WSC 2008 Final Abstracts

Introduction to Simulation
Ricki G. Ingalls (Oklahoma State University)

Abstract:
Simulation is a powerful tool if understood and used properly. This introduction to simulation tutorial is designed to teach the basics of simulation, including structure, function, data generated, and its proper use. The introduction starts with a definition of simulation, goes through a talk about what makes up a simulation, how the simulation actually works, and how to handle data generated by the simulation. Throughout the paper, there is discussion on issues concerning the use of simulation in industry.

Some Topics for Simulation Optimization
Michael Fu (University of Maryland), Chun-Hung Chen (George Mason University) and Leyuan Shi (University of Wisconsin)

Abstract:
We give a tutorial introduction to simulation optimization. We begin by classifying the problem setting according to the decision variables and constraints, putting the setting in the simulation context, and then summarize the main approaches to simulation optimization. We then discuss three topics in more depth: optimal computing budget allocation, stochastic gradient estimation, and the nested partitions method. We conclude by briefly discussing some related research and currently available simulation optimization software.

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.

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

Abstract:
Techniques are presented for modeling and generating the univariate probabilistic input processes that drive many simulation experiments. Emphasis is on the generalized beta distribution family, the Johnson translation system of distributions, and the Bezier distribution family. Also discussed are nonparametric techniques for modeling and simulating time-dependent arrival streams using nonhomogeneous Poisson processes. Public-domain software implementations and current applications are presented for each input-modeling technique. Many of the references include live hyperlinks providing online access to the referenced material.

Statistical Analysis of Simulation Output
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.

Better Than a Petaflop: The Power of Efficient Experimental Design
Susan M. Sanchez (OR Dept, Naval Postgraduate School)

Abstract:
Recent advances in high-performance computing have pushed computational capabilities to a petaflop (a thousand trillion operations per second) in a single computing cluster. This breakthrough has been hailed as a way to fundamentally change science and engineering by letting people perform experiments that were previously beyond reach. But for those interested in exploring the I/O behavior of their simulation model, efficient experimental design has a much higher payoff at a much lower cost. 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 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. Ideally, this tutorial will entice you to use experimental designs in your upcoming simulation studies.

Tips for Successful Practice of Simulation
David T Sturrock (Simio LLC)

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.

Introduction to Monte Carlo Simulation
Samik Raychaudhuri (Oracle Crystal Ball Global Business Unit)

Abstract:
This is an introductory tutorial on Monte Carlo simulation, a type of simulation that relies on repeated random sampling and statistical analysis to compute the results. In this paper, we will briefly describe the nature and relevance of Monte Carlo simulation, the way to perform these simulations and analyze results, and the underlying mathematical techniques required for performing these simulations. We will present a few examples from various areas where Monte Carlo simulation is used, and also touch on the current state of software in this area.

Agent-Based Modeling and Simulation: ABMS Examples
Charles Macal and Michael North (Argonne National Laboratory and The University of Chicago)

Abstract:
Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting 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 support their research. Some have gone so far as to contend that ABMS "is a third way of doing science," in addition to traditional deductive and inductive reasoning (Axelrod 1997). Computational advances have made possible a growing number of agent-based models across a variety of application domains. Applications range from modeling agent behavior in the stock market, supply chains, and consumer markets, to predicting the spread of epidemics, the threat of bio-warfare, and the factors responsible for the fall of ancient civilizations. This tutorial describes the theoretical and practical foundations of ABMS, identifies toolkits and methods for developing agent models, and illustrates the development of a simple agent-based model.