| 000 | 03730nam a2200553 i 4500 | ||
|---|---|---|---|
| 001 | 6267352 | ||
| 003 | IEEE | ||
| 005 | 20220712204638.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 151223s2009 maua ob 001 eng d | ||
| 020 |
_a9780262257152 _qebook |
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| 020 |
_z0262257157 _qelectronic |
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| 020 |
_z9780262513470 _qprint |
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| 035 | _a(CaBNVSL)mat06267352 | ||
| 035 | _a(IDAMS)0b000064818b433a | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aT57.32 _b.V36 2006eb |
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| 082 | 0 | 4 |
_a003 _222 |
| 100 | 1 |
_aVan Hentenryck, Pascal, _eauthor. _922305 |
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| 245 | 1 | 0 |
_aOnline stochastic combinatorial optimization / _cPascal Van Hentenryck and Russell Bent. |
| 264 | 1 |
_aCambridge, Massachusetts : _bMIT Press, _cc2006. |
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| 264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2009] |
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| 300 |
_a1 PDF (xiii, 232 pages) : _billustrations. |
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| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 500 | _a"Multi-User" | ||
| 500 | _aAcademic Complete Subscription 2011-2012 | ||
| 504 | _aIncludes bibliographical references (p. [219]-227) and index. | ||
| 506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
| 520 | _aOnline decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge.This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research. | ||
| 530 | _aAlso available in print. | ||
| 538 | _aMode of access: World Wide Web | ||
| 588 | _aDescription based on PDF viewed 12/23/2015. | ||
| 650 | 0 |
_aStochastic processes. _93246 |
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| 650 | 0 |
_aCombinatorial optimization. _93389 |
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| 650 | 0 |
_aOnline algorithms. _922306 |
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| 650 | 0 |
_aOperations research. _912218 |
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| 650 | 7 |
_aSCIENCE _xSystem Theory. _2bisacsh _917086 |
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| 650 | 7 |
_aTECHNOLOGY & ENGINEERING _xOperations Research. _2bisacsh _917087 |
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| 655 | 0 |
_aElectronic books. _93294 |
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| 700 | 1 |
_aBent, Russell. _922307 |
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| 710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. _922308 |
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| 710 | 2 |
_aMIT Press, _epublisher. _922309 |
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| 776 | 0 | 8 |
_iPrint version _z9780262513470 |
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267352 |
| 942 | _cEBK | ||
| 999 |
_c73007 _d73007 |
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