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26
Stochastic Gradient Estimation
, 2006
"... We consider the problem of efficiently estimating gradients from stochastic simulation. Although the primary motivation is their use in simulation optimization, the resulting estimators can also be useful in other ways, e.g., sensitivity analysis. The main approaches described are finite differences ..."
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Cited by 37 (5 self)
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We consider the problem of efficiently estimating gradients from stochastic simulation. Although the primary motivation is their use in simulation optimization, the resulting estimators can also be useful in other ways, e.g., sensitivity analysis. The main approaches described are finite differences (including simultaneous perturbations), perturbation analysis, the likelihood ratio/score function method, and the use of weak derivatives.
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 21 (8 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
OnLine IPA Gradient Estimators in Stochastic Continuous Fluid Models
 Journal of Optimization Theory and Applications
, 2002
"... This paper applies Infinitesimal Perturbation Analysis (IPA) to lossrelated and workloadrelated metrics in a class of Stochastic Flow Models (SFM). It derives closedform formulas for gradient estimators of these metrics with respect to various parameters of interest, such as bu#er size, service ra ..."
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Cited by 8 (2 self)
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This paper applies Infinitesimal Perturbation Analysis (IPA) to lossrelated and workloadrelated metrics in a class of Stochastic Flow Models (SFM). It derives closedform formulas for gradient estimators of these metrics with respect to various parameters of interest, such as bu#er size, service rate and inflow rate. The IPA estimators derived are simple and fast to compute, and are further shown to be unbiased and nonparametric in the sense that they can be computed directly from observed data without any knowledge of the underlying probability law. These properties hold the promise of utilizing IPA gradient estimates as an ingredient of online management and control of telecommunications networks. While this paper considers singlenode SFMs, the analysis method developed is amenable to extensions to networks of SFM nodes with more general topologies. Key words and phrases. Stochastic Fluid Models (SFM), Infinitesimal Perturbation Analysis (IPA), network management and control. # Supported in part by the National Science Foundation under grant DMI0085659 and by DARPA under contract F306020020556.
Infinitesimal Perturbation Analysis and Optimization for MaketoStock Manufacturing Systems Based on Stochastic Fluid Models. Discrete Event Dynamic System
, 2006
"... In this paper we study MakeToStock manufacturing systems and seek online algorithms for determining optimal or near optimal buffer capacities (hedging points) that balance inventory against stockout costs. Using a Stochastic Fluid Model (SFM), we derive sample derivatives (sensitivities) which, u ..."
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Cited by 3 (0 self)
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In this paper we study MakeToStock manufacturing systems and seek online algorithms for determining optimal or near optimal buffer capacities (hedging points) that balance inventory against stockout costs. Using a Stochastic Fluid Model (SFM), we derive sample derivatives (sensitivities) which, under very weak structural assumptions on the defining demand and service processes, are shown to be unbiased estimators of the sensitivities of a cost function with respect to these capacities. When applied to discretepart systems, we show that these estimators are greatly simplified and become nonparametric. Thus, they can be easily implemented and evaluated on line. Though the implementation on discretepart systems does not necessarily preserve the unbiasedness property, simulation results show that stochastic approximation algorithms that use such estimates do converge to optimal or near optimal hedging points. 1
Multiintersection Traffic Light Control Using Infinitesimal Perturbation Analysis?
"... Abstract: We address the traffic light control problem for multiple intersections in tandem by viewing it as a stochastic hybrid system and developing a Stochastic Flow Model (SFM) for it. Using Infinitesimal Perturbation Analysis (IPA), we derive online gradient estimates of a cost metric with res ..."
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Cited by 3 (1 self)
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Abstract: We address the traffic light control problem for multiple intersections in tandem by viewing it as a stochastic hybrid system and developing a Stochastic Flow Model (SFM) for it. Using Infinitesimal Perturbation Analysis (IPA), we derive online gradient estimates of a cost metric with respect to the controllable green and red cycle lengths. The IPA estimators obtained require counting traffic light switchings and estimating car flow rates only when specific events occur. The estimators are used to iteratively adjust light cycle lengths to improve performance and, in conjunction with a standard gradientbased algorithm, to obtain optimal values which adapt to changing traffic conditions. Simulation results are included to illustrate the approach.
WeC01.2 Fluid Approximation and Perturbation Analysis of a Dynamic Priority Call Center
"... Abstract — We analyze a call center with multiclass calls and dynamic priority service discipline, in which a lower priority customer becomes high priority when its waiting time exceeds a given service level threshold. For each priority queue, the service discipline is first come, first served. Base ..."
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Abstract — We analyze a call center with multiclass calls and dynamic priority service discipline, in which a lower priority customer becomes high priority when its waiting time exceeds a given service level threshold. For each priority queue, the service discipline is first come, first served. Based on a fluid approximation of the system, we apply infinitesimal perturbation analysis (IPA) to derive estimators for the derivative of the queue lengths with respect to the threshold parameter. We establish unbiasedness of the estimators, and report numerical results via simulation. I.
Quasidynamic Traffic Light Control for a Single Intersection
, 2013
"... We address the traffic light control problem for a single intersection by viewing it as a stochastic hybrid system and developing a Stochastic Flow Model (SFM) for it. We adopt a quasidynamic control policy based on partial state information defined by detecting whether vehicle backlog is above ..."
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We address the traffic light control problem for a single intersection by viewing it as a stochastic hybrid system and developing a Stochastic Flow Model (SFM) for it. We adopt a quasidynamic control policy based on partial state information defined by detecting whether vehicle backlog is above or below a certain threshold, without the need to observe an exact vehicle count. The policy is parameterized by green and red cycle lengths which depend on this partial state information. Using Infinitesimal Perturbation Analysis (IPA), we derive online gradient estimators of an average traffic congestion metric with respect to these controllable green and red cycle lengths when the vehicle backlog is above or below the threshold. The estimators are used to iteratively adjust light cycle lengths so as to improve performance and, in conjunction with a standard gradientbased algorithm, to seek optimal values which adapt to changing traffic conditions. Simulation results are included to illustrate the approach and quantify the benefits of quasidynamic traffic light control over earlier static approaches. 1.
IPA for Continuous Stochastic Marked Graphs ∗
"... This paper presents a unied framework for the Innitesimal Perturbation Analysis (IPA) gradientestimation technique in the setting of marked graphs. It proposes a systematic approach for computing the derivatives of sample performance functions with respect to structural and control parameters. The ..."
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This paper presents a unied framework for the Innitesimal Perturbation Analysis (IPA) gradientestimation technique in the setting of marked graphs. It proposes a systematic approach for computing the derivatives of sample performance functions with respect to structural and control parameters. The resulting algorithms are recursive in both time and network
ows, and their successive steps are computed in response to the occurrence and propagation of certain events in the network. Such events correspond to discontinuities in the network
owrates, and their special characteristics are due to the properties of continuous transitions and
uid places. Following a general outline of the framework we focus on a simple yet canonical example, and investigate throughput and workloadrelated performance criteria as functions of structural and control variables. Simulation experiments support the analysis and testify to the potential viability of the proposed approach. Published as:
Application of IPA to Fluid Petri Nets
"... Infinitesimal Perturbation Analysis (IPA) recently has been extensively investigated in the setting of fluid queues, where it was shown to yield simple algorithms for computing the gradients of several performance functions. More lately, efforts have been made to extend its application domain from f ..."
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Cited by 1 (1 self)
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Infinitesimal Perturbation Analysis (IPA) recently has been extensively investigated in the setting of fluid queues, where it was shown to yield simple algorithms for computing the gradients of several performance functions. More lately, efforts have been made to extend its application domain from fluid queueing networks to other kinds of stochastic hybrid systems. In this vein, the present paper inaugurates a study of the application of IPA to a class of hybrid Petri nets. The main point of concern is the modeling element of the fluid transition with multiple input places, representing concurrency and synchronization in Petri nets, and not yet studied in the context of IPA. We first derive the IPA gradient of the throughput with respect to fluid flow parameters at the input places, and then consider an example of optimizing throughput in a forkjoin system. Simulation experiments are presented in support of the theoretical results. We point out that the main purpose of the paper is to initiate a study of IPA in the setting of hybrid Petri nets, and not to consider application examples. Published as:
Infinitesimal perturbation analysis for maketostock manufacturing systems based on stochastic fluid models
 in Proceedings of the IFAC Workshop of Descrete Event Systems WoDES’04
, 2004
"... Abstract: In this paper we study MakeToStock manufacturing systems and generalize the results obtained in Panayiotou et al. (2002). Specifically, we use the same modeling framework to derive sample derivatives of the objective function of interest with respect to buffer capacities (hedging points) ..."
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Cited by 1 (1 self)
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Abstract: In this paper we study MakeToStock manufacturing systems and generalize the results obtained in Panayiotou et al. (2002). Specifically, we use the same modeling framework to derive sample derivatives of the objective function of interest with respect to buffer capacities (hedging points). In the earlier work, the input processes (machine processing and demand) were assumed piecewise constant. In this work, we generalize the results to piecewise differentiable processes. The derived estimates are unbiased and nonparametric, in the sense that they require no knowledge of the distributions of the underlying random processes. However, unlike the earlier results, these estimators require knowledge of some rates at certain points in time. 1.