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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Traffic modeling for telecommunications networks
Victor S. Frost,Benjamin Melamed +1 more
TL;DR: An overview of discrete event simulation is given and two important modelling issues that are germane to extant and emerging networks: traffic modelling and rare event simulation are singled out.
Book
Design and Analysis of Simulation Experiments
TL;DR: The text presents both classic and modern statistical designs for discrete-event simulation and provides relatively simple solutions for selecting problems to simulate, how to analyze the resulting data from simulation, and computationally challenging simulation problems.
Journal ArticleDOI
Bias correction of daily GCM rainfall for crop simulation studies
TL;DR: In this article, a procedure that calibrates both the frequency and the intensity distribution of daily GCM rainfall relative to a target station, and demonstrate its application to maize yield simulation at a location in semi-arid Kenya is presented.
Journal ArticleDOI
Accelerated gradient methods for nonconvex nonlinear and stochastic programming
Saeed Ghadimi,Guanghui Lan +1 more
TL;DR: The AG method is generalized to solve nonconvex and possibly stochastic optimization problems and it is demonstrated that by properly specifying the stepsize policy, the AG method exhibits the best known rate of convergence for solving general non Convex smooth optimization problems by using first-order information, similarly to the gradient descent method.
Journal ArticleDOI
Stochastic Kriging for Simulation Metamodeling
TL;DR: The basic theory of kriging is extended, as applied to the design and analysis of deterministic computer experiments, to the stochastic simulation setting to provide flexible, interpolation-based metamodels of simulation output performance measures as functions of the controllable design or decision variables.