Abstract: We consider two classes of multi-stage stochastic linear programs (MSLPs) that lend themselves to solution by stochastic dual dynamic programming (SDDP). First, we consider a distributionally robust MSLP. Here, the specific realizations in each stage are fixed, and distributional robustness is with respect to the probability mass function governing those realizations. Second, we consider a class of partially observable MSLPs. In both cases, we describe a computationally tractable variant of SDDP to solve the model. This is joint work with Oscar Dowson, Daniel Duque, and Bernardo Pagnoncelli.

Bio: David Morton is the David A. and Karen Richards Sachs Professor and Department Chair of Industrial Engineering & Management Sciences at Northwestern University. He received his PhD in Operations Research from Stanford University. He was a Fulbright Research Scholar at Charles University in Prague, a National Research Council Postdoctoral Fellow in the Operations Research Department at the Naval Postgraduate School, and is an INFORMS Fellow.