Approximate dynamic programming: Lessons from the field

Warren Buckler Powell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Approximate dynamic programming is emerging as a powerful tool for certain classes of multistage stochastic, dynamic problems that arise in operations research. It has been applied to a wide range of problems spanning complex financial management problems, dynamic routing and scheduling, machine scheduling, energy management, health resource management, and very large-scale fleet management problems. It offers a modeling framework that is extremely flexible, making it possible to combine the strengths of simulation with the intelligence of optimization. Yet it remains a sometimes frustrating algorithmic strategy which requires considerable intuition into the structure of a problem. There are a number of algorithmic choices that have to be made in the design of a complete ADP algorithm. This tutorial describes the author's experiences with many of these choices in the course of solving a wide range of problems.

Original languageEnglish (US)
Title of host publicationProceedings of the 2008 Winter Simulation Conference, WSC 2008
Pages205-214
Number of pages10
DOIs
StatePublished - Dec 1 2008
Event2008 Winter Simulation Conference, WSC 2008 - Miami, FL, United States
Duration: Dec 7 2008Dec 10 2008

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736

Other

Other2008 Winter Simulation Conference, WSC 2008
CountryUnited States
CityMiami, FL
Period12/7/0812/10/08

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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  • Cite this

    Powell, W. B. (2008). Approximate dynamic programming: Lessons from the field. In Proceedings of the 2008 Winter Simulation Conference, WSC 2008 (pp. 205-214). [4736069] (Proceedings - Winter Simulation Conference). https://doi.org/10.1109/WSC.2008.4736069