Comparing stochastic systems using regenerative simulation with common random numbers

Suppose two alternative designs for a stochastic system are to be compared. These two systems can be simulated independently or dependently. This paper presents a method for comparing two regenerative stochastic processes in a dependent fashion using common random numbers. A set of sufficient conditions is given that guarantees that the dependent simulations will produce a variance reduction over independent simulations. Numerical examples for a variety of simple stochastic models are included which illustrate the variance reduction achieved.

[1]  Walter L. Smith,et al.  Regenerative stochastic processes , 1955, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[2]  J. Wolfowitz,et al.  On the Characteristics of the General Queueing Process, with Applications to Random Walk , 1956 .

[3]  Kai Lai Chung,et al.  Markov Chains with Stationary Transition Probabilities , 1961 .

[4]  D. Walkup,et al.  Association of Random Variables, with Applications , 1967 .

[5]  P. Billingsley,et al.  Convergence of Probability Measures , 1970, The Mathematical Gazette.

[6]  D. Daley Stochastically monotone Markov Chains , 1968 .

[7]  Peter W Lewis,et al.  Naval Postgraduate School Random Number Generator Package LLRANDOM , 1973 .

[8]  Bill Mitchell,et al.  Variance Reduction by Antithetic Variates in GI/G/1 Queuing Simulations , 1973, Oper. Res..

[9]  Donald L. Iglehart,et al.  Approximations for the repairman problem with two repair facilities, I: No spares , 1973, Advances in Applied Probability.

[10]  Approximations for the repairman problem with two repair facilities, II: Spares , 1974, Advances in Applied Probability.

[11]  F. David,et al.  Statistical Techniques in Simulation: Part I , 1975 .

[12]  Jack P. C. Kleijnen,et al.  Statistical Techniques in Simulation , 1977, IEEE Transactions on Systems, Man and Cybernetics.

[13]  D. Iglehart,et al.  Discrete time methods for simulating continuous time Markov chains , 1976, Advances in Applied Probability.

[14]  George S. Fishman,et al.  Solution of Large Networks by Matrix Methods , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[15]  A. A. Crane,et al.  An introduction to the regenerative method for simulation analysis , 1977 .