WSC 2001 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.

Challenges of Introducing Simulation as a Decision Making Tool
Martha A. Centeno (Florida International University) and Manuel Carrillo (Jackson Memorial Hospital)

Abstract:
Over the years, simulation models have been successfully built to observe the behavior of systems. Despite advances in the field and its growth in popularity, when simulation is to be introduced to an organization, there are challenges to be met including acceptance by staff, availability of staff to describe the various operations, existence of useful data, and management expectations. Organizations are continuously collecting data, which may lead one to believe that developing stochastic models of an organization’s activities should be easy. However, elicitation of useful information may end up being a major bottleneck because usually the information system collecting such data is not designed for stochastic modeling. Unrealistic management expectations may result in simulation modeling being thrown away when these expectations are not met. Success in introducing simulation modeling will depend heavily on how well these challenges are addressed and managed.

How to Build Valid and Credible Simulation Models
Averill M. Law and Michael G. McComas (Averill 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 conceptual model, structured walk-through of the conceptual model, use of sensitivity analysis to determine important model factors, and comparison of model and system performance measures 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.

ABC’s of Output Analysis
Susan M. Sanchez (Naval Postgraduate School)

Abstract:
We present a brief overview of several of the basic output analysis techniques for evaluating stochastic dynamic simulations. This tutorial is intended for those with little previous exposure to the topic, for those in need of a refresher course, and especially for those who have never heard of output analysis. We discuss the reasons why simulation output analysis differs from that taught in basic statistics courses and point out how to avoid common pitfalls that may lead to erroneous results and faulty conclusions.

Some Myths and Common Errors in Simulation Experiments
Bruce W. Schmeiser (Purdue University)

Abstract:
During the more than fifty years that Monte Carlo simulation experiments have been performed on digital computers, a wide variety of myths and common errors have evolved. We discuss some of them, with a focus on probabilistic and statistical issues.

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.

Simulation Optimization
Michael C. Fu (University of Maryland)

Abstract:
In this tutorial introduction to simulation optimization, we present motivating and illustrative examples, summarize most of the major approaches, and briefly describe some software implementations. The focus is on issues and concepts, rather than mathematical rigor, so the format is Q & A rather than theorem-proof.

Input Modeling Techniques for Discrete-Event Simulations
Lawrence 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) have a broader treatment of input modeling than presented here. Nelson and Yamnitsky (1998) survey advanced techniques.

Spreadsheet Simulation
Andrew F. Seila (Department of MIS)

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, discusses how to setup a spreadsheet simulation, and finally examines when a spreadsheet is not an appropriate platform for simulation.