This paper reviews statistical methods for analyzing output data from computer simulations. Specifically, it focuses on the estimation of steady-state system parameters. The estimation techniques include the replication/deletion approach, the regenerative method, the batch means method, and methods based on standardized time series.

This article gives an overview of a framework for automatically generating large-scale simulation models from a domain specific problem definition data schema, here semiconductor manufacturing. This simulation model uses an object-oriented Petri net data structure. The Petri net based simulation uses the same enabling rules as classical Petri nets, but has extensions of time and priorities. This approach minimizes the effort of model verification. Each object identified in the problem data specification is mapped to corresponding Petri net fragments. The Petri net simulation model is synthesized from verifiable subnets. This allows ensuring the liveness of the final Petri net simulation model. The applicability of this approach is demonstrated by generating a simulation model based on the Sematech data set.

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Output analysis methods that provide reliable point and confidence-interval estimators for system performance characteristics are critical elements of any modern simulation project. Remarkable advances in simulation output analysis have been achieved over the last thirty years, in part owing to the application of data-reuse techniques designed to improve estimator accuracy and efficiency. Many of the key insights regarding data reuse are given in the seminal 1984 Winter Simulation Conference paper by Meketon and Schmeiser that is titled "Overlapping Batch Means: Something for Nothing?" and that introduced the method of overlapping batch means (OBM). We trace the development of OBM from the original work of Meketon and Schmeiser, and we discuss some recent extensions of the method.

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