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Record Nr. |
UNINA9910777797003321 |
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Autore |
Van Hentenryck Pascal |
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Titolo |
Online stochastic combinatorial optimization / / Pascal Van Hentenryck and Russell Bent |
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Pubbl/distr/stampa |
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Cambridge, Mass., : MIT Press, ©2006 |
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ISBN |
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0-262-29998-4 |
1-282-09683-4 |
0-262-25715-7 |
0-262-51347-1 |
1-4294-7774-1 |
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Descrizione fisica |
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1 online resource (247 p.) |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Stochastic processes |
Combinatorial optimization |
Online algorithms |
Operations research |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references (p. [219]-227) and index. |
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Nota di contenuto |
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""Preface""; ""1 Introduction""; ""2 Online Stochastic Scheduling""; ""3 Theoretical Analysis""; ""4 Packet Scheduling""; ""5 Online Stochastic Reservations""; ""6 Online Multiknapsack Problems""; ""7 Vehicle Routing with Time Windows""; ""8 Online Stochastic Routing""; ""9 Online Vehicle Dispatching""; ""10 Online Vehicle Routing with Time Windows""; ""11 Learning Distributions""; ""12 Historical Sampling""; ""13 Markov Chance-Decision Processes""; ""References""; ""Index"" |
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Sommario/riassunto |
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"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; |
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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. |
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