Addendum to "Extensions and generalizations of smoothed perturbation analysis in a generalized semi-Markov process framework"

Under a very general framework, both in terms of finite-time performance measures and system structure, the authors derive smoothed perturbation analysis (SPA) estimators and prove their unbiasedness. The commuting condition, which has been key in previous work, is not required a priori, and thus the framework includes such systems as the GI/G/1/K queue and multiclass queueing networks, which do not satisfy the commuting condition. The generality achieved is traded off against the fact that the estimator is not always easily implementable on a single sample path. The use of the commuting condition in a local sense is proposed to help simplify the estimators derived: queueing and multiclass queueing networks are used as illustrative examples. For a simple multiclass closed queueing network, some simulation results are provided. When the commuting condition is satisfied globally, the framework allows the recovery of previous results on IPA and SPA estimators as corollaries of the main theorems. >

[1]  Christos G. Cassandras,et al.  A new approach to the analysis of discrete event dynamic systems , 1983, Autom..

[2]  Xi-Ren Cao Convergence of parameter sensitivity estimates in a stochastic experiment , 1984, The 23rd IEEE Conference on Decision and Control.

[3]  Y. Ho,et al.  Smoothed (conditional) perturbation analysis of discrete event dynamical systems , 1987 .

[4]  Rajan Suri,et al.  Infinitesimal perturbation analysis for general discrete event systems , 1987, JACM.

[5]  Wei-Bo Gong,et al.  Smoothed Perturbation Analysis of Markovian Queueing Networks , 1988, 1988 American Control Conference.

[6]  Y. Ho,et al.  Extensions of infinitesimal perturbation analysis , 1988 .

[7]  R. Suri,et al.  Perturbation analysis gives strongly consistent sensitivity estimates for the M/G/ 1 queue , 1988 .

[8]  C. Cassandras,et al.  On-line sensitivity analysis of Markov chains , 1989 .

[9]  P. Glasserman,et al.  Smoothed perturbation analysis for a class of discrete-event systems , 1990 .

[10]  Paul Glasserman,et al.  Gradient Estimation Via Perturbation Analysis , 1990 .

[11]  M. Fu,et al.  Variance Properties of Second Derivative Perturbation Analysis Estimators for Single-Server Queues , 1990, 1990 American Control Conference.

[12]  M. Fu,et al.  On choosing the characterization for smoothed perturbation analysis , 1991 .

[13]  M. W. McKinnon,et al.  On perturbation analysis of queueing networks with finitely supported service time distributions , 1991 .

[14]  Paul Glasserman,et al.  Structural Conditions for Perturbation Analysis Derivative Estimation: Finite-Time Performance Indices , 1991, Oper. Res..

[15]  P. Glasserman,et al.  Strongly Consistent Steady-State Derivative Estimates , 1991, Probability in the Engineering and Informational Sciences.

[16]  M. Fu,et al.  Second Derivative Sample Path Estimators for the GI/G/m Queue , 1993 .