|
WSC 2006 Abstracts |
Introductory Tutorials Track
Monday 10:30:00 AM 12:00:00 PM
Introduction to Simulation
Chair: Axel Lehmann (Universitaet der Bundeswehr
Muenchen)
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.
Monday 1:30:00 PM 3:00:00 PM
Spreadsheet Simulation
Chair:
Axel Lehmann (Universitaet der Bundeswehr Muenchen)
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.
Monday 3:30:00 PM 5:00:00 PM
Simulation Input Analysis
Chair:
Axel Lehmann (Universitaet der Bundeswehr Muenchen)
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.
Tuesday 8:30:00 AM 10:00:00 AM
Simulation Output Analysis
Chair: Axel Lehmann (Universitaet der Bundeswehr
Muenchen)
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.
Tuesday 10:30:00 AM 12:00:00 PM
Simulation Experiment Design
Chair: Axel Lehmann (Universitaet der Bundeswehr
Muenchen)
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.
Tuesday 1:30:00 PM 3:00:00 PM
Simulation Model Constuction
Chair: Tobias Kiesling (International Computer Science Institute)
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.
Tuesday 3:30:00 PM 5:00:00 PM
Simulation Practice
Chair:
Tobias Kiesling (International Computer Science
Institute)
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.
Wednesday 8:30:00 AM 10:00:00 AM
Agent-Based Simulation
Chair:
Axel Lehmann (Universitaet der Bundeswehr Muenchen)
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.
Wednesday 10:30:00 AM 12:00:00 PM
Parallel and Distributed
Simulation
Chair: Tobias Kiesling (International Computer Science
Institute)