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Simulation Optimization: A Review, New Developments, and Applications
 In Proceedings of the 37th Winter Simulation Conference
, 2005
"... ABSTRACT We provide a descriptive review of the main approaches for carrying out simulation optimization, and sample some recent algorithmic and theoretical developments in simulation optimization research. Then we survey some of the software available for simulation languages and spreadsheets, and ..."
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Cited by 54 (5 self)
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ABSTRACT We provide a descriptive review of the main approaches for carrying out simulation optimization, and sample some recent algorithmic and theoretical developments in simulation optimization research. Then we survey some of the software available for simulation languages and spreadsheets, and present several illustrative applications.
Minibatch stochastic approximation methods for nonconvex stochastic composite optimization
, 2013
"... ..."
Scalable scheduling policy design for open soft realtime systems
 RealTime and Embedded Technology and Applications Symposium, IEEE
"... Abstract—Open soft realtime systems, such as mobile robots, must respond adaptively to varying operating conditions, while balancing the need to perform multiple mission specific tasks against the requirement that those tasks complete in a timely manner. Setting and enforcing a utilization target f ..."
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Cited by 7 (6 self)
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Abstract—Open soft realtime systems, such as mobile robots, must respond adaptively to varying operating conditions, while balancing the need to perform multiple mission specific tasks against the requirement that those tasks complete in a timely manner. Setting and enforcing a utilization target for shared resources is a key mechanism for achieving this behavior. However, because of the uncertainty and nonpreemptability of some tasks, key assumptions of classical scheduling approaches do not hold. In previous work we presented foundational methods for generating task scheduling policies to enforce proportional resource utilization for open soft realtime systems with these properties. However, these methods scale exponentially in the number of tasks, limiting their practical applicability. In this paper, we present a novel parameterized scheduling policy that scales our technique to a much wider range of systems. These policies can represent geometric features of the scheduling policies produced by our earlier methods, but only require a number of parameters that is quadratic in the number of tasks. We provide empirical evidence that the best of these policies are competitive with exact solution methods in small problems, and significantly outperform heuristic methods in larger ones. I.
Conditional Monte Carlo estimation of quantile sensitivities
, 2009
"... doi 10.1287/mnsc.1090.1090 ..."
A multimodel algorithm for the optimization of congested networks
 Proceedings of the European Transport Conference (ETC
, 2009
"... Microscopic simulators embed numerous traffic models that make them detailed and realistic tools appropriate to perform scenariobased or sensitivity analysis. This realism leads to nonlinear objective functions with no available closed form and containing potentially several local minima. As nonli ..."
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Cited by 4 (2 self)
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Microscopic simulators embed numerous traffic models that make them detailed and realistic tools appropriate to perform scenariobased or sensitivity analysis. This realism leads to nonlinear objective functions with no available closed form and containing potentially several local minima. As nonlinear, stochastic and evaluationexpensive models, their integration within an optimization framework remains a difficult and challenging task. We believe that in order to perform both fast and reliable simulation optimization for congested networks, information from the simulation tool should be combined with information from a network model that analytically captures the structure of the underlying problem. This paper presents a surrogate that combines the information from a calibrated microscopic traffic simulation model of the Lausanne city center (Dumont and Bert, 2006), with an analytical queueing network model (Osorio and Bierlaire, 2009a) that resorts to finite capacity queueing theory to capture the key traffic dynamics and the underlying network structure, e.g. how upstream and downstream queues interact, how this interaction is linked to network congestion. This network model, which consists of a system of nonlinear equations, has been successfully used in past work to a solve traffic signal control problem (Osorio and Bierlaire, 2009b). We integrate this surrogate within a derivativefree (DF) trust region optimization framework (Conn et al., 2009a). Resorting to a DF algorithm is particularly appropriate for noisy problems where the derivatives are difficult to obtain and often unreliable. This is also the case when the evaluation of the objective function is computationally expensive, or when the simulation source code is unavailable. In the field of transportation, the simulators typically fall into all three of these categories. The framework is illustrated by solving a fixedtime signal control problem for a subnetwork of the Lausanne city center. The performance of the derived plans is compared to that of an existing plan for the city of Lausanne. 1
The mathematics of continuousvariable simulation optimization
 Proceedings of the 2008 Winter Simulation Conference
, 2008
"... Continuousvariable simulation optimization problems are those optimization problems where the objective function is computed through stochastic simulation and the decision variables are continuous. We discuss verifiable conditions under which the objective function is continuous or differentiable, ..."
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Cited by 4 (2 self)
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Continuousvariable simulation optimization problems are those optimization problems where the objective function is computed through stochastic simulation and the decision variables are continuous. We discuss verifiable conditions under which the objective function is continuous or differentiable, and outline some key properties of two classes of methods for solving such problems, namely sampleaverage approximation and stochastic approximation. 1
A Guide to SampleAverage Approximation
, 2011
"... We provide a review of the principle of sampleaverage approximation (SAA) for solving simulationoptimization problems. Our goal is to provide an accessible overview of the area and emphasize interesting recent work. We explain when one might want to use SAA and when one might expect it to provide g ..."
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Cited by 3 (0 self)
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We provide a review of the principle of sampleaverage approximation (SAA) for solving simulationoptimization problems. Our goal is to provide an accessible overview of the area and emphasize interesting recent work. We explain when one might want to use SAA and when one might expect it to provide goodquality solutions. We also review some of the key theoretical properties of the solutions obtained through SAA. We contrast SAA with stochastic approximation (SA) methods in terms of the computational effort required to obtain solutions of a given quality, explaining why SA “wins” asymptotically. However, an extension of SAA known as retrospective optimization can match the asymptotic convergence rate of SA, at least up to a multiplicative constant. 1
Sensitivity analysis for barrier options
 Proceedings of the 2009 Winter Simulation Conference:1272–1282
, 2009
"... Barrier options are popular derivative securities with payoffs dependent on whether or not an underlying asset crosses a barrier. This paper presents a Monte Carlo simulationbased method of sensitivity analysis for barrier options based on smoothed perturbation analysis (SPA) for a general form of ..."
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Barrier options are popular derivative securities with payoffs dependent on whether or not an underlying asset crosses a barrier. This paper presents a Monte Carlo simulationbased method of sensitivity analysis for barrier options based on smoothed perturbation analysis (SPA) for a general form of discontinuous sample function payoffs. The connection between the resulting SPA estimator and the probability formula derived in Hong (2008) and its generalization in Liu and Hong (2009) is explored. Using a Brownian bridge result, the estimator is applied to continuouslymonitored barrier options with rebates. Illustrative simulation examples are provided. 1
Optimality Functions in Stochastic Programming
, 2009
"... Optimality functions in nonlinear programming conveniently measure, in some sense, the distance between a candidate solution and a stationary point. They may also provide guidance towards the development of implementable algorithms. In this paper, we use an optimality function to construct procedu ..."
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Cited by 2 (2 self)
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Optimality functions in nonlinear programming conveniently measure, in some sense, the distance between a candidate solution and a stationary point. They may also provide guidance towards the development of implementable algorithms. In this paper, we use an optimality function to construct procedures for validation analysis in stochastic programs with nonlinear, possibly nonconvex, expected value functions as both objective and constraint functions. We construct an estimator of the optimality function and examine its consistency, bias, and asymptotic distribution. The estimator leads to confidence intervals for the value of the optimality function at a candidate solution and, hence, provides a quantitative measure of solution quality. We also construct an implementable algorithm for solving smooth stochastic programs based on sample average approximations and the optimality function estimator. Preliminary numerical tests illustrate the proposed algorithm and validation analysis procedures.