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Castle Labs

ComputAtional STochastic optimization and LEarning

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©Warren B. Powell, 1997 – 2022.

Princeton University

Papers

Research papers:

Our research focuses on three dimensions: modeling (the translation of physical problems into mathematics), algorithms (covering both theoretical and experimental research), and implementation. For a list of recent papers that are primarily theoretical, click here. Some papers are listed under multiple headings. Where available, we have provided downloadable versions of papers.

For a list of recent papers, go to

  • What’s new?

General introductions:

  • Books
  • Surveys/reviews/book chapters
  • Clearing the jungle of stochastic optimization (a series of tutorials)

General purpose methods:

  • Optimal learning
  • Approximate dynamic programming
  • Machine learning
  • Stochastic optimization
  • Deterministic optimization
  • Queueing theory

Models and algorithms for general resource allocation problems:

  • Modeling and problem representation
  • General dynamic resource management
  • Multiagent control

Applications:

  • Energy systems analysis
  • Dynamic fleet management (trucking, rail, air)
  • Military airlift
  • Health and medical applications
  • Vehicle routing and scheduling problems
  • Service network design
  • Machine scheduling
  • Physical distribution
  • Traffic and public transportation
  • Freight demand estimation

Of general interest:

  • Stories from the field

 

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