Management Science
Steady-state simulation of queueing processes: survey of problems and solutions
ACM Computing Surveys (CSUR)
Asymptotic formulas for Markov processes with applications to simulation
Operations Research
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Simulation: The Practice of Model Development and Use
Simulation: The Practice of Model Development and Use
Initial bias and estimation error in discrete event simulation
WSC '82 Proceedings of the 14th conference on Winter Simulation - Volume 2
The art of simulation
Warm-up periods in simulation can be detrimental
Probability in the Engineering and Informational Sciences
Proceedings of the 40th Conference on Winter Simulation
Interval estimation using replication/deletion and MSER truncation
Proceedings of the Winter Simulation Conference
Rethinking the initialization bias problem in steady-state discrete event simulation
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
Performance comparison of MSER-5 and N-Skart on the simulation start-up problem
Proceedings of the Winter Simulation Conference
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In this paper, we discuss the factors affecting the initialization bias in discrete event simulation. Specifically, we assume that the time average is used to find the equilibrium expectation of a certain variable R, say the number in a queueing network, and we would like to minimize the mean squared error (MSE) between the time average of R and its equilibrium expectation. To do this, a warm-up period is often used during which no data is collected, and we want to find the length of this period such that the MSE is minimal. We show that if starting in what Tocher calls a 芒聙聹typical condition芒聙聺, warm-ups tend to be redundant. This result is strengthened by theoretical arguments and numerical experiments. If starting in a typical state is inconvenient, warm-up periods should be used, and methods to find optimal warm-up periods are discussed. The numerical methods used for our experiments do not rely on Monte Carlo simulation. Instead, we determine the MSE of the time average by the randomization method and other deterministic methods.