WSC 2003 Final Abstracts

Quasi-Monte Carlo Methods for Simulation
Pierre L'Ecuyer (University of Montreal)

Abstract:
Quasi-Monte Carlo (QMC) methods are numerical techniques for estimating large-dimensional integrals, usually over the unit hypercube. They can be applied, at least in principle, to any simulation whose aim is to estimate a mathematical expectation. This covers a very wide range of applications. In this paper, we review some of the key ideas of quasi-Monte Carlo methods from a broad perspective, with emphasis on some recent results. We visit lattice rules in different types of spaces and make the connections between these rules and digital nets, thus covering the two most widely used QMC methods.

Input Model Uncertainty: Why Do We Care and What Should We Do About It?
Shane G. Henderson (Cornell University)

Abstract:
An input model is a collection of distributions together with any associated parameters that are used as primitive inputs in a simulation model. Input model uncertainty arises when one is not completely certain what distributions and/or parameters to use. This tutorial attempts to provide a sense of why one should consider input uncertainty and what methods can be used to deal with it.

Selecting the Best System: Theory and Methods
Seong-Hee Kim (Georgia Institute of Technology) and Barry L. Nelson (Northwestern University)

Abstract:
This paper provides an advanced tutorial on the construction of ranking-and-selection procedures for selecting the best simulated system. We emphasize procedures that provide a guaranteed probability of correct selection, and the key theoretical results that are used to derive them.

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

Abstract:
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.

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

Abstract:
An overview of technologies concerned with distributing the execution of simulation programs across multiple processors is presented. Here, particular emphasis is placed on discrete event simulations. The High Level Architecture (HLA) developed by the Department of Defense in the United States is first described to provide a concrete example of a contemporary approach to distributed simulation. The remainder of this paper is focused on time management, a central issue concerning the synchronization of computations on different processors. Time management algorithms broadly fall into two categories, termed conservative and optimistic synchronization. A survey of both conservative and optimistic algorithms is presented focusing on fundamental principles and mechanisms. Finally, time management in the HLA is discussed as a means to illustrate how this standard supports both approaches to synchronization.

Call Center Simulation Modeling: Methods, Challenges, and Opportunities
Vijay Mehrotra (San Francisco State University) and Jason Fama (Blue Pumpkin Software Inc.)

Abstract:
Using stochastic models to plan call center operations, schedule call center staff efficiently, and analyze projected performance is not a new phenomenon, dating back to Erlang's work in the early twentieth century. However, several factors have recently conspired to increase demand for call center simulation analysis. In this tutorial, we will provide an overview of call center simulation models, highlighting typical inputs and data sources, modeling challenges, and key model outputs. In the process, we will also present an interesting “real-world” example of effective use of call center simulation.

Control Variates Techniques for Monte Carlo Simulation
Roberto Szechtman (Naval Postgraduate School)

Abstract:
In this paper we present an overview of classical results about the variance reduction technique of control variates. We emphasize aspects of the theory that are of importance to the practitioner, as well as presenting relevant applications.

Verification, Validation, and Certification of Modeling and Simulation Applications
Osman Balci (Virginia Tech)

Abstract:
Certifying that a large-scale complex modeling and simulation (M&S) application can be used for a set of specific purposes is an onerous task, which involves complex evaluation processes throughout the entire M&S development life cycle. The evaluation processes consist of verification and validation activities, quality assurance, assessment of qualitative and quantitative elements, assessments by subject matter experts, and integration of disparate measurements and assessments. Planning, managing, and conducting the evaluation processes require a disciplined life-cycle approach and should not be performed in an ad hoc manner. The purpose of this tutorial paper is to present structured evaluation processes throughout the entire M&S development life cycle. Engineers, analysts, and managers can execute the evaluation processes presented herein to be able to formulate a certification decision for a large-scale complex M&S application.

Advanced Event Scheduling Methodology
Lee W. Schruben, Theresa M. Roeder, and Wai Kin Chan (University of California, Berkeley), Paul Hyden (Clemson University) and Mike Freimer (The Pennsylvania State University)

Abstract:
Simulation Event Graphs (SEGs) are a graphical representation one of the three major simulation world views, event scheduling. This paper describes four advanced modeling techniques that allow the simulation practitioner to gather a great deal of information at relatively little development and/or processing effort beyond that of developing the simulation model.