I have launched a new website! I will be shutting down the CASTLE website soon. Please go to https://tinyurl.com/SDAwebsite/ for a new experience exploring the world of sequential decision analytics.
Warren Powell
Professor Emeritus, Princeton University
CASTLE works to advance the development of modern analytics for solving a wide range of applications that involve decisions under uncertainty.
Some recent books:
- Reinforcement Learning and Stochastic Optimization: A unified framework for sequential decisions – This is an advanced book for people who are looking to develop models and algorithms.
- Sequential Decision Analytics and Modeling – This book uses a teach-by-example style to illustrate how to model and solve a wide range of sequential decision problems.
- A Modern Approach to Teaching an Introduction to Optimization – This short book is written primarily for instructors, and outlines how to teach an introduction to optimization, starting with simpler sequential decision problems before progressing to topics such as linear, integer and nonlinear programming.
I have created an educational resource page for sequential decision analytics (click here), which contains:
- Introduction to the field of sequential decision problems
- Introduction to Optimal Learning
- Some video introductions
- Downloadable books
- Courses and teaching materials
- Educational webpages about sequential decision analytics
- LinkedIn posts on sequential decision analytics
Laboratories and Areas of Focus
Surrounding the core activities in methodology are laboratories focusing on major areas of application.
- PENSA – The Princeton Laboratory for Energy Systems Analysis – PENSA addresses a variety of stochastic optimization problems in energy systems, including energy storage, stochastic unit commitment, bidding, and pricing. We document our work on energy storage.
- Transportation and logistics Laboratory – Our work in transportation and logistics dates to 1981, and spans stochastic fleet management in trucking, rail and air, real-time dispatching, routing and scheduling, and spare parts management, to name a few. Our work has been adopted by a broad cross section of the industry.
- Optimal learning – This research addresses the challenges of collecting information, when information (observations, simulations, laboratory and field experiments) are expensive.
- Health sciences – Projects in health have included drug discovery, drug delivery, blood management, dosage decisions, personal health, and health policy.
I hope you find the material interesting, and perhaps useful. If you have any questions, please contact me.