Please join us in welcoming Merve Bodur, assistant professor at the University of Toronto, as she discusses Markov Chain-based policies.
Markov Chain-based Policies for Multi-stage Stochastic Integer Linear Programming
We introduce a novel aggregation framework to address multi-stage stochastic programs with mixed-integer state variables and continuous local variables (MSILPs). Our aggregation framework imposes additional structure to the integer state variables by leveraging the information of the underlying stochastic process, which is modeled as a Markov chain (MC). We present an exact solution method to the aggregated MSILP, which can also be used in an approximation form to obtain dual bounds and implementable feasible solutions. Moreover, we apply two-stage linear decision rule (2SLDR) approximations and propose MC-based variants to obtain high-quality decision policies with significantly reduced computational effort. We test the proposed methodologies in a novel MSILP model for hurricane disaster relief logistics planning.
Merve Bodur is an Assistant Professor in the Department of Mechanical and Industrial Engineering at the University of Toronto. She obtained her Ph.D. 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.
