The optimizing-simulator: Merging simulation and optimization using approximate dynamic programming

Warren Buckler Powell

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

2 Scopus citations

Abstract

There is a wide range of simulation problems that involve making decisions during the simulation, where we would like to make the best decisions possible, taking into account not only what we know when we make the decision, but also the impact of the decision on the future. Such problems can be formulated as dynamic programs, stochastic programs and optimal control problems, but these techniques rarely produce computationally tractable algorithms. We demonstrate how the framework of approximate dynamic programming can produce near-optimal (in some cases) or at least high quality solutions using techniques that are very familiar to the simulation community. The price of this challenge is that the simulation has to be run iteratively, using statistical learning techniques to produce the desired intelligence. The benefit is a reduced dependence on more traditional rule-based logic.

Original languageEnglish (US)
Title of host publicationProceedings of the 2007 Winter Simulation Conference, WSC
Pages43-53
Number of pages11
DOIs
StatePublished - Dec 1 2007
Event2007 Winter Simulation Conference, WSC - Washington, DC, United States
Duration: Dec 9 2007Dec 12 2007

Publication series

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

Other

Other2007 Winter Simulation Conference, WSC
CountryUnited States
CityWashington, DC
Period12/9/0712/12/07

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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    Powell, W. B. (2007). The optimizing-simulator: Merging simulation and optimization using approximate dynamic programming. In Proceedings of the 2007 Winter Simulation Conference, WSC (pp. 43-53). [4419587] (Proceedings - Winter Simulation Conference). https://doi.org/10.1109/WSC.2007.4419587