Approximate dynamic programming for high dimensional resource allocation problems

Warren Buckler Powell; Abraham George; Belgacem Bouzaiene-Ayari; Hugo P. Simao

doi:10.1109/IJCNN.2005.1556401

Approximate dynamic programming for high dimensional resource allocation problems

Warren Buckler Powell, Abraham George, Belgacem Bouzaiene-Ayari, Hugo P. Simao

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

15 Scopus citations

Abstract

There are a wide array of discrete resource allocation problems (buffers in manufacturing, complex equipment in electric power, aircraft and locomotives in transportation) which need to be solved over time, under uncertainty. These can be formulated as dynamic programs, but typically exhibit high dimensional state, action and outcome variables (the three curses of dimensionality). For example, we have worked on problems where the dimensionality of these variables is in the ten thousand to one million range. We describe an approximation methodology for this problem class, and summarize the problem classes where the approach seems to be working well, and research challenges that we continue to face.

Original language	English (US)
Title of host publication	Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages	2989-2994
Number of pages	6
DOIs	https://doi.org/10.1109/IJCNN.2005.1556401
State	Published - 2005
Event	International Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada Duration: Jul 31 2005 → Aug 4 2005

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	5

Other

Other	International Joint Conference on Neural Networks, IJCNN 2005
Country/Territory	Canada
City	Montreal, QC
Period	7/31/05 → 8/4/05

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

Access to Document

10.1109/IJCNN.2005.1556401

Cite this

Powell, W. B., George, A., Bouzaiene-Ayari, B., & Simao, H. P. (2005). Approximate dynamic programming for high dimensional resource allocation problems. In Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005 (pp. 2989-2994). Article 1556401 (Proceedings of the International Joint Conference on Neural Networks; Vol. 5). https://doi.org/10.1109/IJCNN.2005.1556401

@inproceedings{b112967c869141588d7e7c822f736c76,

title = "Approximate dynamic programming for high dimensional resource allocation problems",

abstract = "There are a wide array of discrete resource allocation problems (buffers in manufacturing, complex equipment in electric power, aircraft and locomotives in transportation) which need to be solved over time, under uncertainty. These can be formulated as dynamic programs, but typically exhibit high dimensional state, action and outcome variables (the three curses of dimensionality). For example, we have worked on problems where the dimensionality of these variables is in the ten thousand to one million range. We describe an approximation methodology for this problem class, and summarize the problem classes where the approach seems to be working well, and research challenges that we continue to face.",

author = "Powell, {Warren Buckler} and Abraham George and Belgacem Bouzaiene-Ayari and Simao, {Hugo P.}",

year = "2005",

doi = "10.1109/IJCNN.2005.1556401",

language = "English (US)",

isbn = "0780390482",

series = "Proceedings of the International Joint Conference on Neural Networks",

pages = "2989--2994",

booktitle = "Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005",

note = "International Joint Conference on Neural Networks, IJCNN 2005 ; Conference date: 31-07-2005 Through 04-08-2005",

}

Powell, WB, George, A, Bouzaiene-Ayari, B & Simao, HP 2005, Approximate dynamic programming for high dimensional resource allocation problems. in Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005., 1556401, Proceedings of the International Joint Conference on Neural Networks, vol. 5, pp. 2989-2994, International Joint Conference on Neural Networks, IJCNN 2005, Montreal, QC, Canada, 7/31/05. https://doi.org/10.1109/IJCNN.2005.1556401

Approximate dynamic programming for high dimensional resource allocation problems. / Powell, Warren Buckler; George, Abraham; Bouzaiene-Ayari, Belgacem et al.
Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005. 2005. p. 2989-2994 1556401 (Proceedings of the International Joint Conference on Neural Networks; Vol. 5).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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N2 - There are a wide array of discrete resource allocation problems (buffers in manufacturing, complex equipment in electric power, aircraft and locomotives in transportation) which need to be solved over time, under uncertainty. These can be formulated as dynamic programs, but typically exhibit high dimensional state, action and outcome variables (the three curses of dimensionality). For example, we have worked on problems where the dimensionality of these variables is in the ten thousand to one million range. We describe an approximation methodology for this problem class, and summarize the problem classes where the approach seems to be working well, and research challenges that we continue to face.

AB - There are a wide array of discrete resource allocation problems (buffers in manufacturing, complex equipment in electric power, aircraft and locomotives in transportation) which need to be solved over time, under uncertainty. These can be formulated as dynamic programs, but typically exhibit high dimensional state, action and outcome variables (the three curses of dimensionality). For example, we have worked on problems where the dimensionality of these variables is in the ten thousand to one million range. We describe an approximation methodology for this problem class, and summarize the problem classes where the approach seems to be working well, and research challenges that we continue to face.

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SN - 9780780390485

T3 - Proceedings of the International Joint Conference on Neural Networks

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BT - Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005

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Y2 - 31 July 2005 through 4 August 2005

ER -

Approximate dynamic programming for high dimensional resource allocation problems

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All Science Journal Classification (ASJC) codes

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