Sensitivity analysis of discrete event systems by the push out method

DOI10.1007/BF02060943zbMath0776.93024MaRDI QIDQ1207846

Reuven Y. Rubinstein

Publication date: 16 May 1993

Published in: Annals of Operations Research (Search for Journal in Brave)

zbMATH Keywords

discrete event system

Mathematics Subject Classification ID

Sensitivity (robustness) (93B35)

Related Items (15)

INVENTORY PROCESSES: QUASI-REGENERATIVE PROPERTY, PERFORMANCE EVALUATION, AND SENSITIVITY ESTIMATION VIA SIMULATION ⋮ Derivatives of probability functions and some applications ⋮ An exact method for the sensitivity analysis of systems simulated by rejection techniques ⋮ Differentiation formulas for probability functions: The transformation method ⋮ Unnamed Item ⋮ A New Likelihood Ratio Method for Training Artificial Neural Networks ⋮ Estimating Sensitivities of Portfolio Credit Risk Using Monte Carlo ⋮ Gradient estimation for smooth stopping criteria ⋮ Decomposable score function estimators for sensitivity analysis and optimization of queueing networks ⋮ Perturbation analysis of inhomogeneous finite Markov chains ⋮ Optimization and sensitivity analysis of computer simulation models by the score function method ⋮ Optimization of computer simulation models with rare events ⋮ A simulation-based approach to two-stage stochastic programming with recourse ⋮ Differentiability of probability function ⋮ Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data

Cites Work

• Unnamed Item
• Simulation methods of queues: An overview
• Sampling derivatives of probabilities
• Conditioning for variance reduction in estimating the sensitivity of simulations
• Importance Sampling for Stochastic Simulations
• A Unified View of the IPA, SF, and LR Gradient Estimation Techniques
• Estimating Derivatives Via Poisson's Equation
• The efficiency and heavy traffic properties of the score function method in sensitivity analysis of queueing models
• Queueing Simulation in Heavy Traffic
• Sensitivity Analysis for Simulations via Likelihood Ratios
• Indirect Estimation Via L = λW

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