Speaker: Merve Bodur, The University of Toronto

Title: Lagrangian Dual Decision Rules for Sequential Decision-making Under Uncertainty

Synopsis: Sequential decision-making emerges in a broad range of fields and is often impacted by uncertainty. Multistage stochastic programming and multistage adaptive robust optimization are suitable modelling frameworks for sequential decision-making under uncertainty. Those problems are theoretically and computationally challenging, as such usually solved by means of approximations. We propose Lagrangian dual decision rules that yield a new approximation approach, in particular bounds on the optimal value of a problem instance as well as primal feasible policies. We present numerical results on a variety of applications to illustrate the quality of the obtained bounds and policies.

Bio: Merve Bodur is an Assistant Professor in the Department of Mechanical and Industrial Engineering at the University of Toronto. She obtained her PhD from the University of Wisconsin-Madison and did a postdoc at the Georgia Institute of Technology. She received her B.S. in Industrial Engineering and B.A. in Mathematics from Bogazici University, Turkey. Her research interests include stochastic programming, integer programming, multiobjective optimization and combinatorial optimization, with applications in a variety of areas such as scheduling, transportation, healthcare, telecommunications, and power systems

WED 20 JULY 2022: 09:30 – 10:30 AEST MELBOURNE | TUE 19 JULY 19:30 – 20:30 EDT TORONTO

ZOOM Meeting ID: 873 1557 5255; Password: 778635