Open AccessBook
Simulation Modeling and Analysis
Averill M. Law,W. David Kelton +1 more
TLDR
The text is designed for a one-term or two-quarter course in simulation offered in departments of industrial engineering, business, computer science and operations research.Abstract:
From the Publisher:
This second edition of Simulation Modeling and Analysis includes a chapter on "Simulation in Manufacturing Systems" and examples. The text is designed for a one-term or two-quarter course in simulation offered in departments of industrial engineering,business,computer science and operations research.read more
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Journal ArticleDOI
Water Distribution Reliability: Simulation Methods
TL;DR: Simulation enables computation of a much broader class of reliability measures than do analytical methods, but it requires considerably more computer time and its results are less easy to generalize.
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Why the Monte Carlo method is so important today
TL;DR: The reasons why the Monte Carlo method has evolved from a ‘last resort’ solution to a leading methodology that permeates much of contemporary science, finance, and engineering are explored.
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Estimating security price derivatives using simulation
Mark Broadie,Paul Glasserman +1 more
TL;DR: In this article, the authors present two direct methods, a pathwise method and a likelihood ratio method, for estimating derivatives of security prices using simulation, and compare them to the standard method of resimulation to estimate derivatives.
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Sample Average Approximation Method for Chance Constrained Programming: Theory and Applications
TL;DR: This work considers the sample average approximation (SAA) approach and discusses the convergence properties of the resulting problem, and presents a method for constructing statistical lower bounds for the optimal value of the considered problem.
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Modeling good research practices - Overview: A report of the ISPOR-SMDM modeling good research practices task force-1
TL;DR: In this paper, the authors present the updated recommendations for best practices in conceptualizing models; implementing state-transition approaches, discrete event simulations, or dynamic transmission models; and dealing with uncertainty and validating and reporting models transparently.