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54
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 52 (3 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...
Simple Procedures for Selecting the Best Simulated System when the Number of Alternatives Is Large
 Operations Research
, 1999
"... In this paper we address the problem of finding the simulated system with the best (maximum or minimum) expected performance when the number of alternatives is finite, but large enough that rankingandselection (R&S) procedures may require too much computation to be practical. Our approach is to ..."
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Cited by 44 (9 self)
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In this paper we address the problem of finding the simulated system with the best (maximum or minimum) expected performance when the number of alternatives is finite, but large enough that rankingandselection (R&S) procedures may require too much computation to be practical. Our approach is to use the data provided by the first stage of sampling in an R&S procedure to screen out alternatives that are not competitive and thereby avoid the (typically much larger) secondstage sample for these systems. Our procedures represent a compromise between standard R&S proceduresthat are easy to implement, but can be computationally inefficientand fully sequential proceduresthat can be statistically efficient, but are more difficult to implement and depend on more restrictive assumptions. We present a general theory for constructing combined screening and indifferencezone selection procedures, several specific procedures and a portion of an extensive empirical evaluation. ...
Pricing American options: A comparison of Monte Carlo simulation approaches
 Journal of Computational Finance
, 1999
"... A number of Monte Carlo simulationbased approaches have been proposed within the past decade to address the problem of pricing Americanstyle derivatives. The purpose of this paper is to empirically test some of these algorithms on a common set of problems in order to be able to assess the strength ..."
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Cited by 29 (7 self)
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A number of Monte Carlo simulationbased approaches have been proposed within the past decade to address the problem of pricing Americanstyle derivatives. The purpose of this paper is to empirically test some of these algorithms on a common set of problems in order to be able to assess the strengths and weaknesses of each approach as a function of the problem characteristics. In addition, we introduce another simulationbased approach that parameterizes the early exercise curve and casts the valuation problem as an optimization problem of maximizing the expected payoff (under the martingale measure) with respect to the associated parameters, the optimization problem carried out using a simultaneous perturbation stochastic approximation (SPSA) algorithm.
Simulation Optimization
 In Proceedings of the 2001 Winter Simulation Conference, edited by
"... planning on register automata ..."
Optimal Structured Feedback Policies for ABR Flow Control Using Two Timescale SPSA
 Control,” Proceedings of the Summer Computer Simulation Conference, Society for Computer Simulation
, 1994
"... Abstract—Optimal structured feedback control policies for ratebased flow control of available bit rate service in asynchronous transfer mode networks are obtained in the presence of information and propagation delays, using a numerically efficient twotimescale simultaneous perturbation stochastic ..."
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Cited by 14 (8 self)
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Abstract—Optimal structured feedback control policies for ratebased flow control of available bit rate service in asynchronous transfer mode networks are obtained in the presence of information and propagation delays, using a numerically efficient twotimescale simultaneous perturbation stochastic approximation algorithm. Models comprising both a single bottleneck node and a network with multiple bottleneck nodes are considered. A convergence analysis of the algorithm is presented. Numerical experiments demonstrate fast convergence even in the presence of significant delays. We also illustrate performance comparisons with the wellknown Explicit Rate Indication for Congestion Avoidance (ERICA) algorithm and describe another algorithm (based on ERICA) that does not require estimating available bandwidth (as in ERICA). Index Terms—Network of nodes, optimal structured feedback policies, ratebased ABR flow control, single bottleneck node, twotimescale SPSA. I.
On choosing parameters in retrospectiveapproximation algorithms for simulationoptimization
 Proceedings of the 2006 Winter Simulation Conference. Institute of Electrical and Electronics Engineers: Piscataway
"... The Stochastic RootFinding Problem is that of finding a zero of a vectorvalued function known only through a stochastic simulation. The SimulationOptimization Problem is that of locating a realvalued function’s minimum, again with only a stochastic simulation that generates function estimates. ..."
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Cited by 10 (6 self)
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The Stochastic RootFinding Problem is that of finding a zero of a vectorvalued function known only through a stochastic simulation. The SimulationOptimization Problem is that of locating a realvalued function’s minimum, again with only a stochastic simulation that generates function estimates. Retrospective Approximation (RA) is a samplepath technique for solving such problems, where the solution to the underlying problem is approached via solutions to a sequence of approximate deterministic problems, each of which is generated using a specified sample size, and solved to a specified error tolerance. Our primary focus, in this paper, is providing guidance on choosing the sequence of sample sizes and error tolerances in RA algorithms. We first present an overview of the conditions that guarantee the correct convergence of RA’s iterates. Then we characterize a class of errortolerance and samplesize sequences that are superior to others in a certain precisely defined sense. We also identify and recommend members of this class, and provide a numerical example illustrating the key results. 1
Feature article: Optimization for simulation: Theory vs. practice
 INFORMS Journal on Computing
"... Probably one of the most successful interfaces between operations research and computer science has been the development of discreteevent simulation software. The recent integration of optimization techniques into simulation practice, specifically into commercial software, has become nearly ubiquit ..."
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Cited by 10 (2 self)
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Probably one of the most successful interfaces between operations research and computer science has been the development of discreteevent simulation software. The recent integration of optimization techniques into simulation practice, specifically into commercial software, has become nearly ubiquitous, as most discreteevent simulation packages now include some form of “optimization ” routine. The main thesis of this article, however, is that there is a disconnect between research in simulation optimization—which has addressed the stochastic nature of discreteevent simulation by concentratingon theoretical results of convergence and specialized algorithms that are mathematically elegant—and the recent software developments, which implement very general algorithms adopted from techniques in the deterministic optimization metaheuristic literature (e.g., genetic algorithms, tabu search, artificial neural networks). A tutorial exposition that summarizes the approaches found in the research literature is included, as well as a discussion contrastingthese approaches with the algorithms implemented in commercial software. The article concludes with the author’s speculations on promisingresearch areas and possible future directions in practice.
Estimationbased local search for stochastic combinatorial optimization
 IRIDIA, Université Libre de Bruxelles
, 2007
"... informs doi 10.1287/ijoc.1080.0276 ..."
Convergence analysis of gradient descent stochastic algorithms
 Journal of Optimization Theory and Applications
, 1996
"... Abstract. This paper proves convergence of a samplepath based stochastic gradientdescent algorithm for optimizing expectedvalue performance measures in discrete event systems. The algorithm uses increasing precision at successive iterations, and it moves against the direction of a generalized gra ..."
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Cited by 8 (0 self)
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Abstract. This paper proves convergence of a samplepath based stochastic gradientdescent algorithm for optimizing expectedvalue performance measures in discrete event systems. The algorithm uses increasing precision at successive iterations, and it moves against the direction of a generalized gradient of the computed sample performance function. Two convergence results are established: one, for the case where the expectedvalue function is continuously differentiable; and the other, when that function is nondifferentiable but the sample performance functions are convex. The proofs are based on a version of the uniform law of large numbers which is provable for many discrete event systems where infinitesimal perturbation analysis is known to be strongly consistent. Key Words. Gradient descent, subdifferentials, uniform laws of large numbers, infinitesimal perturbation analysis, discrete event dynamic systems. I.
Efficient Design and Sensitivity Analysis of Control Charts using Monte Carlo Simulation
 Management Science
, 1999
"... The design of control charts in statistical quality control addresses the optimal selection of the design parameters such as the sampling frequency and the control limits; and includes sensitivity analysis with respect to system parameters such as the various process parameters and the economic c ..."
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Cited by 8 (3 self)
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The design of control charts in statistical quality control addresses the optimal selection of the design parameters such as the sampling frequency and the control limits; and includes sensitivity analysis with respect to system parameters such as the various process parameters and the economic costs of sampling. The advent of more complicated control chart schemes has necessitated the use of Monte Carlo simulation in the design process, especially in the evaluation of performance measures such as average run length. In this paper, we apply two gradient estimation procedures  perturbation analysis and the likelihood ratio/score function method  to derive estimators that can be used in gradientbased optimization algorithms and in sensitivity analysis when Monte Carlo simulation is employed. We illustrate the techniques on a general control chart that includes the Shewhart chart and the exponentiallyweighted moving average chart as special cases. Simulation examples comparing the estimators with each other and with "brute force" finite di#erences demonstrate the possibility of significant variance reduction in settings of practical interest.