WSC 2002 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.

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 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 examines when a spreadsheet is not an appropriate platform for simulation.

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

Abstract:
We discuss methods for statistically analyzing the output from stochastic simulations. Both terminating and steady-state simulations are considered.

Answers to the Top Ten Input Modeling Questions
Bahar Biller (Carnegie Mellon University) and Barry L. Nelson (Northwestern University)

Abstract:
In this tutorial we provide answers to the top ten input-modeling questions that new simulation users ask, point out common mistakes that occur and give relevant references. We assume that commercial input-modeling software will be used when possible, and only suggest non-commercial options when there is little else available. Detailed examples will be provided in the tutorial presentation.

Simulation-Based Optimization
Averill M. Law and Michael G. McComas (Averill M. Law & Associates)

Abstract:
In this tutorial we present an introduction to simulation-based optimization, which is, perhaps, the most important new simulation technology in the last five years. We give a precise statement of the problem being addressed and also experimental results for two commercial optimization packages applied to a manufacturing example with seven decision variables.

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

Abstract:
Simulation models are useful for examining the performance of alternative system configurations and/or alternative operating procedures for a system. 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.

Model Verification and Validation
John S. Carson, II (Brooks-PRI Automation)

Abstract:
In this paper we outline practical techniques and guidelines for verifying and validating simulation models. The goal of verification and validation is a model that is accurate when used to predict the performance of the real-world system that it represents, or to predict the difference in performance between two scenarios or two model configurations. The process of verifying and validating a model should also lead to improving a model’s credibility with decision makers. We provide examples of a number of typical situations where model developers may make inappropriate or inaccurate assumptions, and offer guidelines and techniques for carrying out verification and validation.

Supply Chain Analysis: Spreadsheet or Simulation?
Leonardo Chwif (Simulate), Marcos Ribeiro Pereira Barretto (Mechatronics Lab) and Eduardo Saliby (Cel-Coppead)

Abstract:
In the last few decades, a lot of company effort has been spent in the optimization of internal efficiency, aiming at cost reduction and competitiveness. Especially over the last decade, there has been a consensus that not only the company, but the whole supply chain in which it fits, is responsible for the success or failure of any business. Therefore, supply chain analysis tools and methodologies have become more and more important. From all tools, spreadsheets are by far the most widely used technique for scenario analysis. Other techniques such as optimization, simulation or both (simulation-optimization) are alternatives for in-depth analysis. While spreadsheet-based analysis is mainly a static-deterministic approach, simulation is a dynamic-stochastic tool. The purpose of this paper is to compare spreadsheet-based and simulation-based tools showing the impact of using these two different approaches on the analysis of a real (yet simplified) supply chain case study.

An Evaluation and Selection Methodology for Discrete-Event Simulation Software
Tamrat W. Tewoldeberhan, Alexander Verbraeck, and Edwin Valentin (Delft University of Technology) and Gilles Bardonnet (Accenture)

Abstract:
For large international companies with their own simulation team it is often hard to select new discrete event simulation software. Often, preferences and application areas between countries differ, and simulation software already in use influences the outcome of the selection process. Available selection methods do not suffice in such cases. Therefore, a two-phase evaluation and selection methodology is proposed. Phase one quickly reduces the long list to a short list of packages. Phase two matches the requirements of the company with the features of the simulation package in detail. Different methods are used for a detailed evaluation of each package. Simulation software vendors participate in both phases. The approach was tested for the Accenture world-wide simulation team. After the study, we can conclude that the methodology was effective in terms of quality and efficient in terms of time. It can easily be applied for other large organizations with a team of simulation specialists.