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Handbook of simulation optimization

Fu, Michael C.

Handbook of simulation optimization

Record Type:	Electronic resources : Monograph/item
Title/Author:	Handbook of simulation optimizationedited by Michael C Fu.
other author:	Fu, Michael C.
Published:	New York, NY :Springer New York :2015.
Description:	xvi, 387 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Simulation methods.
Online resource:	http://dx.doi.org/10.1007/978-1-4939-1384-8
ISBN:	9781493913848 (electronic bk.)

Handbook of simulation optimization
Handbook of simulation optimization[electronic resource] /edited by Michael C Fu. - New York, NY :Springer New York :2015. - xvi, 387 p. :ill. (some col.), digital ;24 cm. - International series in operations research & management science,v.2160884-8289 ;. - International series in operations research & management science ;v. 140..

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 (electronic bk.)

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

185954
Simulation methods.

LC Class. No.: T57.62 / .H36 2015

Dewey Class. No.: 003.76

Handbook of simulation optimization
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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.
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