Decision Support and
Business Intelligence
Systems
(9th Ed., Prentice Hall)
Chapter 4:
Modeling and Analysis
Learning Objectives





4-2
Understand the basic concepts of management
support system (MSS) modeling
Describe how MSS models interact with data
and the users
Understand the well-known model classes and
decision making with a few alternatives
Describe how spreadsheets can be used for
MSS modeling and solution
Explain the basic concepts of optimization,
simulation and heuristics; when to use which
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Learning Objectives






4-3
Describe how to structure a linear
programming model
Understand how search methods are used to
solve MSS models
Explain the differences among algorithms,
blind search, and heuristics
Describe how to handle multiple goals
Explain what is meant by sensitivity analysis,
what-if analysis, and goal seeking
Describe the key issues of model management
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Outline










4-4
Modeling for DSS
2. Static and Dynamic models
3. Treating certainty, uncertainty
4. Influence diagrams
5. Modeling with spreadsheets
6.Decision Tables and Decision trees
7.MSS mathematical models
8. Search approaches
9.Simulation
10. Model base management system
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
MSS Modeling


Modeling A key element in most MSS
Leads to reduced cost and increased revenue


DuPont Simulates Rail Transportation System and
Avoids Costly Capital Expenses
Procter & Gamble uses several DSS models
collectively to support strategic decisions

4-5
Locating distribution centers, forecasting demand,
scheduling production per product type, etc.
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Major Modeling Issues


Problem identification and environmental
analysis (information collection)
Variable identification


predicting


4-6
Influence diagrams, cognitive maps
More information leads to better prediction
Multiple models: A MSS can include several
models, each of which represents a different
part of the decision-making problem
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Categories of Models
Category
4-7
Objective
Techniques
Optimization of
problems with few
alternatives
Find the best solution from a
small number of alternatives
Decision tables,
decision trees
Optimization via
algorithm
Find the best solution from a
large number of alternatives
using a step-by-step process
Linear and other
mathematical
programming models
Optimization via an
analytic formula
Find the best solution in one
step using a formula
Some inventory models
Simulation
Find a good enough solution
by experimenting with a
dynamic model of the system
Several types of
simulation
Heuristics
Find a good enough solution
using “common-sense” rules
Heuristic programming
and expert systems
Predictive and
other models
Predict future occurrences.....
Markov chains,
financial, …
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Static and Dynamic Models

Static Analysis




Dynamic Analysis




4-8
Single snapshot of the situation
Single interval
Steady state
Dynamic models
Evaluate scenarios that change over time
Time dependent
More realistic: Extends static models
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Decision Making:
Treating Certainty, Uncertainty and Risk

Certainty Models




Uncertainty




Several outcomes for each decision
Probability of each outcome is unknown
Knowledge would lead to less uncertainty
Risk analysis (probabilistic decision making)


4-9
Assume complete knowledge
All potential outcomes are known
May yield optimal solution
Probability of each of several outcomes occurring
Level of uncertainty => Risk (expected value)
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Certainty, Uncertainty and Risk
4-10
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Influence Diagrams






4-11
Graphical representation of model
Provides relationship framework
Examines dependencies of variables
Any level of details
Shows impact of change
Shows what-if analysis
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Influence Diagrams: Relationships
CERTAINTY
Amount in
CDs
Interest
Collected
UNCERTAINTY
Price
Sales
The shape of
the arrow
indicates the
type of
relationship
RANDOM (risk) variable: Place a tilde (~) above the variable’s name
~
Demand
Sales
4-12
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Influence Diagrams
Decision
Variables:
Result or
outcome
(intermediate or
final)
Intermediate or
uncontrollable
Arrows indicate type of relationship and direction of influence
Certainty
Uncertainty
4-13
Amount in
CDs
Interest
earned
Sales
Price
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
4-13
MSS Modeling with Spreadsheets



Spreadsheet: most popular end-user modeling tool
Flexible and easy to use
Powerful functions







4-14
Add-in functions and solvers
Programmability (via macros)
What-if analysis
Simple database management
Seamless integration of model and data
Include both static and dynamic models
Examples: Microsoft Excel, Lotus 1-2-3
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Excel spreadsheet - static model example:
Simple loan calculation of monthly payments
F  P(1  i )n
 i (1  i )n 
A  P

n
(
1

i
)

1


4-15
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Excel spreadsheet Dynamic model
example:
Simple loan
calculation of
monthly payments
and effects of
prepayment
4-16
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Heuristic Programming




4-17
Cuts the search space
Gets satisfactory solutions more
quickly and less expensively
Finds good enough feasible
solutions to very complex
problems
Traveling Salesman Problem
>>>
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Traveling Salesman Problem

What is it?


4-18
A traveling salesman must visit customers in
several cities, visiting each city only once, across
the country. Goal: Find the shortest possible route
Total number of unique routes (TNUR):
TNUR = (1/2) (Number of Cities – 1)!
Number of Cities
TNUR
5
12
6
60
9
20,160
20
1.22 1018
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
When to Use Heuristics
When to Use Heuristics



Inexact or limited input data
Complex reality
For making quick decisions
Limitations of Heuristics

4-19
Cannot guarantee an optimal solution
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Modern Heuristic Methods

Tabu search


Genetic algorithms


Survival of the fittest
Simulated annealing

4-20
Intelligent search algorithm
Analogy to Thermodynamics
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Simulation


4-21
Technique for conducting experiments with a
computer on a complate model of the
behavior of a system
Frequently used in DSS tools
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Major Characteristics of Simulation




!
4-22
Imitates reality and capture its richness
Technique for conducting experiments
Descriptive
Often to “solve” very complex problems
Simulation is normally used only when a
problem is too complex to be treated using
numerical optimization techniques
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Advantages of Simulation






4-23
The theory is fairly straightforward
Great deal of time compression
Experiment with different alternatives
The model reflects manager’s perspective
Can handle wide variety of problem types
Can include the real complexities of problems
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Limitations of Simulation


4-24
Cannot guarantee an optimal solution
Slow and costly construction process
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Simulation Methodology


Model real system and conduct repetitive experiments.
Steps:
1.
2.
3.
4.
4-25
Define problem
Construct simulation model
Test and validate model
Design experiments
5. Conduct experiments
6. Evaluate results
7. Implement solution
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
Model Base Management




MBMS: capabilities similar to that of DBMS
But, there are no comprehensive model base
management packages
Each organization uses models somewhat
differently
There are many model classes

4-26
Within each class there are different solution
approaches
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
End of the Chapter

4-27
Questions / Comments…
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall
All rights reserved. No part of this publication may be reproduced, stored in a
retrieval system, or transmitted, in any form or by any means, electronic,
mechanical, photocopying, recording, or otherwise, without the prior written
permission of the publisher. Printed in the United States of America.
Copyright © 2011 Pearson Education, Inc.
Publishing as Prentice Hall
4-28
Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall