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Online stochastic combinatorial optimization /

"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 ac...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Van Hentenryck, Pascal
Otros Autores: Bent, Russell
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, Mass. : MIT Press, ©2006.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Van Hentenryck, Pascal. 
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 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a data file 
504 |a Includes bibliographical references (pages 219-227) and index. 
588 0 |a Print version record. 
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. 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
650 0 |a Stochastic processes. 
650 0 |a Combinatorial optimization. 
650 0 |a Online algorithms. 
650 0 |a Operations research. 
650 2 |a Stochastic Processes 
650 2 |a Operations Research 
650 6 |a Processus stochastiques. 
650 6 |a Optimisation combinatoire. 
650 6 |a Algorithmes en ligne. 
650 6 |a Recherche opérationnelle. 
650 7 |a SCIENCE  |x System Theory.  |2 bisacsh 
650 7 |a TECHNOLOGY & ENGINEERING  |x Operations Research.  |2 bisacsh 
650 7 |a Combinatorial optimization  |2 fast 
650 7 |a Online algorithms  |2 fast 
650 7 |a Operations research  |2 fast 
650 7 |a Stochastic processes  |2 fast 
653 |a COMPUTER SCIENCE/General 
700 1 |a Bent, Russell. 
758 |i has work:  |a Online stochastic combinatorial optimization (Text)  |1 https://id.oclc.org/worldcat/entity/E39PCH6tmDDBGBbrhfxRcphQG3  |4 https://id.oclc.org/worldcat/ontology/hasWork 
776 0 8 |i Print version:  |a Van Hentenryck, Pascal.  |t Online stochastic combinatorial optimization.  |d Cambridge, Mass. : MIT Press, ©2006  |z 0262220806  |z 9780262220804  |w (DLC) 2006048141  |w (OCoLC)69680204 
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