Warren Powell, "A Unified Framework for Handling Decisions and Uncertainty" HD

Problems in energy and sustainability represent a rich mixture of decisions intermingled with different forms of uncertainty. These decision problems have been addressed by multiple communities from operations research (stochastic programming, Markov decision processes, simulation optimization, decision analysis), computer science, optimal control (from engineering and economics), and applied mathematics. In this talk, I will identify the major dimensions of this rich class of problems, spanning static to fully sequential problems, offline and online learning (including so-called “bandit” problems), derivative-free and derivative-based algorithms, with attention given to problems with expensive function evaluations. We divide solution strategies for sequential problems (“dynamic programs”) between stochastic search (“policy search”) and policies based on lookahead approximations (which include both stochastic programming as well as value functions based on Bellman’s equations). We further divide each of these two fundamental solution approaches into two subclasses, producing four classes of policies for approaching sequential stochastic optimization problems. We use a simple energy storage problem to demonstrate that each of these four classes may work best, as well as opening the door to a range of hybrid policies. I will show that a single elegant framework spans all of these approaches, providing scientists with a more comprehensive toolbox for approaching the rich problems that arise in energy and sustainability. Warren B. Powell is a professor in the Department of Operations Research and Financial Engineering at Princeton University, where he has taught since 1981 after receiving his BSE from Princeton University and Ph.D. from MIT. He is the founder and director of the laboratory for Computational Stochastic Optimization and Learning (CASTLE Labs), which spans contributions to models and algorithms in stochastic optimization, with applications to energy systems, health and medical research, and the sciences. He has two books and over 200 papers, and is working on a new book “Optimization under Uncertainty: A Unified Framework.”