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15
Simulation Budget Allocation for Further Enhancing the Efficiency of Ordinal Optimization
 Journal of Discrete Event Dynamic Systems: Theory and Applications
, 2000
"... Abstract. Ordinal Optimization has emerged as an efficient technique for simulation and optimization. Exponential convergence rates can be achieved in many cases. In this paper, we present a new approach that can further enhance the efficiency of ordinal optimization. Our approach determines a highl ..."
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Cited by 56 (17 self)
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Abstract. Ordinal Optimization has emerged as an efficient technique for simulation and optimization. Exponential convergence rates can be achieved in many cases. In this paper, we present a new approach that can further enhance the efficiency of ordinal optimization. Our approach determines a highly efficient number of simulation replications or samples and significantly reduces the total simulation cost. We also compare several different allocation procedures, including a popular twostage procedure in simulation literature. Numerical testing shows that our approach is much more efficient than all compared methods. The results further indicate that our approach can obtain a speedup factor of higher than 20 above and beyond the speedup achieved by the use of ordinal optimization for a 210design example.
AN ASYMPTOTIC ALLOCATION FOR SIMULTANEOUS SIMULATION EXPERIMENTS
, 1999
"... In this paper, we consider the allocation of a fixed total number of simulation replications among competing design alternatives in order to (i) identify the best simulated design, (ii) intelligently determine the best simulation run lengths for all simulation experiments, and (iii) significantly re ..."
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Cited by 10 (1 self)
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In this paper, we consider the allocation of a fixed total number of simulation replications among competing design alternatives in order to (i) identify the best simulated design, (ii) intelligently determine the best simulation run lengths for all simulation experiments, and (iii) significantly reduce the total computation cost. An asymptotically optimal allocation rule for maximizing a lower bound of the probability of correct selection is presented. Moreover, we illustrate the efficiency of our method with a series of generic numerical experiments. The simulation cost is significantly reduced with our sequential approach.
Selecting The Best System: A DecisionTheoretic Approach
 In Proc. 1997 Winter Simulation Conference
, 1997
"... The problem of selecting the best system from a finite set of alternatives is considered from a Bayesian decisiontheoretic perspective. The framework presented is quite general, and permits selection from two or more systems, with replications that use either independent or common random numbers, w ..."
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Cited by 10 (2 self)
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The problem of selecting the best system from a finite set of alternatives is considered from a Bayesian decisiontheoretic perspective. The framework presented is quite general, and permits selection from two or more systems, with replications that use either independent or common random numbers, with unknown means and covariances for the output, and permits Gaussian or nonGaussian simulation output. For the case of unknown mean and variance with common random numbers, the framework provides a probability of correct selection that does not suffer from problems associated with the Bonferroni inequality. We indicate some criteria for which the Bayesian approach and other approaches are in general agreement, or disagreement. The probability of correct selection can be calculated either by quadrature or by Monte Carlo simulation from the posterior distribution of the parameters of the statistical distribution of the simulation output. We also comment on expectedvalue decisionmaking ver...
Input Model Uncertainty: Why Do We Care And What Should We Do About It?
, 2003
"... An input model is a collection of distributions together with any associated parameters that are used as primitive inputs in a simulation model. Input model uncertainty arises when one is not completely certain what distributions and/or parameters to use. This tutorial attempts to provide a sense of ..."
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Cited by 8 (1 self)
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An input model is a collection of distributions together with any associated parameters that are used as primitive inputs in a simulation model. Input model uncertainty arises when one is not completely certain what distributions and/or parameters to use. This tutorial attempts to provide a sense of why one should consider input uncertainty and what methods can be used to deal with it.
Bayesian Methods For Simulation
"... This tutorial describes some ways that Bayesian methods address problems that arise during simulation studies. This includes quantifying uncertainty about input distributions and parameters, sensitivity analysis, and the selection of the best of several simulated alternatives. Focus is on illustrati ..."
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Cited by 8 (2 self)
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This tutorial describes some ways that Bayesian methods address problems that arise during simulation studies. This includes quantifying uncertainty about input distributions and parameters, sensitivity analysis, and the selection of the best of several simulated alternatives. Focus is on illustrating the main ideas and their relevance to practical problems. Numerous citations for both introductory and more advanced material provide a launching pad into the Bayesian literature.
Efficient Inference for Mixed Bayesian Networks
 Proceedings of the 5th ISIF/IEEE International Conference on Information Fusion, 2002
, 2002
"... Bayesian network is a compact representation for probabilistic models and inference. They have been used successfully for multisensor fusion and situation assessment. It is well known that, in general, the inference algorithms to compute the exact posterior probability of the target state are either ..."
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Cited by 5 (0 self)
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Bayesian network is a compact representation for probabilistic models and inference. They have been used successfully for multisensor fusion and situation assessment. It is well known that, in general, the inference algorithms to compute the exact posterior probability of the target state are either computationally infeasible for dense networks or impossible for mixed discretecontinuous networks. In those cases, one approach is to compute the approximate results using simulation methods. This paper proposes efficient inference methods for those cases. The goal is not to compute the exact or approximate posterior probability of the target state, but to identify the top (most likely) ones in an efficient manner. The approach is to use intelligent simulation techniques where previous samples will be used to guide the future sampling strategy. By focusing the sampling on the "important" space, we are able to sort out the top candidates quickly. Simulation results are included to demonstrate the performances of the algorithms.
Distributed WebBased Simulation Optimization
, 2000
"... Web technology is having a significant impact on computer simulation. Most of the effort in webbased simulation is aimed at modeling, particularly at building simulation languages and at creating model libraries that can be assembled and executed over the web. We focus on the efficiency of simulati ..."
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Cited by 4 (0 self)
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Web technology is having a significant impact on computer simulation. Most of the effort in webbased simulation is aimed at modeling, particularly at building simulation languages and at creating model libraries that can be assembled and executed over the web. We focus on the efficiency of simulation experimentation for optimization. We introduce a framework for combining the statistical efficiency of simulation optimization techniques with the effectiveness of parallel execution algorithms. In particular, the Optimal Computing Budget Allocation (OCBA) algorithm is implemented in a webbased environment for lowcost parallel and distributed simulation experimentation. A prototype implementation with some experimental results is presented.
Selecting The Best System: A DecisionTheoretic Approach
, 1997
"... The problem of selecting the best system from a finite set of alternatives is considered from a Bayesian decisiontheoretic perspective. The framework presented is quite general, and permits selection from two or more systems, with replications that use either independent or common random numbers, w ..."
Abstract
 Add to MetaCart
The problem of selecting the best system from a finite set of alternatives is considered from a Bayesian decisiontheoretic perspective. The framework presented is quite general, and permits selection from two or more systems, with replications that use either independent or common random numbers, with unknown mean and covariance for the output, and permits Gaussian or nonGaussian simulation output. For the case of unknown means and variance with common random numbers, the framework provides a probability of correct selection that does not su#er from problems associated with the Bonferroni inequality. We indicate some criteria for which the Bayesian approach and other approaches are in general agreement, or disagreement. The probability of correct selection can be calculated either by quadrature or by Monte Carlo simulation from the posterior distribution of the parameters of the statistical distribution of the simulation output. We also comment on expectedvalue decisionmaking versu...
Proceedings of the 2001 Winter Simulation Conference
"... Proper education of a modeling and simulation professional meeting the extensive criteria imposed by the community poses significant challenges. In this paper, we explore the formation of a universitybased education in modeling and simulation to meet the challenges. We examine the factors affecting ..."
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Proper education of a modeling and simulation professional meeting the extensive criteria imposed by the community poses significant challenges. In this paper, we explore the formation of a universitybased education in modeling and simulation to meet the challenges. We examine the factors affecting the composition of a modeling and simulation course. Based on the anticipated consequences, we propose potential solutions.
BAYESIAN IDEAS AND DISCRETE EVENT SIMULATION: WHY, WHAT AND HOW
"... Bayesian methods are useful in the simulation context for several reasons. They provide a convenient and useful way to represent uncertainty about alternatives (like manufacturing system designs, service operations, or other simulation applications) in a way that quantifies uncertainty about the per ..."
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Bayesian methods are useful in the simulation context for several reasons. They provide a convenient and useful way to represent uncertainty about alternatives (like manufacturing system designs, service operations, or other simulation applications) in a way that quantifies uncertainty about the performance of systems, or about inputs parameters of those systems. They also can be used to improve the efficiency of discrete optimization with simulation and response surface methods. Bayesian methods work well with other decision theoretic tools, and can therefore provide a link from traditional operationslevel experiments to higherlevel managerial decisionmaking needs, in addition to improving the efficiency of computer experiments. 1