WSC 2001 Final Abstracts

Advanced Tutorials Track

Monday 10:30:00 AM 12:00:00 PM
Simulation Mathematics and Random Number Generation

Chair: John M. Charnes (The University of Kansas)

Mathematics for Simulation
Shane G. Henderson (Cornell University)

I survey several mathematical techniques and results that are useful in the context of stochastic simulation. The concepts are introduced through the study of a simple model of ambulance operation to ensure clarity, concreteness and cohesion.

Software for Uniform Random Number Generation: Distinguishing the Good and the Bad
Pierre L'Ecuyer (Université de Montréal)

The requirements, design principles, and statistical testing approaches of uniform random number generators for simulation are briefly surveyed. An object-oriented random number package where random number streams can be created at will, and with convenient tools for manipulating the streams, is presented. A version of this package is now implemented in the Arena and Automod simulation tools. We also test some random number generators available in popular software environments such as Microsoft's Excel and Visual Basic, SUN's Java, etc., by using them on two very simple simulation problems. They fail the tests by a wide margin.

Monday 1:30:00 PM 3:00:00 PM
Verification and Validation

Chair: Heinz Weigl (ESLA)

Some Approaches and Paradigms for Verifying and Validating Simulation Models
Robert G. Sargent (Syracuse University)

In this paper we discuss verification and validation of simulation models. The different approaches to deciding model validity are described, two different paradigms that relate verification and validation to the model development process are presented, the use of graphical data statistical references for operational validity is discussed, and a recommended procedure for model validation is given.

Monday 3:30:00 PM 5:00:00 PM
Output Analysis

Chair: Christoph Roser (Toyota Central R&D Laboratories)

Output Data Analysis for Simulations
Christos Alexopoulos (Georgia Institute of Technology) and Andrew F. Seila (The University of Georgia)

This paper reviews statistical methods for analyzing output data from computer simulations of single systems. In particular, it focuses on the estimation of steady-state system parameters. The estimation techniques include the replication/deletion approach, the regenerative method, the batch means method, and the standardized time series method.

Tuesday 8:30:00 AM 10:00:00 AM
Option Pricing

Chair: Dean C. Chatfield (Virginia Tech)

Simulation in Financial Engineering
Jeremy Staum (Cornell University)

This paper presents an overview of the use of simulation algorithms in the field of financial engineering, assuming on the part of the reader no familiarity with finance and a modest familiarity with simulation methodology, but not its specialist research literature. The focus is on the challenges specific to financial simulations and the approaches that researchers have developed to handle them, although the paper does not constitute a comprehensive survey of the research literature. It offers to simulation researchers, professionals, and students an introduction to an application of increasing significance both within the simulation research community and among financial engineering practitioners.

Tuesday 10:30:00 AM 12:00:00 PM
Optimization and System Selection

Chair: Amy Jo Naylor (Corning, Inc.)

Simulation/Optimization Using “Real-World” Applications
Jay April, Fred Glover, James Kelly, and Manuel Laguna (OptTek Systems, Inc.)

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 customer relationship management.

Statistical Selection of the Best System
David Goldsman (School of Industrial & Systems Engineering) and Barry L. Nelson (Department of Industrial Engineering and Management Sciences)

This tutorial discusses some statistical procedures for selecting the best of a number of competing systems. The term "best" may refer to that simulated system having, say, the largest expected value or the greatest likelihood of yielding a large observation. We describe six procedures for finding the best, three of which assume that the underlying observations arise from competing normal distributions, and three of which are essentially nonparametric in nature. In each case, we comment on how to apply the above procedures for use in simulations.

Tuesday 1:30:00 PM 3:00:00 PM
Parallel Simulation

Chair: Gwendolyn H. Walton (University of Central Florida)

Parallel and Distributed Simulation Systems
Richard M. Fujimoto (Georgia Institute of Technology)

Originating from basic research conducted in the 1970’s and 1980’s, the parallel and distributed simulation field has matured over the last few decades. Today, operational systems have been fielded for applications such as military training, analysis of communication networks, and air traffic control systems, to mention a few. This tutorial gives an overview of technologies to distribute the execution of simulation programs over multiple computer systems. Particular emphasis is placed on synchronization (also called time management) algorithms as well as data distribution techniques.

Tuesday 3:30:00 PM 5:00:00 PM
Inside Simulation Software

Chair: Keebom Kang (Naval Postgraduate School)

Inside Discrete-Event Simulation Software: How it Works and Why it Matters
Thomas J. Schriber (University of Michigan) and Daniel T. Brunner (Systemflow Simulations, Inc.)

This paper provides simulation practitioners and consumers with a grounding in how discrete-event simulation software works. Topics include discrete-event systems; entities, resources, control elements and operations; simulation runs; entity states; entity lists; and entity-list management. The implementation of these generic ideas in AutoMod, SLX, and Extend is described. The paper concludes with several examples of "why it matters" for modelers to know how their simulation software works, including coverage of SIMAN (Arena), ProModel, and GPSS/H as well as the other three tools.

Wednesday 8:30:00 AM 10:00:00 AM
Experimental Design and Analysis

Chair: Enver Yücesan (INSEAD)

An Overview of Newer, Advanced Screening Methods for the Initial Phase in an Experimental Design
Linda Trocine (Venutek, LLC) and Linda C. Malone (University of Central Florida)

Screening is the first phase of an experimental study on systems and simulation models. Its purpose is to eliminate negligible factors so that efforts may be concentrated upon just the important ones. Successfully screening more than about 20 or 30 factors has been investigated only in the past 10 or 15 years with most improvements in the past 5 years. A handful of alternative methods including sequential bifurcation, iterated fractional factorial designs, and the Trocine Screening Procedure are described and evaluative and comparative results are presented.

Analysis of Simulation Experiments by Bootstrap Resampling
Russell C.H. Cheng (University of Southampton)

This tutorial considers some very general procedures for analysing the results of a simulation experiment using bootstrap resampling. Bootstrapping has come to be recognised in statistics as being far ranging and effective. However it is not so well known in simulation despite being ideally suited for use in such a context. We discuss aspects ranging from the elementary to the advanced. We describe the rationale and the simple steps needed to implement bootstrapping in (i) estimation of the distributional properties of the output and its dependence on factors of interest; (ii) model fitting; (iii) model selection; (iv) model validation; (v) sensitivity analysis.

Wednesday 10:30:00 AM 12:00:00 PM
System Control

Chair: Lars Randell (Lund University)

Distributed Simulation and Control: The Foundations
Wayne J. Davis (University of Illinois @ Urbana-Champaign)

This paper investigates seeks a new simulation and execution paradigm for the design and operation of complex systems. An expanded life cycle for a simulation model is first provided. It is assumed that complex systems can be represented as systems of interacting subsystems, which evolve by executing tasks upon objects. Care is taken to distinguish the real world where process execution occurs from the virtual world where planning is addressed. It is illustrated that the ideal model should be able to both evaluate and control the subsystem that it addresses. The advantages of such approach are discussed with relation to both validation and execution needs. In particular, it is demonstrated that a distributed-controller based paradigm could provide significant advantages in the evaluation of the system using distributed simulation. This form of execution is also contrasted to evolving on-line simulation requirements that will support the real-time distributed management of these systems.

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