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
Optimization via simulation: a review
TL;DR: Techniques for optimizing stochastic discrete-event systems via simulation, including perturbation analysis, the likelihood ratio method, and frequency domain experimentation are reviewed.
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The workload on parallel supercomputers: modeling the characteristics of rigid jobs
Uri Lublin,Dror G. Feitelson +1 more
TL;DR: This work analyzes and model the job-level workloads with an emphasis on those aspects that are universal to all sites, and creates a synthetic workload based on the results of the analysis.
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Conceptual modelling for simulation Part I: definition and requirements
TL;DR: The meaning of conceptual modelling and the requirements of a conceptual model are discussed, and a conceptual modelling framework is proposed due to a paucity of advice on how to design a conceptual models.
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Bayesian theory of probabilistic forecasting via deterministic hydrologic model
TL;DR: The BFS is compared with Monte Carlo simulation and “ensemble forecasting” technique, none of which can alone produce a probabilistic forecast that meets requirements of rational decision making, but each can serve as a component of the BFS.
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The Knowledge-Gradient Policy for Correlated Normal Beliefs
TL;DR: A fully sequential sampling policy is proposed called the knowledge-gradient policy, which is provably optimal in some special cases and has bounded suboptimality in all others and it is demonstrated how this policy may be applied to efficiently maximize a continuous function on a continuous domain while constrained to a fixed number of noisy measurements.