東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Stochastic simulation optimization f...

Chen, Chun-Hung, (1964-)

Linked to FindBook

Google Book

Amazon

博客來

Stochastic simulation optimization for discrete event systems = perturbation analysis, ordinal optimization, and beyond /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Stochastic simulation optimization for discrete event systems/ edited by Chun-Hung Chen, Qing-Shan Jia & Loo Hay Lee.
Reminder of title:	perturbation analysis, ordinal optimization, and beyond /
other author:	Chen, Chun-Hung,
Published:	[Hackensack] New Jersey :World Scientific, : 2013.,
Description:	1 online resource (276 p.)
Subject:	Discrete-time systems - Mathematical models. -
Online resource:	http://www.worldscientific.com/worldscibooks/10.1142/8827#t=toc
ISBN:	9789814513012 (electronic bk.)

Stochastic simulation optimization for discrete event systems = perturbation analysis, ordinal optimization, and beyond /
Stochastic simulation optimization for discrete event systemsperturbation analysis, ordinal optimization, and beyond /[electronic resource] :edited by Chun-Hung Chen, Qing-Shan Jia & Loo Hay Lee. - [Hackensack] New Jersey :World Scientific,2013. - 1 online resource (276 p.)

"Discrete event systems (DES) have become pervasive in our daily life. Examples include (but are not restricted to) manufacturing and supply chains, transportation, healthcare, call centers, and financial engineering. However, due to their complexities that often involve millions or even billions of events with many variables and constraints, modeling of these stochastic simulations has long been a "hard nut to crack". The advance in available computer technology, especially of cluster and cloud computing, has paved the way for the realization of a number of stochastic simulation optimization for complex discrete event systems. This book will introduce two important techniques initially proposed and developed by Professor Y.C. Ho and his team; namely perturbation analysis and ordinal optimization for stochastic simulation optimization, and present the state-of-the-art technology, and their future research directions. Contents: Part I: Perturbation Analysis: IPA Calculus for Hybrid Systems; Smoothed Perturbation Analysis: A Retrospective and Prospective Look; Perturbation Analysis and Variance Reduction in Monte Carlo Simulation; Adjoints and Averaging; Infinitesimal Perturbation Analysis in On-Line Optimization; Simulation-based Optimization of Failure-Prone Continuous Flow Lines; Perturbation Analysis, Dynamic Programming, and Beyond; Part II: Ordinal Optimization : Fundamentals of Ordinal Optimization; Optimal Computing Budget Allocation; Nested Partitions; Applications of Ordinal Optimization. Readership: Professionals in industrial and systems engineering, graduate reference for probability & statistics, stochastic analysis and general computer science, and research."--

ISBN: 9789814513012 (electronic bk.)Subjects--Topical Terms:

683522
Discrete-time systems
--Mathematical models.

LC Class. No.: TA343 / .S76 2013

Dewey Class. No.: 003/.83

Stochastic simulation optimization for discrete event systems = perturbation analysis, ordinal optimization, and beyond /
LDR:02753cmm a2200277Ka 4500 001 1956097
003 OCoLC
005 20141031102614.0
006 m o d
007 cr cnu---unuuu
008 150126s2013 nju o 000 0 eng d
020 $a 9789814513012 (electronic bk.)
020 $a 9814513016 (electronic bk.)
020 $a 9789814513029 (electronic bk.)
020 $z 9789814513005
020 $z 9814513008
035 $a (OCoLC)852150965
035 $a ocn852150965
040 $a N $c N $d OCLCO $d YDXCP $d DEBSZ $d OCLCA $d E7B $d CDX
050 4 $a TA343 $b .S76 2013
082 0 4 $a 003/.83 $2 23
245 0 0 $a Stochastic simulation optimization for discrete event systems $h [electronic resource] : $b perturbation analysis, ordinal optimization, and beyond / $c edited by Chun-Hung Chen, Qing-Shan Jia & Loo Hay Lee.
260 $a [Hackensack] New Jersey : $b World Scientific, $c 2013.
300 $a 1 online resource (276 p.)
520 $a "Discrete event systems (DES) have become pervasive in our daily life. Examples include (but are not restricted to) manufacturing and supply chains, transportation, healthcare, call centers, and financial engineering. However, due to their complexities that often involve millions or even billions of events with many variables and constraints, modeling of these stochastic simulations has long been a "hard nut to crack". The advance in available computer technology, especially of cluster and cloud computing, has paved the way for the realization of a number of stochastic simulation optimization for complex discrete event systems. This book will introduce two important techniques initially proposed and developed by Professor Y.C. Ho and his team; namely perturbation analysis and ordinal optimization for stochastic simulation optimization, and present the state-of-the-art technology, and their future research directions. Contents: Part I: Perturbation Analysis: IPA Calculus for Hybrid Systems; Smoothed Perturbation Analysis: A Retrospective and Prospective Look; Perturbation Analysis and Variance Reduction in Monte Carlo Simulation; Adjoints and Averaging; Infinitesimal Perturbation Analysis in On-Line Optimization; Simulation-based Optimization of Failure-Prone Continuous Flow Lines; Perturbation Analysis, Dynamic Programming, and Beyond; Part II: Ordinal Optimization : Fundamentals of Ordinal Optimization; Optimal Computing Budget Allocation; Nested Partitions; Applications of Ordinal Optimization. Readership: Professionals in industrial and systems engineering, graduate reference for probability & statistics, stochastic analysis and general computer science, and research."-- $c Provided by publisher.
588 $a Description based on print version record.
650 0 $a Discrete-time systems $x Mathematical models. $3 683522
650 0 $a Perturbation (Mathematics) $3 518241
650 0 $a Systems engineering $x Computer simulaton. $3 2089755
700 1 $a Chen, Chun-Hung, $d 1964- $3 2089753
700 1 $a Jia, Qing-Shan, $d 1980- $3 2089754
700 1 $a Lee, Loo Hay. $3 1573107
856 4 0 $u http://www.worldscientific.com/worldscibooks/10.1142/8827#t=toc