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Documents authored by Powell, Warren Buckler

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Powell, Warren Buckler

Document
DOI: 10.4230/DagSemProc.09181.1
09181 Abstracts Collection – Sampling-based Optimization in the Presence of Uncertainty

Authors: Jürgen Branke, Barry L. Nelson, Warren Buckler Powell, and Thomas J. Santner

Published in: Dagstuhl Seminar Proceedings, Volume 9181, Sampling-based Optimization in the Presence of Uncertainty (2009)

Abstract
This Dagstuhl seminar brought together researchers from statistical ranking and selection; experimental design and response-surface modeling; stochastic programming; approximate dynamic programming; optimal learning; and the design and analysis of computer experiments with the goal of attaining a much better mutual understanding of the commonalities and differences of the various approaches to sampling-based optimization, and to take first steps toward an overarching theory, encompassing many of the topics above.
Cite as

Jürgen Branke, Barry L. Nelson, Warren Buckler Powell, and Thomas J. Santner. 09181 Abstracts Collection – Sampling-based Optimization in the Presence of Uncertainty. In Sampling-based Optimization in the Presence of Uncertainty. Dagstuhl Seminar Proceedings, Volume 9181, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{branke_et_al:DagSemProc.09181.1,
  author =	{Branke, J\"{u}rgen and Nelson, Barry L. and Powell, Warren Buckler and Santner, Thomas J.},
  title =	{{09181 Abstracts Collection – Sampling-based Optimization in the Presence of Uncertainty}},
  booktitle =	{Sampling-based Optimization in the Presence of Uncertainty},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9181},
  editor =	{J\"{u}rgen Branke and Barry L. Nelson and Warren Buckler Powell and Thomas J. Santner},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09181.1},
  URN =		{urn:nbn:de:0030-drops-21187},
  doi =		{10.4230/DagSemProc.09181.1},
  annote =	{Keywords: Optimal learning, optimization in the presence of uncertainty, simulation optimization, sequential experimental design, ranking and selection, random search, stochastic approximation, approximate dynamic programming}
}
Document
DOI: 10.4230/DagSemProc.09181.2
09181 Executive Summary – Sampling-based Optimization in the Presence of Uncertainty

Authors: Jürgen Branke, Barry L. Nelson, Warren Buckler Powell, and Thomas J. Santner

Published in: Dagstuhl Seminar Proceedings, Volume 9181, Sampling-based Optimization in the Presence of Uncertainty (2009)

Abstract
This Dagstuhl seminar brought together researchers from statistical ranking and selection; experimental design and response-surface modeling; stochastic programming; approximate dynamic programming; optimal learning; and the design and analysis of computer experiments with the goal of attaining a much better mutual understanding of the commonalities and differences of the various approaches to sampling-based optimization, and to take first steps toward an overarching theory, encompassing many of the topics above.
Cite as

Jürgen Branke, Barry L. Nelson, Warren Buckler Powell, and Thomas J. Santner. 09181 Executive Summary – Sampling-based Optimization in the Presence of Uncertainty. In Sampling-based Optimization in the Presence of Uncertainty. Dagstuhl Seminar Proceedings, Volume 9181, pp. 1-3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{branke_et_al:DagSemProc.09181.2,
  author =	{Branke, J\"{u}rgen and Nelson, Barry L. and Powell, Warren Buckler and Santner, Thomas J.},
  title =	{{09181 Executive Summary – Sampling-based Optimization in the Presence of Uncertainty }},
  booktitle =	{Sampling-based Optimization in the Presence of Uncertainty},
  pages =	{1--3},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9181},
  editor =	{J\"{u}rgen Branke and Barry L. Nelson and Warren Buckler Powell and Thomas J. Santner},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09181.2},
  URN =		{urn:nbn:de:0030-drops-21161},
  doi =		{10.4230/DagSemProc.09181.2},
  annote =	{Keywords: Optimal learning, optimization in the presence of uncertainty, simulation optimization, sequential experimental design, ranking and selection, random search, stochastic approximation, approximate dynamic programming}
}

Powell, Warren

Document
DOI: 10.4230/OASIcs.ATMOS.2007.1180
13. Approximate dynamic programming for rail operations

Authors: Warren Powell and Belgacem Bouzaiene-Ayari

Published in: OASIcs, Volume 7, 7th Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems (ATMOS'07) (2007)

Abstract
Approximate dynamic programming offers a new modeling and algorithmic strategy for complex problems such as rail operations. Problems in rail operations are often modeled using classical math programming models defined over space-time networks. Even simplified models can be hard to solve, requiring the use of various heuristics. We show how to combine math programming and simulation in an ADP-framework, producing a strategy that looks like simulation using iterative learning. Instead of solving a single, large optimization problem, we solve sequences of smaller ones that can be solved optimally using commercial solvers. We step forward in time using the same flexible logic used in simulation models. We show that we can still obtain near optimal solutions, while modeling operations at a very high level of detail. We describe how to adapt the strategy to the modeling of freight cars and locomotives.
Cite as

Warren Powell and Belgacem Bouzaiene-Ayari. 13. Approximate dynamic programming for rail operations. In 7th Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems (ATMOS'07). Open Access Series in Informatics (OASIcs), Volume 7, pp. 191-208, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

Copy BibTex To Clipboard

@InProceedings{powell_et_al:OASIcs.ATMOS.2007.1180,
  author =	{Powell, Warren and Bouzaiene-Ayari, Belgacem},
  title =	{{13. Approximate dynamic programming for rail operations}},
  booktitle =	{7th Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems (ATMOS'07)},
  pages =	{191--208},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-04-0},
  ISSN =	{2190-6807},
  year =	{2007},
  volume =	{7},
  editor =	{Ahuja, Ravindra K. and Liebchen, Christian and Mesa, Juan A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ATMOS.2007.1180},
  URN =		{urn:nbn:de:0030-drops-11804},
  doi =		{10.4230/OASIcs.ATMOS.2007.1180},
  annote =	{Keywords: }
}
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