Online stochastic combinatorial optimization /

Online stochastic combinatorial optimization /

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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

Table of Contents:

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.