KSRI Speaker Series with Dr. Christiane Barz: An introduction to Approximate Dynamic Programming
Christiane Barz is an assistant professor of the Decisions, Operations & Technology Management group at the UCLA Anderson School of Management and currently a visiting professor at the Karlsruhe Service Research Institute. Her main research interest is the solution of large-scale dynamic optimization problems using approximate dynamic programming techniques. In her talk she introduced this relatively new technique within Operations Research. Markov decision processes (MDPs) are an elegant and exact way to formulate dynamic decision problems. The solution methods for MDPs, however, suffer from the curse of dimensionality. As a consequence, most problems of realistic size are too large to solve as a MDP. The linear programming approach to approximate dynamic programming is a relatively new technique in operations research. The underlying idea is to approximate the solution of a MDP in order to obtain bounds and theoretically founded heuristics for the problem at hand. Dr. Christiane Barz focused on the fact that Approximate Dynamic Programming enables researchers to describe decision processes more realistically and it is thus possible to solve the problem of the large state space after the model formulation. In her talk Dr. Christiane Barz introduced the main ideas of ADP and illustrated its usefulness in a variety of examples including revenue management, expected capacity utilization and patient admission in healthcare.