WSC 2005 Final Abstracts

Monday 10:30:00 AM 12:00:00 PM
Agent-based Modeling
Chair: Jeff Hong (Hong Kong University of Science and Technology)

Tutorial on Agent-Based Modeling and Simulation
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 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 besides deductive and inductive reasoning. Computational advances have made possible a growing number of agent-based applications in a variety of 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 consumer behavior to understanding the fall of ancient civilizations, to name a few. This tutorial describes the theoretical and practical foundations of ABMS, identifies toolkits and methods for developing ABMS models, and provides some thoughts on the relationship between ABMS and traditional modeling techniques.

Introduction to Modeling and Simulation
John S. Carson II (Brooks Automation)

Abstract:
Simulation is a powerful tool for the evaluation and analysis of new system designs, modifications to existing systems and proposed changes to control systems and operating rules. Conducting a valid simulation is both an art and a science. This paper provides an introduction to discrete-event simulation and the main concepts – system state, events, processes – underlying simulation. It discusses the major world views used by simulation software. It includes a brief discussion of a number of other important issues: the advantages and disadvantages of using a simulation model, the skills required to develop a simulation model, the key steps in conducting a simulation study, as well as some project management guidelines and pitfalls to avoid.

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

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 conceptual model, structured walk-through of the conceptual model, 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.

Tuesday 8:30:00 AM 10:00:00 AM
Spreadsheet Simulation
Chair: Hong Wan (Purdue University)

Spreadsheet Simulation
Andrew F. Seila (The 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
Emily K. Lada (SAS Insitute, Inc.), Natalie M. Steiger (University of Maine), Mary Ann Wagner (SAIC) and James R. Wilson (NC 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.

Tips for Successful Practice of Simulation
Deborah A. Sadowski (Rockwell Software)

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 ap-proaches for avoiding these problems.

Tuesday 3:30:00 PM 5:00:00 PM
Simulation Debugging
Chair: Sujin Kim (Cornell University)

Debugging Simulation Models
David Krahl (Imagine That, Inc.)

Abstract:
While much has been written about model validation and verification, the actual process of correcting, or debugging, a model is presented as an afterthought. This paper will describe different types of bugs and will present techniques, drawn from both the simulation modeling and application programming worlds, for determining the cause of an error in the model.

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 could not 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 an understanding of a complex simulation model and gain insights into its behavior. Designs that 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.

Simulation Optimization: A Review, New Developments, and Applications
Michael C. Fu (University of Maryland), Fred Glover (University of Colorado) and Jay April (OptTek Systems, Inc.)

Abstract:
We provide a descriptive review of the main approaches for carrying out simulation optimization, and sample some recent algorithmic and theoretical developments in simulation optimization research. Then we survey some of the software available for simulation languages and spreadsheets, and present several illustrative applications.