WSC 2006 Abstracts

As Simple As Possible, But No Simpler: A Gentle Introduction to Simulation Modeling
Paul J. Sanchez (Naval Postgraduate School)

Abstract:
We start with basic terminology and concepts of modeling, and decompose the art of modeling as a process. This overview of the process helps clarify when we should or should not use simulation models. We discuss some common missteps made by many inexperienced modelers, and propose a concrete approach for avoiding those mistakes. After a quick review of event graphs, which are a very straightforward notation for discrete event systems, we illustrate how an event graph can be translated quite directly to a computer program with the aid of a surprisingly simple library. The resulting programs are easy to implement and computationally are extremely efficient. The first half of the paper focuses principles of modeling, and should be of general interest. The second half will be of interest to students, teachers, and readers who wish to know how simulation models work and how to implement them from the ground up.

Spreadsheet Simulation
Andrew F. Seila (University of Georgia)

Abstract:
Spreadsheet simulation refers to the use of a spreadsheet as a platform for representing simulation models and performing simulation experiments. This tutorial explains the reasons for using this platform for simulation, discusses why this is frequently an efficient way to build simulation models and execute them, describes how to setup a spreadsheet simulation, and finally examines some limitations on the use of spreadsheets for simulation.

Introduction to Modeling and Generating Probabilistic Input Processes for Simulation
Michael E. Kuhl (Rochester Institute of Technology), Emily K. Lada (SAS), Natalie M. Steiger (University of Maine), Mary Ann Wagner (SAIC) and James R. Wilson (North Carolina State University)

Abstract:
Techniques are presented for modeling and generating the univariate and multivariate probabilistic input processes that drive many simulation experiments. Among univariate input models, emphasis is given to the generalized beta distribution family, the Johnson translation system of distributions, and the Bezier distribution family. Among bivariate and higher-dimensional input models, emphasis is given to computationally tractable extensions of univariate Johnson distributions. Also discussed are nonparametric techniques for modeling and simulating time-dependent arrival streams using nonhomogeneous Poisson processes.

Output Analysis for Simulations
Marvin Nakayama (New Jersey Institute of Technology)

Abstract:
We discuss methods for statistically analyzing the output from stochastic discrete-event or Monte Carlo simulations. Terminating and steady-state simulations are considered.

Work Smarter, Not Harder: Guidelines for Designing Simulation Experiments
Susan M. Sanchez (Naval Postgraduate School)

Abstract:
We present the basic concepts of experimental design, the types of goals it can address, and why it is such an important and useful tool for simulation. A well-designed experiment allows the analyst to examine many more factors than would otherwise be possible, while providing insights that cannot be gleaned from trial-and-error approaches or by sampling factors one at a time. We focus on experiments that can cut down the sampling requirements of some classic designs by orders of magnitude, yet make it possible and practical to develop a better understanding of a complex simulation model. Designs we have found particularly useful for simulation experiments are illustrated using simple simulation models, and we provide links to other resources for those wishing to learn more. Ideally, this tutorial will leave you excited about experimental designs - and prepared to use them - in your upcoming simulation studies.

How to Build Valid and Credible Simulation Models
Averill M. Law (Averill M. Law & Associates)

Abstract:
In this tutorial we present techniques for building valid and credible simulation models. Ideas to be discussed include the importance of a definitive problem formulation, discussions with subject-matter experts, interacting with the decision-maker on a regular basis, development of a written assumptions document, structured walk-through of the assumptions document, use of sensitivity analysis to determine important model factors, and comparison of model and system output data for an existing system (if any). Each idea will be illustrated by one or more real-world examples. We will also discuss the difficulty in using formal statistical techniques (e.g., confidence intervals) to validate simulation models.

Tips for the Successful Practice of Simulation
Deborah A Sadowski and David T. Sturrock (Rockwell Automation)

Abstract:
Succeeding with a technology as powerful as simulation involves much more than the technical aspects you may have been trained in. The parts of a simulation study that are outside the realm of modeling and analysis can make or break the project. This paper explores the most common pitfalls in performing simulation studies and identifies approaches for avoiding these problems.

Tutorial on Agent-based Modeling and Simulation Part 2: How to Model with Agents
Charles M. Macal and Michael J. North (Argonne National Laboratory)

Abstract:
Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of interacting autonomous agents. ABMS promises to have far reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to do research. Some have gone so far as to contend that ABMS is a new way of doing science. Computational advances make possible a growing number of agent-based applications across many fields. Applications range from modeling agent behavior in the stock market and supply chains, to predicting the spread of epidemics and the threat of bio-warfare, from modeling the growth and decline of ancient civilizations to modeling the complexities of the human immune system, and many more. This tutorial describes the foundations of ABMS, identifies ABMS tool-kits and development methods illustrated through a supply chain example, and provides thoughts on the appropriate contexts for ABMS versus conventional modeling techniques.

Parallel and Distributed Simulation: Traditional Techniques and Recent Advances
Kalyan Perumalla (Oak Ridge National Laboratory)

Abstract:
This tutorial on parallel and distributed simulation systems reviews some of the traditional synchronization techniques and presents some recent advances.