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WSC'00 |
Robust Design: Seeking the Best of All Possible
Worlds
Susan M. Sanchez (Naval Postgraduate School)
Abstract:
We describe a framework for analyzing simulation output
in order to find solutions that will work well after implementation. We show
how the use of a loss function that incorporates both system mean and system
variability can be used to efficiently and effectively carry out system
optimization and improvement efforts. For models whose behavior depends on
quantitative factors, we illustrate how robust design can be accomplished by
using simple experimental designs in conjunction with response-surface
metamodels. The results can yield new insights into system behavior, and may
lead to recommended system configurations that differ substantially from those
selected by analysis solely on the basis of mean response. We assume a
knowledge base at the level of Chapter 12 of Simulation Modeling and
Analysis (Law and Kelton, 2000) but will review essential elements and
distribute illustrative examples at the session.
Developing Industrial Strength Simulation Models
Using Visual Basic for Applications (VBA)
Marvin S. Seppanen
(Productive Systems)
Abstract:
Since 1984 the author has developed simulation models
that use input data from spreadsheets. These original applications used a
standalone Basic program to convert Lotus 123® data into Siman Experiment
Frames. While this process has evolved overtime, it did not reach a truly
viable level until Arena® 3.0 introduced Visual Basic® for Applications (VBA)
by Microsoft®. This advanced tutorial demonstrates the basic concepts
developed by the author to transfer data between Excel® and Arena. The same
techniques can be used to communicate simulation data with a wide range of VBA
supported tools, such as Access®, AutoCAD®, and Visio®. Arena permits the
model developer to use VBA as the model file is loaded, executed, or
terminated or as entities flow through the Arena model modules. This tutorial
focuses on the design of Excel workbooks for simulation applications and the
transfer of data to/from Arena using VBA.
Groupware and the Simulation
Consultant
Simon J.E. Taylor (Brunel University)
Abstract:
This paper recognises that good communication and
interaction are key factors to the success of a simulation project and
suggests that groupware technology can increase the chances of success. To
underline this, the paper reviews the process of simulation to illustrate the
amount of communication and interaction that must take place during a
simulation project. The paper then discusses computer supported cooperative
work and groupware, a research field and information technology that has
successfully supported communication and interaction in other industries. To
illustrate how groupware may by used by the simulation consultant,
net-conferencing, exemplified by Microsoft's NetMeeting, is presented. The
paper ends with some observations on the future of these applications in
simulation modelling.
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.
Output Analysis for Simulations
Christos
Alexopoulos (Georgia Institute of Technology) and Andrew F. Seila (University
of Georgia)
Abstract:
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.
Bayesian Methods for Simulation
Stephen E.
Chick (The University of Michigan)
Abstract:
This tutorial describes some ways that Bayesian methods
address problems that arise during simulation studies. This includes
quantifying uncertainty about input distributions and parameters, sensitivity
analysis, and the selection of the best of several simulated alternatives.
Focus is on illustrating the main ideas and their relevance to practical
problems. Numerous citations for both introductory and more advanced material
provide a launching pad into the Bayesian literature.
A Survey of Simulation Optimization Techniques and
Procedures
James R. Swisher (Mary Washington Hospital), Paul D.
Hyden (Cornell University), Sheldon H. Jacobson (University of Illinois at
Urbana-Champaign) and Lee W. Schruben (University of California (Berkeley))
Abstract:
Discrete-event simulation optimization is a problem of
significant interest to practitioners interested in extracting useful
information about an actual (or yet to be designed) system that can be modeled
using discrete-event simulation. This paper presents a brief survey of the
literature on discrete-event simulation optimization over the past decade
(1988 to the present). Swisher et al. (2000) provides a more comprehensive
review of this topic while Jacobson and Schruben (1989) covers the literature
preceding 1988. Optimization of both discrete and continuous input parameters
are examined herein. The continuous input parameter case is separated into
gradient and non-gradient based optimization procedures. The discrete input
parameter case differentiates techniques appropriate for small and for large
numbers of feasible input parameter values.
A Framework for Response Surface Methodology for
Simulation Optimization
H. Gonda Neddermeijer, Gerrit J. van
Oortmarssen, Nanda Piersma, and Rommert Dekker (Erasmus University Rotterdam)
Abstract:
We develop a framework for automated optimization of
stochastic simulation models using Response Surface Methodology. The framework
is especially intended for simulation models where the calculation of the
corresponding stochastic response function is very expensive or
time-consuming. Response Surface Methodology is frequently used for the
optimization of stochastic simulation models in a non-automated fashion. In
scientific applications there is a clear need for a standardized algorithm
based on Response Surface Methodology. In addition, an automated algorithm is
less time-consuming, since there is no need to interfere in the optimization
process. In our framework for automated optimization we describe the many
choices that have to be made in constructing such an algorithm.
Mathematics for Simulation
Shane G.
Henderson (University of Michigan)
Abstract:
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.
On Hybrid Combination of Queueing and Simulation
Nico M. van Dijk (University of Amsterdam / Incontrol Business
Engineers)
Abstract:
"Should we pool separate queues into a single queue or
not?" A question as practical as for daily-life situations such as at a bank,
a hospital or a service center as well as for technical applications such as
in manufacturing or telecommunications (multi-plexing). A question that
involves fundamental insights of queuing theory. A question that is still open
for research. A question that in realistic situations not only benefits from
but even requires a hybrid combination of analysis and simulation.
Using Simulation for Option Pricing
John
M. Charnes (The University of Kansas)
Abstract:
Monte Carlo simulation is a popular method for pricing
financial options and other derivative securities because of the availability
of powerful workstations and recent advances in applying the tool. The
existence of easy-to-use software makes simulation accessible to many users
who would otherwise avoid programming the algorithms necessary to value
derivative securities. This paper presents examples of option pricing and
variance reduction, and demonstrates their implementation with Crystal Ball
2000, a spreadsheet simulation add-in program.
Creating Distributed Simulation Using DEVS
M&S Environments
Bernard P. Zeigler and Hessam S. Sarjoughian
(University of Arizona)
Abstract:
We briefly review the theory of modeling and simulation
and its support for constructing distributed simulations. Formal
representation of simulation models can contribute to a number of aspects in
the modeling and simulation enterprise. Separation of models from simulation
execution engines is a prerequisite transferring model among phases of a
project as well as from project to project. The Discrete Event System
Specification (DEVS) formalism, drawing on its system theoretic basis,
provides a number of important properties such as hierarchical, modular
composition, universality and uniqueness that can support development of
simulation models and environments their development. An layered architecture
for supporting comprehensive M&S environments is discussed that unifies
the theoretical framework with implementation in distributed computational
environments.