Application of descent algorithms with Armijo stepsizes to simulation-based optimization of queueing networks

Gradient descent algorithms with Armijo stepsizes are adapted to simulation-based optimization of queuing networks. The cost function was evaluated by Monte Carlo simulation, and its gradient was estimated by infinitesimal perturbation analysis. The Armijo stepsize routine requires multiple function evaluations, which are simultaneously performed by finite perturbation analysis (FPA) in one simulation run. Two kinds of FPA estimators are considered: one is precise, but time consuming, and the other is approximate, but faster. Numerical examples show the validity of the proposed algorithm.<<ETX>>

[1]  Yu-Chi Ho,et al.  Performance evaluation and perturbation analysis of discrete event dynamic systems , 1987 .

[2]  Peter W. Glynn,et al.  Proceedings of Ihe 1986 Winter Simulation , 2022 .

[3]  Edwin K. P. Chong,et al.  Convergence of Recursive Optimization Algorithms using IPA Derivative Estimates , 1990, 1990 American Control Conference.

[4]  H. Mukai,et al.  An optimization algorithm driven by probabilistic simulation , 1986, 1986 25th IEEE Conference on Decision and Control.

[5]  Yu-Chi Ho,et al.  Sensitivity analysis and optimization of throughput in a production line with blocking , 1987 .

[6]  Reuven Y. Rubinstein,et al.  Sensitivity Analysis and Performance Extrapolation for Computer Simulation Models , 1989, Oper. Res..

[7]  Christos G. Cassandras,et al.  Infinitesimal and finite perturbation analysis for queueing networks , 1982, 1982 21st IEEE Conference on Decision and Control.

[8]  M. Fu.  Convergence of a stochastic approximation algorithm for the GI/G/1 queue using infinitesimal perturbation analysis , 1990 .

[9]  R. Suri,et al.  Perturbation analysis: the state of the art and research issues explained via the GI/G/1 queue , 1989, Proc. IEEE.

[10]  L. Armijo.  Minimization of functions having Lipschitz continuous first partial derivatives. , 1966 .

[11]  Marc S. Meketon,et al.  Optimization in simulation: a survey of recent results , 1987, WSC '87.

[12]  Peter W. Glynn,et al.  Monte Carlo Optimization of Stochastic Systems: Two New Approaches. , 1986 .

[13]  Alan Weiss,et al.  Sensitivity Analysis for Simulations via Likelihood Ratios , 1989, Oper. Res..