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CHAPTER 5
Modeling and Analysis
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Modeling and Analysis
s Major DSS component
s Model base and model management
s CAUTION - Difficult Topic Ahead
Familiarity with major ideas
Basic concepts and definitions
Tool--influence diagram
Model directly in spreadsheets
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th editionCopyright 2001, Prentice Hall, Upper Saddle River, NJ
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s Structure of some successful models and
methodologies Decision analysis
Decision trees
Optimization
Heuristic programming Simulation
s New developments in modeling tools / techniques
s Important issues in model base management
Modeling and Analysis
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th editionCopyright 2001, Prentice Hall, Upper Saddle River, NJ
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Major Modeling Issues
s Problem identification
s Environmental analysis
s Variable identification
s Forecastings Multiple model use
s Model categories or selection (Table 5.1)
s
Model managements Knowledge-based modeling
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th editionCopyright 2001, Prentice Hall, Upper Saddle River, NJ
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Static and Dynamic Models
s Static Analysis
Single snapshot
s Dynamic Analysis
Dynamic models
Evaluate scenarios that change overtime
Time dependent
Trends and patterns over timeExtend static models
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th editionCopyright 2001, Prentice Hall, Upper Saddle River, NJ
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Treating Certainty,
Uncertainty, and Risk
s Certainty Models
s
s Uncertainty
s
s Risk
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th editionCopyright 2001, Prentice Hall, Upper Saddle River, NJ
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Influence Diagrams
s
Graphical representations of a models Model of a model
s Visual communication
s Some packages create and solve the mathematical model
s Framework for expressing MSS model relationships Rectangle = a decision variable
Circle = uncontrollable or intermediate variable
Oval = result (outcome) variable: intermediate or final
Variables connected with arrows
Example (Figure 5.1)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th editionCopyright 2001, Prentice Hall, Upper Saddle River, NJ
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Analytica Influence Diagram of a MarketingProblem: The Marketing Model (Figure 5.2a)
(Courtesy of Lumina Decision Systems, Los Altos, CA)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th editionCopyright 2001, Prentice Hall, Upper Saddle River, NJ
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MSS Modeling in Spreadsheets
s Spreadsheet: most popular end-user modeling tools Powerful functions
s Add-in functions and solvers
s Important for analysis, planning, modeling
s Programmability (macros) (More)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th editionCopyright 2001, Prentice Hall, Upper Saddle River, NJ
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s What-if analysis
s Goal seeking
s Simple database management
s Seamless integration
s Microsoft Excel
s Lotus 1-2-3
s
s Excel spreadsheet static model example of a simpleloan calculation of monthly payments (Figure5.3)
s
s Excel spreadsheet dynamic model example of asimple loan calculation of monthly payments andeffects of prepayment (Figure 5.4)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th editionCopyright 2001, Prentice Hall, Upper Saddle River, NJ
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Decision Analysisof Few Alternatives
(Decision Tables and Trees)
Single Goal Situations
s
s Decision tables
s
s Decision trees
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th editionCopyright 2001, Prentice Hall, Upper Saddle River, NJ
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Decision Tables
s Investment example
s One goal: maximize the yield after one year
s Yield depends on the status of the economy
(thestate of nature)
Solid growth
Stagnation
Inflation
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1. If solid growth in the economy, bonds yield12%; stocks 15%; time deposits 6.5%
2. If stagnation, bonds yield 6%; stocks 3%;
time deposits 6.5%
3. If inflation, bonds yield 3%; stocks lose 2%;time deposits yield 6.5%
Possible Situations
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View Problem as a Two-Person Game
Payoff Table 5.2s
s Decision variables (alternatives)
s
s Uncontrollable variables (states of economy)
s
s Result variables (projected yield)
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Table 5.2: Investment ProblemDecision Table Model
States of Nature
Solid Stagnation Inflation
Alternatives Growth
Bonds 12% 6% 3%
Stocks 15% 3% -2%
CDs 6.5% 6.5% 6.5%
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Treating Uncertainty
s Optimistic approach
s
s Pessimistic approach
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Treating Risk
s Use known probabilities (Table 5.3)
s
s
Risk analysis: compute expected valuess
s Can be dangerous
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Table 5.3: Decision Under Risk and ItsSolution
Solid Stagnation Inflation Expected
Growth Value
Alternatives .5 .3 .2
Bonds 12% 6% 3% 8.4% *
Stocks 15% 3% -2% 8.0%
CDs 6.5% 6.5% 6.5% 6.5%
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s Decision Trees
s Other methods of treating risk Simulation
Certainty factors
Fuzzy logic
s Multiple goals
s Yield, safety, and liquidity (Table 5.4)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th editionCopyright 2001, Prentice Hall, Upper Saddle River, NJ
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Table 5.4: Multiple Goals
Alternatives Yield Safety Liquidity
Bonds 8.4% High High
Stocks 8.0% Low High
CDs 6.5% Very High High
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Optimization via MathematicalProgramming
s Linear programming (LP)
Used extensively in DSS
s Mathematical Programming Family of tools to solve managerial problems
in allocating scarce resources among
various activities to optimize a measurablegoal
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LP AllocationProblem Characteristics
1. Limited quantity of economic resources
2. Resources are used in the production ofproducts or services
3. Two or more ways (solutions, programs)to use the resources
4. Each activity (product or service) yieldsa return in terms of the goal
5. Allocation is usually restricted byconstraints
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LP Allocation Model
s Rational economic assumptions 1. Returns from allocations can be compared in a common
unit
2. Independent returns
3. Total return is the sum of different activities returns
4. All data are known with certainty
5. The resources are to be used in the most economicalmanner
s Optimal solution: the best, found algorithmically
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Linear Programming
s Decision variables
s Objective function
s
Objective function coefficientss Constraints
s Capacities
s Input-output (technology) coefficients
Line
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Heuristic Programming
s Cuts the search
s Getssatisfactory solutions more quickly and lessexpensively
s Finds rules to solve complex problems
s Finds good enough feasible solutions to complex problems
s Heuristics can be
Quantitative
Qualitative (in ES)
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When to Use Heuristics
1. Inexact or limited input data
2. Complex reality
3. Reliable, exact algorithm not available
4. Computation time excessive5. To improve the efficiency of optimization
6. To solve complex problems
7. For symbolic processing
8. For making quick decisions
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Simulation
s Technique for conducting experiments with acomputer on a model of a management system
s
s Frequently used DSS tool
s
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Major Characteristics of Simulation
s Imitates reality and capture its richness
s
s Technique for conducting experiments
s
s Descriptive, not normative tool
s
s Often to solve very complex, risky problems
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Simulation Methodology
Model real system and conduct repetitiveexperiments
1. Define problem
2. Construct simulation model 3. Test and validate model
4. Design experiments
5. Conduct experiments
6. Evaluate results 7. Implement solution
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Multidimensional Modeling
s Performed in online analytical processing (OLAP)
s From a spreadsheet and analysis perspective
s 2-D to 3-D to multiple-D
s Multidimensional modeling tools: 16-D +s Multidimensional modeling - OLAP (Figure 5.6)
s Tool can compare, rotate, and slice and dicecorporate data across different management
viewpoints
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Vi l I t ti M d li (VIS) d
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Visual Interactive Modeling (VIS) andVisual Interactive Simulation (VIS)
s Visual interactive modeling (VIM) (DSS In Action 5.8)Also called
Visual interactive problem solving
Visual interactive modeling
Visual interactive simulation
s Use computer graphics to present the impact of differentmanagement decisions.
s Can integrate with GIS
s Users perform sensitivity analysis
s Static or a dynamic (animation) systems (Figure 5.7)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
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Visual Interactive Simulation (VIS)
s Decision makers interact with the simulatedmodel and watch the results over time
s Visual interactive models and DSS
VIM (Case Application W5.1 on books Website)
Queueing
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Quantitative Software Packages-OLAP
s Preprogrammed models can expedite DSSprogramming time
s Some models are building blocks of other models
Statistical packages
Management science packages
Revenue (yield) management
Other specific DSS applications including spreadsheet add-ins
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Model Base Management
s MBMS: capabilities similar to that of DBMS
s But, there are no comprehensive model base managementpackages
s Each organization uses models somewhat differently
s There are many model classess Within each class there are different solution approaches
s Some MBMS capabilities require expertise and reasoning
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition
Copyright 2001, Prentice Hall, Upper Saddle River, NJ