Likelihood ratio gradient estimation for stochastic systems
Communications of the ACM - Special issue on simulation
Bias properties of budget constrained simulations
Operations Research
Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Simulation: a statistical perspective
Simulation: a statistical perspective
The simulation metamodel
A one-measurement form of simultaneous perturbation stochastic approximation
Automatica (Journal of IFAC)
Optimization: algorithms and consistent approximations
Optimization: algorithms and consistent approximations
Weighted Means in Stochastic Approximation of Minima
SIAM Journal on Control and Optimization
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Global Stochastic Optimization with Low-Dispersion Point Sets
Operations Research
Simulation Modeling Handbook: A Practical Approach
Simulation Modeling Handbook: A Practical Approach
Sensitivity analysis and the “what if” problem in simulation analysis
Mathematical and Computer Modelling: An International Journal
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Descriptive simulation finds the performance measure of a system, given a particular value for the input parameters. Inverse simulation reverses this and attempts to find the controllable input parameters required to achieve a particular performance measure. This paper proposes using a 'stochastic approximation' to estimate the necessary design parameters within a range of desired accuracy. The proposed solution algorithm is based on Newton's methods using a single-run simulation to minimize a loss function that measures the deviation from a target value. The properties of the solution algorithm and the validity of the estimates are examined by applying them to a reliability system with a known analytical solution.