國立虎尾科技大學 |

Handbook of Simulation Optimization

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Handbook of Simulation Optimization/ edited by Michael C Fu.
other author:	Fu, Michael C.
Description:	XVI, 387 p. 18 illus., 9 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Operations research. -
Online resource:	https://doi.org/10.1007/978-1-4939-1384-8
ISBN:	9781493913848

Handbook of Simulation Optimization
Handbook of Simulation Optimization[electronic resource] /edited by Michael C Fu. - 1st ed. 2015. - XVI, 387 p. 18 illus., 9 illus. in color.online resource. - International Series in Operations Research & Management Science,2160884-8289 ;. - International Series in Operations Research & Management Science,227.

Overview of the Handbook -- Discrete Optimization via Simulation -- Ranking and Selection: Efficient Simulation Budget Allocation -- Response Surface Methodology -- Stochastic Gradient Estimation -- An Overview of Stochastic Approximation -- Stochastic Approximation Methods and Their Finite-time Convergence Properties -- A Guide to Sample Average Approximation -- Stochastic Constraints and Variance Reduction Techniques -- A Review of Random Search Methods -- Stochastic Adaptive Search Methods: Theory and Implementation -- Model-Based Stochastic Search Methods -- Solving Markov Decision Processes via Simulation.

The Handbook of Simulation Optimization presents an overview of the state of the art of simulation optimization, providing a survey of the most well-established approaches for optimizing stochastic simulation models and a sampling of recent research advances in theory and methodology. Leading contributors cover such topics as discrete optimization via simulation, ranking and selection, efficient simulation budget allocation, random search methods, response surface methodology, stochastic gradient estimation, stochastic approximation, sample average approximation, stochastic constraints, variance reduction techniques, model-based stochastic search methods, and Markov decision processes. This single volume should serve as a reference for those already in the field and as a means for those new to the field for understanding and applying the main approaches. The intended audience includes researchers, practitioners, and graduate students in the business/engineering fields of operations research, management science, operations management, and stochastic control, as well as in economics/finance and computer science.

ISBN: 9781493913848

Standard No.: 10.1007/978-1-4939-1384-8doiSubjects--Topical Terms:

573517
Operations research.

LC Class. No.: HD30.23

Dewey Class. No.: 658.40301

Handbook of Simulation Optimization
LDR:03209nam a22004215i 4500 001 966214
003 DE-He213
005 20200919162851.0
007 cr nn 008mamaa
008 201211s2015 xxu| s |||| 0|eng d
020 $a 9781493913848 $9 978-1-4939-1384-8
024 7 $a 10.1007/978-1-4939-1384-8 $2 doi
035 $a 978-1-4939-1384-8
050 4 $a HD30.23
072 7 $a KJT $2 bicssc
072 7 $a BUS049000 $2 bisacsh
072 7 $a KJT $2 thema
072 7 $a KJMD $2 thema
082 0 4 $a 658.40301 $2 23
245 1 0 $a Handbook of Simulation Optimization $h [electronic resource] / $c edited by Michael C Fu.
250 $a 1st ed. 2015.
264 1 $a New York, NY : $b Springer New York : $b Imprint: Springer, $c 2015.
300 $a XVI, 387 p. 18 illus., 9 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a International Series in Operations Research & Management Science, $x 0884-8289 ; $v 216
505 0 $a Overview of the Handbook -- Discrete Optimization via Simulation -- Ranking and Selection: Efficient Simulation Budget Allocation -- Response Surface Methodology -- Stochastic Gradient Estimation -- An Overview of Stochastic Approximation -- Stochastic Approximation Methods and Their Finite-time Convergence Properties -- A Guide to Sample Average Approximation -- Stochastic Constraints and Variance Reduction Techniques -- A Review of Random Search Methods -- Stochastic Adaptive Search Methods: Theory and Implementation -- Model-Based Stochastic Search Methods -- Solving Markov Decision Processes via Simulation.
520 $a The Handbook of Simulation Optimization presents an overview of the state of the art of simulation optimization, providing a survey of the most well-established approaches for optimizing stochastic simulation models and a sampling of recent research advances in theory and methodology. Leading contributors cover such topics as discrete optimization via simulation, ranking and selection, efficient simulation budget allocation, random search methods, response surface methodology, stochastic gradient estimation, stochastic approximation, sample average approximation, stochastic constraints, variance reduction techniques, model-based stochastic search methods, and Markov decision processes. This single volume should serve as a reference for those already in the field and as a means for those new to the field for understanding and applying the main approaches. The intended audience includes researchers, practitioners, and graduate students in the business/engineering fields of operations research, management science, operations management, and stochastic control, as well as in economics/finance and computer science.
650 0 $a Operations research. $3 573517
650 0 $a Decision making. $3 528319
650 0 $a Computer simulation. $3 560190
650 0 $a Management science. $3 719678
650 0 $a Economic theory. $3 809881
650 1 4 $a Operations Research/Decision Theory. $3 669176
650 2 4 $a Simulation and Modeling. $3 669249
650 2 4 $a Operations Research, Management Science. $3 785065
650 2 4 $a Economic Theory/Quantitative Economics/Mathematical Methods. $3 1069071
700 1 $a Fu, Michael C. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1063805
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781493913831
776 0 8 $i Printed edition: $z 9781493913855
776 0 8 $i Printed edition: $z 9781493951666
830 0 $a International Series in Operations Research & Management Science, $x 0884-8289 ; $v 227 $3 1254441
856 4 0 $u https://doi.org/10.1007/978-1-4939-1384-8
912 $a ZDB-2-SBE
912 $a ZDB-2-SXBM
950 $a Business and Economics (SpringerNature-11643)
950 $a Business and Management (R0) (SpringerNature-43719)