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DSS News
D. J. Power, Editor
July 6, 2003 -- Vol. 4, No. 14
A Bi-Weekly Publication of DSSResources.COM
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Check the case on WEBSDSS for Environmental Planning and
Management by Sugumaran and Meyer at DSSResources.COM
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Featured:
* Ask Dan! - How can simulation be used for decision support?
* DSS News Releases
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Ask Dan!
by Dan Power
How can simulation be used for decision support?
Questions about using simulation for building a DSS are reasonably
frequent in my Ask Dan! email. So this column has been in the works for
some time, but my summer research project on advanced decision and
planning support motivated me to move this column to the "front burner".
Coincidentally, I received an email on Friday, July 4, 2003 from John
Walker ( http://jbwalker.com ). John wrote "I appreciate your
newsletter. Keep 'em coming!" Thanks for the positive feedback. Also,
John thought I might be interested in a June 16, 2003 interview with
Eric Bonabeau at CIOInsight.com. Eric is the founder of Icosystem Corp.,
Cambridge, MA ( http://Icosystem.com ). Icosystem develops agent-based
models and simulations. Agent-based or multi-agent simulations are the
"latest and greatest" technology or approach in the simulation toolkit.
Before I discuss agent-based simulations, let's review the basics of
simulation. According to a number of sources, simulation is the most
frequently used quantitative approach for solving business problems and
supporting business decision making. That generalization may be true,
but simulation is still the province of management science
"specialists". Simulation has not been made "manager friendly".
Simulation is a broad term that refers to an approach for imitating the
behavior of an actual or anticipated human or physical system. The terms
simulation and model, especially quantitative and behavioral models, are
closely linked. From my perspective, a model shows the relationships and
attributes of interest in the system under study. A quantitative or
behavioral model is by design a simplified view of some of the objects
in a system. A model used in a simulation can capture much detail about
a specific system, but how complex the model is or should be depends
upon the purpose of the simulation that will be "run" using the model.
With a simulation study and when simulation provides the functionality
for a DSS, multiple tests, experiments or "runs" of the simulation are
conducted, the results of each test are recorded and then the aggregate
results of the tests are analyzed to try to answer specific questions.
In a simulation, the decision variables in the model are the inputs that
are manipulated in the tests.
In my DSS book (Power, 2002), Chapter 10 on Building Model-Driven
Decision Support Systems notes "In a DSS context, simulation generally
refers to a technique for conducting experiments with a computer-based
model. One method of simulating a system involves identifying the
various states of a system and then modifying those states by executing
specific events. A wide variety of problems can be evaluated using
simulation including inventory control and stock-out, manpower planning
and assignment, queuing and congestion, reliability and replacement
policy, and sequencing and scheduling (p. 172)."
There are several types of simulation and a variety of terms are used to
identify them. When you read about simulation you will find references
to Monte Carlo simulation, traditional mathematical simulation,
activity-scanning simulation, event-driven simulation, process-based
model simulation, real-time simulation, data-driven simulation,
agent-based and multi-agent simulation, time dependent simulation,
andvisual simulation.
In a Monte Carlo or probabilistic simulation one or more of the
independent variables is specified as a probability distribution of
values. A probabilistic simulation helps take risk and uncertainty in a
system into account in the results. Time dependent or discrete
simulation refers to a situation where it is important to know exactly
when an event occurs. For example, in waiting line or queuing problems,
it is important to know the precise time of arrival to determine if a
customer will have to wait or not. According to Evan and Olson (2002)
and others, activity-scanning simulation models involve describing
activities that occur during a fixed interval of time and then
simulating for multiple future periods the consequences of the
activities while process-driven simulation focuses on modeling a logical
sequence of events rather than activities. An event-driven simulation
also identifies "events" that occur in a system, but the focus is on a
time ordering of the events rather than a causal or logical ordering.
Simulation can assist in either a static or a dynamic analysis of a
system. A dynamic analysis is enhanced with software that shows the
time sequenced operation of the system that is being predicted or
analyzed. Simulation is a descriptive tool that can be used for both
prediction and exploration of the behavior of a specific system. A
complex simulation can help a decision maker plan activities, anticipate
the effects of specific resource allocations and assess the consequences
of actions and events. In a business simulation course, text materials
usually focus on static, Monte-Carlo simulations and dynamic, system
simulations (cf., Evan and Olson, 2002).
In many situations simulation specialists build a simulation and then
conduct the special study and report their results to management. Evans
and Olson (2002) discuss examples of how simulation has been used to
support business and engineering decision making. They report a number
of special decision support studies including one that evaluated the
number of Hotel reservations to accept to effectively utilize capacity
to create an overbooking policy (p. 161-163), a Call Center staffing
capacity analysis (p. 163-165), a study comparing new incinerating
system options for a municipal garbage recycling center (p. 176-179), a
study evaluating government policy options, and various studies for
designing facilities.Examples of model-driven DSS built with a
simulation as the dominant component include: a Monte Carlo simulation
to manage foreign-exchange risks; a spreadsheet-based DSS for assessing
the risk of commercial loans (cf., Decisioneering Staff, 2001), a DSS
for developing a weekly production schedule for hundreds of products at
multiple plants; a program for estimating returns for fixed-income
securities; and a simulation program for setting bids for competitive
lease sales (cf., Evan and Olson, p. 190).
Sometimes in an effort to provide decision support an actual small-scale
model or ecosystem is built and then it is "used in a simulated
environment". For example, a physical model of an airplane may be built
so that it can be tested in a wind tunnel to examine its design
properties. Today a computer simulation might be used in place of a
"physical model" for much of the design testing. The case "Product
development decision support at Lockheed Martin" by Silicon Graphics
Staff posted at DSSResources.COM October 16, 2002 is an example of this
use of simulation.
Agent-based or multi-agent simulation does not replace any of the
traditional simulation techniques. But in the last 5 years, agent-based
visual simulations have become an alternative approach for analyzing
some business systems. According to Bonabeau, "People have been thinking
in terms of agent-based modeling for many years but just didn't have the
computing power to actually make it useful until recently. With
agent-based modeling, you describe a system from the bottom up, from the
point of view of its constituent units, as opposed to a top-down
description, where you look at properties at the aggregate level without
worrying about the system's constituent elements."
Multi-agent simulations can be used to simulate some natural and
man-created systems that traditional simulation techniques can not.
Bonabeau asserts agent-based modeling works best in situations where a
system is "comprised of many constituent units that interact and where
the behavior of the units can be described in simple terms. So it's a
situation where the complexity of the whole system emerges out of
relatively simple behavior at the lowest level." Examples of such
systems include shoppers in a grocery store, passengers, visitors and
employees at an airport or production workers and supervisors at a
factory. What is the objective of an agent-based simulation? According
to Bonabeau, "the objective is to find a robust solution" -- one that
will work fine no matter what happens in the "real world".
A simulation study can answer questions like how many teller stations
will provide 90% confidence that no one will need to wait in line for
more than 5 minutes or how likely is it that a specific project will be
completed on time and under budget? With a visual simulation decision
makers or analyst can observe an airplane in a wind tunnel, a proposed
factory in operation or customers entering a new bank or a construction
project as "it will occur".
Based on my observations over the past 25 years, simulation has been
used much more for one-time, special decision support studies than it
has been used as the model-component in building a model-driven DSS.
This is and can change with increased ease in creating visual
simulations. Visuals imulation means managers can see a graphic display
of simulation activities, events and results. Will Wright's games "The
Sims", "SimCoaster" and "SimCity" (cf., http://thesims.ea.com/ ) are the
precursors for advanced, agent-based, model-driven DSS. I am continuing
my research on boids, sims, swarms, ants and other such agent
technologies. So perhaps in another Ask Dan! I can discuss in more
detail complex, realistic visual simulations based upon behavioral
models. My sense is that current technologies can support development of
complex, "faster than real-time", dynamic, agent-based, model-driven DSS
for a wide variety of specific decision situations.
References
Decisioneering Staff, "SunTrust 'Banks' on Crystal Ball for assessing
the risk of commercial loans", Decisioneering, Inc., November 1998,
posted at DSSResources.COM March 16, 2001.
Eppen, G.D., F.J. Gould, and C.P. Schmidt. Introductory Management
Science (Fourth Edition), Englewood Cliffs, NJ: Prentice Hall, 1993.
Evan, J. R. and D. L. Olson, Introduction to Simulation and Risk
Analysis (2nd Edition), Upper Saddle River, NJ: Prentice Hall, 2002.
Rothfeder, J. "Expert Voices: Icosystem's Eric Bonabeau,"
CIOInsight.com, June 16, 2003,
http://www.cioinsight.com/article2/0,3959,1124316,00.asp.
Silicon Graphics Staff, "Product development decision support at
Lockheed Martin", sgi, Inc., 2002, posted at DSSResources.COM October
16, 2002.
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DSS News is copyrighted (c) 2003 by D. J. Power. Please send your questions to
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