WSC 2003

WSC 2003 Final Abstracts

Introductory Tutorials Track

Monday 10:30:00 AM 12:00:00 PM
Introduction to Simulation

Chair: Elmira Popova (The University of Texas at Austin)

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

Simulation is a powerful tool for the analysis of new system designs, retrofits to existing systems and proposed changes to operating rules. Conducting a valid simulation is both an art and a science. This paper provides an introduction to simulation and modeling and the main concepts underlying simulation. It discusses a number of key issues regarding a simulation team, how to conduct a simulation study, the skills required and the steps involved. It also provides project management guidelines and outlines pitfalls to avoid.

Monday 1:30:00 PM 3:00:00 PM
Input Modeling

Chair: Bahar Biller (Carnegie-Mellon University)

Input Modeling
Lawrence Leemis (The College of William & Mary)

Most discrete-event simulation models have stochastic elements that mimic the probabilistic nature of the system under consideration. A close match between the input model and the true underlying probabilistic mechanism associated with the system is required for successful input modeling. The general question considered here is how to model an element (e.g., arrival process, service times) in a discrete-event simulation given a data set collected on the element of interest. For brevity, it is assumed that data is available on the aspect of the simulation of interest. It is also assumed that raw data is available, as opposed to censored data, grouped data, or summary statistics. This example-driven tutorial examines introductory techniques for input modeling. Most simulation texts (e.g., Law and Kelton 2000) have a broader treatment of input modeling than presented here. Nelson and Yamnitsky (1998) survey advanced techniques.

Monday 3:30:00 PM 5:00:00 PM
Spreadsheet Simulation

Chair: Paul Sanchez (Naval Postgraduate School)

Spreadsheet Simulation
Andrew F. Seila (University of Georgia)

Spreadsheet simulation refers to the use of a spreadsheet as a platform for representing simulation models and performing the simulation experiment. 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 suggests when a spreadsheet is not an appropriate platform for simulation.

Tuesday 8:30:00 AM 10:00:00 AM
Tips for Successful Practice of Simulation

Chair: Deb Sadowski (Rockwell Software)

Tips for Successful Practice of Simulation
Deborah A. Sadowski (Rockwell Software) and Mark R. Grabau (Limited Brands)

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. We explore the most common pitfalls in performing simulation studies and identify approaches for avoiding these problems.

Tuesday 10:30:00 AM 12:00:00 PM
Verification and Validation

Chair: John Carson (Brooks-PRI Automation)

Verification and Validation of Simulation Models
Robert G. Sargent (Syracuse University)

In this paper we discuss verification and validation of simulation models. Four different approaches to deciding model validity are described; two different paradigms that relate verification and validation to the model development process are presented; various validation techniques are defined; conceptual model validity, model verification, operational validity, and data validity are discussed; a way to document results is given; a recommended procedure for model validation is presented; and accreditation is briefly discussed.

Tuesday 1:30:00 PM 3:00:00 PM
Output Analysis

Chair: Roberto Szechtman (Naval Postgraduate School)

Analysis of Simulation Output
Marvin K. Nakayama (New Jersey Institute of Technology)

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

Tuesday 3:30:00 PM 5:00:00 PM
Experimental Design for Simulation

Chair: Tom Cioppa (US Army TRADOC)

Experimental Design for Simulation
W. David Kelton (University of Cincinnati) and Russell R. Barton (The Pennsylvania State University)

This tutorial introduces some of the ideas, issues, challenges, solutions, and opportunities in deciding how to experiment with simulation models to learn about their behavior. Careful planning, or designing, of simulation experiments is generally a great help, saving time and effort by providing efficient ways to estimate the effects of changes in the model’s inputs on its outputs. Traditional experimental-design methods are discussed in the context of simulation experiments, as are the broader questions pertaining to planning computer-simulation experiments.

Wednesday 8:30:00 AM 10:00:00 AM
Designing a Simulation Study

Chair: Averill Law (Law & Associates)

How to Conduct a Successful Simulation Study
Averill M. Law (Averill M. Law & Associates, Inc.)

In this tutorial we give a definitive and comprehensive seven-step approach for conducting a successful simulation study. Topics to be discussed include problem formulation, collection and analysis of data, developing a valid and credible model, modeling sources of system randomness, design and analysis of simulation experiments, and project management.

Wednesday 10:30:00 AM 12:00:00 PM
Simulation-Based Optimization

Chair: Nilay Argon (University of Wisconsin)

Practical Introduction to Simulation Optimization
Jay April, Fred Glover, James P. Kelly, and Manuel Laguna (OptTek Systems)

The merging of optimization and simulation has seen a rapid growth in recent years. A Google search on “Simulation Optimization” returns more than six thousand pages where this phrase appears. The content of these pages ranges from articles, conference presentations and books to software, sponsored work and consultancy. This is an area that has sparked as much interest in the academic world as in practical settings. In this paper, we first summarize some of the most relevant approaches that have been developed for the purpose of optimizing simulated systems. We then concentrate on the metaheuristic black-box approach that leads the field of practical applications and provide some relevant details of how this approach has been implemented and used in commercial software. Finally, we present an example of simulation optimization in the context of a simulation model developed to predict performance and measure risk in a real world project selection problem.

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