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163
Verification and Validation of Simulation Models
 Proceedings of 1994 Winter Simulation Conference, Lake Buena Vista, FL
, 1994
"... This paper discusses verification and validation of simulation models. The different approaches to deciding model validity are presented; how model verification and validation relate to the model development process are discussed; various validation techniques are defined; conceptual model validity, ..."
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Cited by 344 (11 self)
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This paper discusses verification and validation of simulation models. The different approaches to deciding model validity are presented; how model verification and validation relate to the model development process are discussed; various validation techniques are defined; conceptual model validity, model verification, operational validity, and data validity are described; ways to document results are given; and a recommended procedure is presented. 1.
Comparative Studies Of Metamodeling Techniques Under Multiple Modeling Criteria
 Structural and Multidisciplinary Optimization
, 2000
"... 1 Despite the advances in computer capacity, the enormous computational cost of complex engineering simulations makes it impractical to rely exclusively on simulation for the purpose of design optimization. To cut down the cost, surrogate models, also known as metamodels, are constructed from and ..."
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Cited by 126 (7 self)
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1 Despite the advances in computer capacity, the enormous computational cost of complex engineering simulations makes it impractical to rely exclusively on simulation for the purpose of design optimization. To cut down the cost, surrogate models, also known as metamodels, are constructed from and then used in lieu of the actual simulation models. In the paper, we systematically compare four popular metamodeling techniquesPolynomial Regression, Multivariate Adaptive Regression Splines, Radial Basis Functions, and Krigingbased on multiple performance criteria using fourteen test problems representing different classes of problems. Our objective in this study is to investigate the advantages and disadvantages these four metamodeling techniques using multiple modeling criteria and multiple test problems rather than a single measure of merit and a single test problem. 1 Introduction Simulationbased analysis tools are finding increased use during preliminary design to explore desi...
Latin Hypercube Sampling and the propagation of uncertainty in analyses of complex systems,” Reliability Engineering and System Safety
, 2003
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Environment Centered Analysis and Design of Coordination Mechanisms
, 1995
"... Coordination, as the act of managing interdependencies between activities, is one of the central research issues in Distributed Artificial Intelligence. Many researchers have shown that there is no single best organization or coordination mechanism for all environments. Problems in coordinating the ..."
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Cited by 103 (24 self)
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Coordination, as the act of managing interdependencies between activities, is one of the central research issues in Distributed Artificial Intelligence. Many researchers have shown that there is no single best organization or coordination mechanism for all environments. Problems in coordinating the activities of distributed intelligent agents appear in many domains: the control of distributed sensor networks; multiagent scheduling of people and/or machines; distributed diagnosis of errors in localarea or telephone networks; concurrent engineering; `software agents' for information gathering. The design of coordination mechanisms for group...
Quantitative Modeling of Complex Computational Task Environments
 in Proceedings of the Eleventh National Conference on Artificial Intelligence
, 1993
"... There are many formal approaches to specifying how the mental state of an agent entails that it perform particular actions. These approaches put the agent at the center of analysis. For some questions and purposes, it is more realistic and convenient for the center of analysis to be the task envi ..."
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Cited by 95 (46 self)
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There are many formal approaches to specifying how the mental state of an agent entails that it perform particular actions. These approaches put the agent at the center of analysis. For some questions and purposes, it is more realistic and convenient for the center of analysis to be the task environment, domain, or society of which agents will be a part. This paper presents such a task environmentoriented modeling framework that can work handinhand with more agentcentered approaches. Our approach features careful attention to the quantitative computational interrelationships between tasks, to what information is available (and when) to update an agent's mental state, and to the general structure of the task environment rather than singleinstance examples. A task environment model can be used for both analysis and simulation, it avoids the methodologicalproblems of relying solely on singleinstance examples, and provides concrete, meaningful characterizations with which ...
A Methodology for Fitting and Validating Metamodels in Simulation
 European Journal of Operational Research
, 1997
"... This expository paper discusses the relationships among metamodels, simulation models, and problem entities. A metamodel or response surface is an approximation of the input/output function implied by the underlying simulation model. There are several types of metamodel: linear regression, splines, ..."
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Cited by 94 (5 self)
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This expository paper discusses the relationships among metamodels, simulation models, and problem entities. A metamodel or response surface is an approximation of the input/output function implied by the underlying simulation model. There are several types of metamodel: linear regression, splines, neural networks, etc. This paper distinguishes between fitting and validating a metamodel. Metamodels may have different goals: (i) understanding, (ii) prediction, (iii) optimization, and (iv) verification and validation. For this metamodeling, a process with thirteen steps is proposed. Classic design of experiments (DOE) is summarized, including standard measures of fit such as the Rsquare coefficient and crossvalidation measures. This DOE is extended to sequential or stagewise DOE. Several validation criteria, measures, and estimators are discussed. Metamodels in general are covered, along with a procedure for developing linear regression (including polynomial) metamodels. Keywords Simul...
Quantitative Modeling of Complex Environments
, 1994
"... There are many formal approaches to specifying how the mental state of an agent entails the particular actions it will perform. These approaches put the agent at the center of analysis. For some questions and purposes, it is more realistic and convenient for the center of analysis to be the task env ..."
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Cited by 89 (44 self)
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There are many formal approaches to specifying how the mental state of an agent entails the particular actions it will perform. These approaches put the agent at the center of analysis. For some questions and purposes, it is more realistic and convenient for the center of analysis to be the task environment, domain, or society of which agents will be a part. This paper presents such a task environmentoriented modeling framework that can work handinhand with more agentcentered approaches. Our approach features careful attention to the quantitative computational interrelationships between tasks, to what information is available (and when) to update an agent's mental state, and to the general structure of the task environment rather than singleinstance examples. A task environment model can be used for both analysis and simulation, it avoids the methodological problems of relying solely on singleinstance examples, and provides concrete, meaningful characterizations with which to sta...
Optimization via simulation: a review
 Annals of Operations Research
, 1994
"... We review techniques for optimizing stochastic discreteevent systems via simulation. We discuss both the discrete parameter case and the continuous parameter case, but concentrate on the latter which has dominated most of the recent research in the area. For the discrete parameter case, we focus on ..."
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Cited by 89 (21 self)
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We review techniques for optimizing stochastic discreteevent systems via simulation. We discuss both the discrete parameter case and the continuous parameter case, but concentrate on the latter which has dominated most of the recent research in the area. For the discrete parameter case, we focus on the techniques for optimization from a finite set: multiplecomparison procedures and rankingandselection procedures. For the continuous parameter case, we focus on gradientbased methods, including perturbation analysis, the likelihood ratio method, and frequency domain experimentation. For illustrative purposes, we compare and contrast the implementation of the techniques for some simple discreteevent systems such as the (s, S) inventory system and the GI/G/1 queue. Finally, we speculate on future directions for the field, particularly in the context of the rapid advances being made in parallel computing.
Flexibility and Efficiency Enhancements for Constrained Global Design Optimization with Kriging Approximations
, 2002
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Tuning Search Algorithms for RealWorld Applications: A Regression Tree Based Approach
, 2004
"... The optimization of complex realworld problems might benefit from well tuned algorithm's parameters. We propose a methodology that performs this tuning in an effective and efficient algorithmical manner. This approach combines methods from statistical design of experiments, regression analysis ..."
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Cited by 34 (5 self)
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The optimization of complex realworld problems might benefit from well tuned algorithm's parameters. We propose a methodology that performs this tuning in an effective and efficient algorithmical manner. This approach combines methods from statistical design of experiments, regression analysis, design and analysis of computer experiments methods, and treebased regression. It can also be applied to analyze the influence of different operators or to compare the performance of different algorithms. An evolution strategy and a simulated annealing algorithm that optimize an elevator supervisory group controller system are used to demonstrate the applicability of our approach to realworld optimization problems.