When the simulation department of Management Science was created in 1978 it ushered in an era of significant methodological advances in stochastic simulation. However, the foundation for the field---not just the work that has been published in Management Science---was provided by two papers...

A supersaturated design is a factorial design in which the number of factors to be estimated is larger than the available number of experimental runs. The cost and time required for many industrial experimentations can be reduced by using the class of supersaturated designs, since the main goal...

In the study of large, complex computer simulation models the user is often overwhelmed by the vast number of input variables. Moreover, he or she is usually confused about how to make an effective analysis of the model without performing an excessive number of runs, which tend to be costly and...

We present a general recipe for constructing experiment design and analysis procedures that simultaneously provide indifference-zone selection and multiple-comparison inference for choosing the best among k simulated systems. We then exhibit two such procedures that exploit the...

A supersaturated design is a factorial design in which the number of effects to be estimated is greater than the number of runs. It is used in many experiments, for screening purpose, i.e., for studying a large number of factors and identifying the active ones. In this paper, we propose a method...

Comparison with a standard is a general multiple comparison problem, where each system is required to be compared to a single system, referred to as a “standard”, as well as to other alternative systems. The goal is to determine the best system among a number of systems that are better than...

Design Of Experiments (DOE) is needed for experiments with real-life systems, and with either deterministic or random simulation models. This contribution discusses the different types of DOE for these three domains, but focusses on random simulation. DOE may have two goals: sensitivity analysis...

Data truncation is a commonly accepted method of dealing with initialization bias in discrete-event simulation. An algorithm for determining the appropriate initial-data truncation point for univariate output is proposed. The technique entails averaging across independent replications and...

This paper presents a procedure for determining the number of simulation observations required to achieve a preassigned confidence interval for means estimated by simulation. This procedure, which is simple to implement and efficient to use, is compared with two other methods for determining the...