Lee W. Schruben

Simulation modeling with event graphs

This research was partially supported by National Science Foundation Grant ECS-8023177. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission.

Confidence Interval Estimation Using Standardized Time Series

Observations of a stationary stochastic process can be transformed into a standardized time series. This paper presents a lemma giving the asymptotic properties of this standardized series under quite general conditions. In particular, the conditions are satisfied by stationary discrete-event simulations. Confidence intervals can be constructed using this lemma. For illustration, we develop two easily computed interval estimators for the process mean. When independent replications of the series are available, such as in computer simulation experiments, these interval estimators may be combined with the classical confidence interval estimator. These interval estimators also tend to compensate for simulation initialization bias if the sign of the bias is known. In an empirical study using three elementary simulated processes, the interval estimators presented here compare favorably with the classical interval estimator. In a recent paper, Goldsman and Schruben Goldsman, D., L. Schruben. 1982. Asymptotic properties of some confidence interval estimators. Tech. Rep. 544, School of O.R.I.E., Cornell University, Ithaca, NY. show that the asymptotic properties of the confidence intervals presented in this paper strictly dominate those of classical confidence intervals.

Detecting Initialization Bias in Simulation Output

A general approach to testing for initialization bias in the mean of a simulation output series is presented. The output is transformed into a standardized test sequence that can be contrasted with a known limiting stochastic process. This transformation requires very little computation and the asymptotic theory is applicable to a wide variety of simulations. An initialization bias test is developed and several examples of its application are presented.

Pseudorandom Number Assignment in Statistically Designed Simulation and Distribution Sampling Experiments

Abstract This research investigates various strategies for assigning pseudorandom numbers to experimental points in statistically designed simulation and distribution sampling experiments. Strategies studied include the widely advocated practices of (i) employing a common set of pseudorandom numbers for all experimental points, and (ii) assigning a unique set of pseudorandom numbers to each experimental point. An alternative, based upon blocking concepts in designed experiments, is devised and shown to improve upon existing recommendations for a wide class of problems. A small simulation, a pilot study of a hospital resource allocation problem, illustrates the new strategy.

Optimal Tests for Initialization Bias in Simulation Output

We present a family of tests for detecting initialization bias in the mean of a simulation output series using a hypothesis testing framework. The null hypothesis is that the output mean does not change throughout the simulation run. The alternative hypothesis specifies a general transient mean function. The tests are asymptotically optimal based on cumulative sums of deviations about the sample mean. A particular test in this family is applied to a variety of simulation models. The test requires very modest computation and appears to be both robust and powerful.

Techniques for simulation response optimization

Techniques for discrete event simulation optimization are classified into four groups: path search methods, pattern search methods, random methods, and integral methods. Each class is described and a survey of recent literature is presented.

Establishing the credibility of simulations

A procedure is proposed to promote the acceptance of a simulation model. The procedure actively involves potential users of the simulation. Several alterna tive approaches for the statistical analysis of the experimental results are suggested. Two contrasting experiences in applying the procedure to actual simulation projects are discussed.

An experimental procedure for simulation response surface model identification

An experimental method for identifying an appropriate model for a simulation response surface is presented. This technique can be used for globally identifying those factors in a simulation that have a significant influence on the output. The experiments are run in the frequency domain. A simulation model is run with input factors that oscillate at different frequencies during a run. The functional form of a response surface model for the simulation is indicated by the frequency spectrum of the output process. The statistical significance of each term in a prospective response surface model can be measured. Conditions are given for which the frequency domain approach is equivalent to ranking terms in a response surface model by their correlation with the output. Frequency domain simulation experiments typically will require many fewer computer runs than conventional run-oriented simulation experiments.

A survey of recent advances in discrete input parameter discrete-event simulation optimization

Discrete-event simulation optimization is a problem of significant interest to practitioners interested in extracting useful information about an actual (or yet to be designed) system that can be modeled using discrete-event simulation. This paper presents a survey of the literature on discrete-event simulation optimization published in recent years (1988 to the present), with a particular focus on discrete input parameter optimization. The discrete input parameter case differentiates techniques appropriate for small and for large numbers of feasible input parameter values. Examples of applications that illustrate these methods are also discussed.

A Guide to Simulation.

Bratley, Fox, and Schrage’s A Guide to Simulation provides practical recommendations for both the novice and the experienced simulationist, without insulting the reader’s intelligence. It does this with a text that is readable, mathematically precise, and comprehensive enough so that it touches on the majority of concerns which arise in a simulation project. Despite the brevity of the book (only 287 pages of text), its mathematical notation, and the problems which it poses without solutions, the textbook is imbued with a feeling for the &dquo;nitty-gritty&dquo; practical aspects of simulation. The authors generously present many helpful hints, suggestions, recommendations, and caveats gleaned from practical experience.