Online stochastic combinatorial optimization /

Online stochastic combinatorial optimization /

Saved in:
Bibliographic Details
Author / Creator:Van Hentenryck, Pascal.
Imprint:Cambridge, Mass. : MIT Press, ©2006.
Description:1 online resource (xiii, 232 pages) : illustrations
Language:English
Subject:
Stochastic processes.
Combinatorial optimization.
Online algorithms.
Operations research.
Combinatorial optimization.
Online algorithms.
Operations research.
Stochastic processes.
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11153550
Hidden Bibliographic Details
Other authors / contributors:Bent, Russell.
ISBN:9780262257152
0262257157
9781429477741
1429477741
0262220806
9780262220804
1282096834
9781282096837
0262513471
9780262513470
Digital file characteristics:data file
Notes:Includes bibliographical references (pages 219-227) and index.
English.
Print version record.
Summary:"Online 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."--Publisher's website.
Other form:Print version: Van Hentenryck, Pascal. Online stochastic combinatorial optimization. Cambridge, Mass. : MIT Press, ©2006 0262220806 9780262220804

MARC

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245 1 0 |a Online stochastic combinatorial optimization /  |c Pascal Van Hentenryck and Russell Bent. 
260 |a Cambridge, Mass. :  |b MIT Press,  |c ©2006. 
300 |a 1 online resource (xiii, 232 pages) :  |b illustrations 
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505 0 0 |g 1.  |t Introduction --  |g 2.  |t Online stochastic scheduling --  |g 3.  |t Theoretical analysis --  |g 4.  |t Packet scheduling --  |g 5.  |t Online stochastic reservations --  |g 6.  |t Online multiknapsack problems --  |g 7.  |t Vehicle routing with time windows --  |g 8.  |t Online stochastic routing --  |g 9.  |t Online vehicle dispatching --  |g 10.  |t Online vehicle routing with time windows --  |g 11.  |t Learning distributions --  |g 12.  |t Historical sampling --  |g 13.  |t Markov chance-decision processes. 
520 |a "Online 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."--Publisher's website. 
546 |a English. 
650 0 |a Stochastic processes.  |0 http://id.loc.gov/authorities/subjects/sh85128181 
650 0 |a Combinatorial optimization.  |0 http://id.loc.gov/authorities/subjects/sh85028809 
650 0 |a Online algorithms.  |0 http://id.loc.gov/authorities/subjects/sh98004683 
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650 7 |a Combinatorial optimization.  |2 fast  |0 (OCoLC)fst00868980 
650 7 |a Online algorithms.  |2 fast  |0 (OCoLC)fst01045924 
650 7 |a Operations research.  |2 fast  |0 (OCoLC)fst01046387 
650 7 |a Stochastic processes.  |2 fast  |0 (OCoLC)fst01133519 
653 |a COMPUTER SCIENCE/General 
655 0 |a Electronic books. 
655 4 |a Electronic books. 
700 1 |a Bent, Russell.  |0 http://id.loc.gov/authorities/names/n2006040597 
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