State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments

By Jack P. C. Kleijnen, S. Sanchez, Thomas W. Lucas, Thomas M. Cioppa. INFORMS journal on computing, 2005

558 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/befa975e4c9f0c5d902b178a68a45301ab3313bc

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In Computational Theory and Mathematics

In Management Science and Operations Research

Citations in the corpus

A multiobjective response surface approach for improved water quality planning in lakes and reservoirs (2010, methodology): “Design of experiments (DOE) can be employed for effectively sampling the decision space either using statistical techniques [see, e.g., Kleijnen et al., 2005, and references therein] or based on physical considerations and a priori knowledge [Galelli et al., 2008, 2010].”
An algorithm for fast optimal Latin hypercube design of experiments (2010, background): “The LHD presents advantages such as (i) the number of samples (points) is not fixed; (ii) orthogonality of the sampling points (a design is orthogonal if the inner product of any two columns is zero [3, 18]); and (iii) the sampling points do not depend on the surrogate model that will be constructed”
Selecting random Latin hypercube dimensions and designs through estimation of maximum absolute pairwise correlation (2012, unknown): “Computer simulations used in the above areas may contain thousands of input variables and/or take a long time (even many days) to run (Kleijnen et al. 2005).”
A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems (2015, background): “Modern design of experiments [7] can be a first step in such a study, helping to identify promising areas in the solution space or performing sensitivity analysis.”
Research, part of a Special Feature on Empirical based agent-based modeling Agent-based Analysis of Agricultural Policies: an Illustration of the Agricultural Policy Simulator AgriPoliS, its Adaptation and Behavior (2006, background): “Unfortunately, even with complex simulation models, sensitivity analyses are often done in an unstructured way, e. g., by simply varying only one factor at a time (Manson 2002, Kleijnen et al. 2003).”
Factor screening for simulation with multiple responses: Sequential bifurcation (2014, methodology): “We also refer to the general review of design and analysis of simulation experiments by Kleijnen et al. (2005).”