Decision Support and
Business Intelligence
Systems
(9
th
Ed., Prentice Hall)
Chapter 4:
Learning Objectives
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,
Learning Objectives
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
Opening Vignette:
“Model-Based Auctions Serve More
Lunches in Chile”
Background: problem situation
Proposed solution
Results
Modeling and Analysis Topics
Modeling for MSS (a critical component) Static and dynamic models
Treating certainty, uncertainty, and risk
Influence diagrams (in the posted PDF file) MSS modeling in spreadsheets
Decision analysis of a few alternatives (with decision
tables and decision trees)
Optimization via mathematical programming Heuristic programming
Simulation
MSS 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
Locating distribution centers, assignment of DCs to
warehouses/customers, forecasting demand, scheduling production per product type, etc.
Major Modeling Issues
Problem identification and environmental
analysis (information collection)
Variable identification
Influence diagrams, cognitive maps
Forecasting/predicting
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
Categories of Models
Category 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 Predict future occurrences, Forecasting, Markov
Static and Dynamic Models
Static Analysis
Single snapshot of the situation Single interval
Steady state
Dynamic Analysis
Dynamic models
Evaluate scenarios that change over time Time dependent
Represents trends and patterns over time More realistic: Extends static models
Decision Making:
Treating Certainty, Uncertainty and Risk
Certainty Models
Assume complete knowledge
All potential outcomes are known May yield optimal solution
Uncertainty
Several outcomes for each decision
Probability of each outcome is unknown Knowledge would lead to less uncertainty
Risk analysis (probabilistic decision making)
Probability of each of several outcomes occurring
Influence Diagrams
Graphical representations of a model
“Model of a model”
A tool for visual communication
Some influence diagram packages create and solve
the mathematical model
Framework for expressing MSS model relationships
Rectangle = a decision variable
Circle = uncontrollable or intermediate variable
Oval = result (outcome) variable: intermediate or final
Variables are connected with arrows indicates the direction of influence (relationship)
Influence Diagrams: Relationships
Amount in CDs Interest Collected Price Sales Sales ~ Demand CERTAINTY UNCERTAINTYRANDOM (risk) variable: Place a tilde (~) above the variable’s name
The shape of
the arrow
indicates the
type of
Influence Diagrams: Example
~ Amount used in Advertisement Unit Price Units Sold Unit Cost Fixed Cost Income Expenses ProfitAn influence diagram for the profit model
Profit = Income – Expense Income = UnitsSold * UnitPrice
UnitsSold = 0.5 * Advertisement Expense Expenses = UnitsCost * UnitSold + FixedCost
Influence Diagrams: Software
Analytica, Lumina Decision Systems
Supports hierarchical (multi-level) diagrams
DecisionPro, Vanguard Software Co.
Supports hierarchical (tree structured) diagrams
DATA Decision Analysis, TreeAge Software
Includes influence diagrams, decision trees and simulation
Definitive Scenario, Definitive Software
Integrates influence diagrams and Excel, also supports
Monte Carlo simulations
PrecisionTree, Palisade Co.
Creates influence diagrams and decision trees directly in an
Analytica Influence Diagram of a Marketing
Problem: The Marketing Model
MSS Modeling with Spreadsheets
Spreadsheet: most popular end-user modeling tool Flexible and easy to use
Powerful functions
Add-in functions and solvers
Programmability (via macros) What-if analysis
Goal seeking
Simple database management
Seamless integration of model and data
Incorporates both static and dynamic models Examples: Microsoft Excel, Lotus 1-2-3
Excel spreadsheet - static model example:
Simple loan calculation of monthly payments
1 ) 1 ( ) 1 ( ) 1 ( n n n i i i P A i P F
Excel spreadsheet -
Dynamic model
example:
Simple loan
calculation of
monthly payments
and effects of
prepayment
Decision Analysis: A Few Alternatives
Single Goal Situations
Decision tables Multiple criteria decision analysis Features include decision
variables (alternatives),
uncontrollable variables, result variables
Decision trees
Graphical representation of
relationships
Multiple criteria approach Demonstrates complex
relationships
Decision Tables
Investment example
One goal: maximize the yield after one year
Yield depends on the status of the economy
(the
state of nature
)
Solid growth Stagnation Inflation
Investment Example:
Possible Situations
1. If solid growth in the economy, bonds yield 12%; 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%
Payoff Decision variables (alternatives)
Uncontrollable variables (states of economy)
Result variables (projected yield)
Tabular representation:
Investment Example:
Decision Table
Investment Example:
Treating Uncertainty
Optimistic approach
Pessimistic approach
Treating Risk:
Use known probabilities
Decision Analysis: A Few Alternatives
Other methods of treating risk
Simulation, Certainty factors, Fuzzy logic
Multiple goals
MSS Mathematical Models
Decision Variables Mathematical Relationships Uncontrollable Variables Result Variables Non-Quantitative Models (Qualitative)
Captures symbolic relationships between decision variables, uncontrollable
variables and result variables
Quantitative Models: Mathematically links decision variables,
uncontrollable variables, and result variables
Decision variables describe alternative choices.
Uncontrollable variables are outside decision-maker’s control
Result variables are dependent on chosen combination of decision variables
Optimization
via Mathematical Programming
Mathematical Programming
A family of tools designed to help solve
managerial problems in which the decision maker must allocate scarce resources among competing activities to optimize a measurable goal
Optimal solution: The best possible solution
to a modeled problem
Linear programming (LP): A mathematical model
for the optimal solution of resource allocation problems. All the relationships are linear
LP Problem Characteristics
1. Limited quantity of economic resources
2. Resources are used in the production of
products or services
3. Two or more ways (solutions, programs) to
use the resources
4. Each activity (product or service) yields a
return in terms of the goal
Line
Linear Programming Steps
1. Identify the …
Decision variables Objective function
Objective function coefficients Constraints
Capacities / Demands
2. Represent the model
LINDO: Write mathematical formulation
EXCEL: Input data into specific cells in Excel
LP Example
The Product-Mix Linear Programming Model
MBI Corporation
Decision: How many computers to build next month? Two types of mainframe computers: CC7 and CC8
Constraints: Labor limits, Materials limit, Marketing lower limits
CC7 CC8 Rel Limit Labor (days) 300 500 <= 200,000 /mo
Materials ($) 10,000 15,000 <= 8,000,000 /mo
Units 1 >= 100
Units 1 >= 200
Profit ($) 8,000 12,000 Max
LP Solution
Decision Variables: X1: unit of CC-7 X2: unit of CC-8 Objective Function: Maximize Z (profit) Z=8000X1+12000X2 Subject To 300X1 + 500X2 200K 10000X1 + 15000X2 8000K X1 100 X2 200Sensitivity, What-if, and
Goal Seeking Analysis
Sensitivity
Assesses impact of change in inputs on outputs Eliminates or reduces variables
Can be automatic or trial and error
What-if
Assesses solutions based on changes in variables or
assumptions (scenario analysis)
Goal seeking
Backwards approach, starts with goal
Determines values of inputs needed to achieve goal Example is break-even point determination
Heuristic Programming
Cuts the search space
Gets satisfactory solutions more
quickly and less expensively
Finds good enough feasible
solutions to very complex problems
Heuristics can be
Quantitative
Qualitative (in ES)
Traveling Salesman Problem
What is it?
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
When to Use Heuristics
When to Use Heuristics
Inexact or limited input data Complex reality
Reliable, exact algorithm not available Computation time excessive
For making quick decisions
Limitations of Heuristics
Tabu search
Intelligent search algorithm
Genetic algorithms
Survival of the fittest
Simulated annealing
Analogy to Thermodynamics
Simulation
Technique for conducting experiments with a
computer on a comprehensive model of the
behavior of a system
Imitates
reality and capture its richness
Technique for
conducting experiments
Descriptive
,
not normative tool
Often to “solve” very complex problems
Simulation is normally used only when a
problem is too complex to be treated using
numerical optimization techniques
Major Characteristics of Simulation
Advantages of Simulation
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
Produces important performance measures
Often it is the only DSS modeling tool for
Limitations of Simulation
Cannot guarantee an optimal solution
Slow and costly construction process
Cannot transfer solutions and inferences to
solve other problems (problem specific)
So easy to explain/sell to managers, may lead
overlooking analytical solutions
Simulation Methodology
Model real system and conduct repetitive experiments. Steps:
1. Define problem 5. Conduct experiments 2. Construct simulation model 6. Evaluate results
3. Test and validate model 7. Implement solution 4. Design experiments
Simulation Types
Stochastic vs. Deterministic Simulation
In stochastic simulations: We use distributions (Discrete or
Continuous probability distributions)
Time-dependent vs. Time-independent Simulation
Time independent stochastic simulation via Monte Carlo
technique (X = A + B)
Discrete event vs. Continuous simulation Steady State vs. Transient Simulation
Simulation Implementation
Visual interactive modeling (VIM)
Also called
Visual interactive problem solving Visual interactive modeling
Visual interactive simulation
Uses computer graphics to present the impact
of different management decisions
Often integrated with GIS
Users perform sensitivity analysis
Static or a dynamic (animation) systems
Visual Interactive Modeling (VIM) /
Visual Interactive Simulation (VIS)
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
Within each class there are different solution
approaches
Relations MBMS
End of the Chapter
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