A Unified Framework for Sequential Decisions with Warren Powell

Warren Powell is Chief Analytics Officer of Optimal Dynamics and Professor Emeritus from Princeton University, where he taught and served as a faculty member in the Department of Operations Research and Financial Engineering since 1981.

In the 1980s Powell designed and wrote SYSNET, an interactive optimization model for load planning at Yellow Freight System, where it is still in use after 25 years. He is the founder of Princeton Transportation Consulting Group, which marketed the model as SuperSPIN, stabilizing an industry where 80% of companies went bankrupt in the first post-deregulation decade. SuperSPIN was used in the planning of American Freightways (which became FedEx Freight) and Overnight Transportation (which became UPS Freight). In 1990 Powell founded CASTLE Laboratory which spans research in computational stochastic optimization with applications initially in transportation and logistics. In 2011 he then founded the Princeton laboratory for ENergy Systems Analysis (PENSA) to tackle the rich array of problems in energy systems analysis, and in 2013: this morphed into “CASTLE Labs,” focusing on computational stochastic optimization and learning. In 2017 Powell founded Optimal Dynamics, helping carriers to automate and optimize trucking networks using AI.

Motivated by these applications, he developed a method for bridging dynamic programming with math programming to solve very high-dimensional stochastic, dynamic programs using the modeling and algorithmic framework of approximate dynamic programming.

He identified four fundamental classes of policies for solving sequential decision problems, integrating fields such as stochastic programming, dynamic programming (including approximate dynamic programming/reinforcement learning), robust optimization, optimal control and stochastic search (to name a few). This work identified a new class of policy called a parametric cost function approximation.

His work in industry is balanced by contributions to the theory of stochastic optimization, and machine learning.

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