DSS & Warehousing Systems
Chapter 8
Efrem Mallach
Prepared by
Luvai Motiwalla
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Irwin/McGraw-Hill
Copyright © 2000 by The McGraw-Hill Companies, Inc. All rights reserved.
Models in Decision Support
Systems
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Introduction
Model types
Model types used in DSS
Discrete – event simulation models
Designing a discrete – event simulation model
Complete simulation studies
Random numbers, Pseudo – Random numbers and
statistical distributions
Static simulation models
Irwin/McGraw-Hill
Copyright © 2000 by The McGraw-Hill Companies, Inc. All rights reserved.
Introduction
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All DSS above the simplest data - oriented
ones are based on models.
Irwin/McGraw-Hill
Copyright © 2000 by The McGraw-Hill Companies, Inc. All rights reserved.
Model Types
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Basic types of system models include graphical
models, physical models, mathematical, and
symbolic or information – based models.
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The information – based models is more
accurate, though it is not used as widely
because it is a bit more cumbersome.
Irwin/McGraw-Hill
Copyright © 2000 by The McGraw-Hill Companies, Inc. All rights reserved.
Model Types Used in DSS
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Systems versus process models
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Static versus dynamic models
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Copyright © 2000 by The McGraw-Hill Companies, Inc. All rights reserved.
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Continuous versus discrete- event models
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Deterministic versus stochastic models
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Copyright © 2000 by The McGraw-Hill Companies, Inc. All rights reserved.
Discrete – Event Simulation Models
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The concept of discrete – event simulation
Irwin/McGraw-Hill
Copyright © 2000 by The McGraw-Hill Companies, Inc. All rights reserved.
Designing a Discrete – Event
Simulation Model
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The process of designing a discrete – event
simulation model.
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Simulation languages generally include
capabilities that will allow you to develop your
model more quickly.
Irwin/McGraw-Hill
Copyright © 2000 by The McGraw-Hill Companies, Inc. All rights reserved.
Complete Simulation Studies
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A full – scale simulation study runs the
simulation several times for each state of
controllable variables to give us a distribution
of results
Irwin/McGraw-Hill
Copyright © 2000 by The McGraw-Hill Companies, Inc. All rights reserved.
Random Numbers,
Pseudo - Random Numbers and
Statistical Distributions-cont’d
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The behavior of a simulation model depends
on the numbers that determine when each
event occurs.
Irwin/McGraw-Hill
Copyright © 2000 by The McGraw-Hill Companies, Inc. All rights reserved.
Random Numbers,
Pseudo - Random Numbers and
Statistical Distributions
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When you want a non uniform distribution, you
must convert the output of the built – in
function to a number from the desired
distribution. This is done via cumulative
distribution function ( CDF).
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Copyright © 2000 by The McGraw-Hill Companies, Inc. All rights reserved.
Static Simulation Models-cont’d
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Simulation models are dynamic. There are also
static situations where we can apply the same
idea of using pseudo – random numbers to
drive a system model.
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Copyright © 2000 by The McGraw-Hill Companies, Inc. All rights reserved.
Static Simulation Models
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Packages are available to help decision
makers use static, stochastic, simulation
models.
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Copyright © 2000 by The McGraw-Hill Companies, Inc. All rights reserved.