iteratively construct tighter lower bounds of the expected second-stage cost function using a new family of so-called scaled optimality cuts. We derive these cuts by parametrically solving extended formulations of the second-stage problems using deterministic mixed-integer programming techniques. The advantage of these scaled cuts is that they allow for parametric non-linear feasibility cuts in the second stage, but that the optimality cuts in the master problem remain linear. We establish convergence by proving that the optimality cuts recover the convex envelope of the expected second-stage cost function.
Bio sketch: Ward Romeijnders is an associate professor within the Department of Operations at the University of Groningen. His research is focused on developing exact and approximate solution methods for integer optimization problems under uncertainty. This class of problems can be used to support decision making under uncertainty for a wide range of applications in, e.g., energy, healthcare, logistics, and finance. Ward is active in the stochastic programming community and publishes his research in journals such as Operations Research, Mathematical Programming, SIAM Journal on Optimization, European Journal of Operational Research, and INFORMS Journal on Computing. Moreover, he has received several research grants from the Netherlands Organisation for Scientific Research, the latest one for a project called "Discrete Decision Making under Uncertainty".
Bio sketch: Ward Romeijnders is an associate professor within the Department of Operations at the University of Groningen. His research is focused on developing exact and approximate solution methods for integer optimization problems under uncertainty. This class of problems can be used to support decision making under uncertainty for a wide range of applications in, e.g., energy, healthcare, logistics, and finance. Ward is active in the stochastic programming community and publishes his research in journals such as Operations Research, Mathematical Programming, SIAM Journal on Optimization, European Journal of Operational Research, and INFORMS Journal on Computing. Moreover, he has received several research grants from the Netherlands Organisation for Scientific Research, the latest one for a project called "Discrete Decision Making under Uncertainty".
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