WSC 2004 Final Abstracts

Monday 10:30:00 AM 12:00:00 PM
Introduction to Simulation
Chair: Mark Freimer (Pennsylvania State University)

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

Abstract:
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 pit-falls to avoid.

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

Abstract:
In this paper we discuss validation and verfication of simulation models. Four different approaches to deciding model validity are described; two different paradigms that relate validation and verification 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.

Building Credible Input Models
Lawrence M. Leemis (The College of William & Mary)

Abstract:
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, Fishman 2001) have a broader treatment of input modeling than presented here. Nelson and Yamnitsky (1998) survey advanced techniques.

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.

Modeling and Simulation of Complex Systems with Cell-DEVS
Gabriel A. Wainer (Carleton University)

Abstract:
Cell-DEVS enables efficient execution of complex cellular models. The goal of Cell-DEVS is to build discrete-event cell spaces, improving their definition by making the tim-ing specification more expressive. Different models built using Cell-DEVS were implemented in a modeling and simulation tool (CD++, crated following the formal speci-fications of the DEVS formalism). The applications range from biological systems to complex artificial systems. In this tutorial, we will introduce the main characteristics of Cell-DEVS, showing how to model complex cell spaces in an asynchronous environment. We will focus on the application of these techniques to improve model definition, which enables reducing development times of these models. We use a wide variety of previously defined examples in different domains of applications to illustrate the use of the techniques.

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

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.

Statistical Analysis of Simulation Output Data: The Practical State of the Art
Averill M. Law (Averill M. Law & Associates)

Abstract:
One of the most important but neglected aspects of a simulation study is the proper design and analysis of simulation experiments. In this tutorial we give a state-of-the-art presentation of what the practitioner really needs to know to be successful. We will discuss how to choose the simulation run length, the warmup-period duration (if any), and the required number of model replications (each using different random numbers). The talk concludes with a discussion of three critical pitfalls in simulation output-data analysis.

Designing Simulation Experiments
Russell R. Barton (The Pennsylvania State University)

Abstract:
Simulation models provide relatively fast and inexpensive estimates of the performance of alternative system configurations and/or alternative operating procedures. This tutorial provides some techniques for planning a set of simulation model runs, in order to gain insight on system behavior. There is an emphasis on graphical methods for planning the experiment and displaying the results.

New Advances and Applications for Marrying Simulation and Optimization
Jay April, Marco Better, Fred Glover, and James Kelly (OptTek Systems, Inc.)

Abstract:
This tutorial will focus on several new real-world applications that have been developed using an integrated set of methods, including Tabu Search, Scatter Search, Mixed Integer Programming, and Neural Networks, combined with simulation. Applications include project portfolio optimization and supply chain management.