Transcript Document

Defining Procedures for
Decision Analysis
May 02-14 & Engr 466-02A
April 30, 2002
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Client & Faculty Advisors
– Dr. Keith Adams
– Dr. John Lamont
– Dr. Ralph Patterson III
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Team Members
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Marvin Choo
Dave Cohen
Amy Kalbacken
Natasha Khan
Jesse Smith
Theodore Scott
Acknowledgments
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Faculty Advisors
Dr. Doug Gemmil
Dr. Kenneth Kirkland
Dr. Jo Min
Dr. Ron Nelson
Dr. Steve Russell
Dr. Howard Van Aucken
Dr. Max Wortman
Presentation Outline
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Problem Statement
Design Objectives
End-Product Description
Assumptions & Limitations
Project Risks & Concerns
Technical Approach
Evaluation of Project Success
Presentation Outline
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Recommendations for Future Work
Personnel Budgets
Financial Budgets
Lessons Learned
Closing Summary
Problem Statement
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Problem
– Companies often are required to make major
decisions regarding the commercialization
process for a product, process, or service
– How can we maximize efforts most efficiently
during the decision-making process?
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Goal
– Develop a guide that aids users in the decisionmaking process
Design Objectives
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Design Constraints
– Inaccurate research (especially Internet)
– Uncovering all factors
– Limited understanding of algorithms
Design Objectives
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Intended Users & Uses
– People in decision-making positions
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Gain greater understanding of methods
– Software Programmers
Have background reference information
 Detailed starting point for developing
software
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End Product Description
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The report will aid individuals in conducting a
thorough analysis of the decision factors
surrounding their particular product, process, or
service
End Product Description
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Written Report
– Key factors regarding decision processes
– Algorithms used in decision analysis
– Examples of Algorithms
– Functional Software Specification
– Reference Material
Assumptions
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Considering company goals
Aids in decision-making but will not be the
only tool used
Take into account other decision-making
factors and considerations
Using decision-making algorithms
Assumptions
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Use algorithms based on research
Have basic knowledge of decision-making
process
For any business interested in decision
analysis software
No sophisticated mathematics or statistics
are used in algorithms
Limitations
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Ranking the importance of each factor
differently
Not all data accounted for
Selected algorithms may not be applicable
to all decisions
Need to apply each process to specific
situation
Limitations
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Limited knowledge of algorithms
Algorithms may require a statistical
background or other expertise
All factors & constraints may not be
uncovered
Algorithm applicability is based on project
requirements & criteria
Project Risks & Concerns
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Scheduling
interviews
Finding information
Losing a team
member
Understanding
project
Technical Approach
Purpose
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To determine an algorithm for use in creating
software that will implement the decision analysis
process
Process
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Determine the basic project process
Compile a list of potential algorithms
Create a set of criteria for evaluating the algorithms
Research the algorithms
Select the most applicable algorithms
Technical Approach
“Basic Project Process”
Technical Approach
“List of Algorithms”
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Artificial Neural Networks
Bayesian Logic
Decision Matrix
Decision Tree
Fuzzy Logic
Genetic Algorithms
Linear Algebra
Technical Approach
“Criteria for Evaluating the
Algorithms”
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What type of problems is the algorithm good for?
What input data is needed?
What kind of control is needed?
How does the algorithm work?
What are the expected outputs?
How easy or difficult is it to implement?
Is there any information on the solution time, problem size,
etc.
Are there any examples available for the algorithm?
Are there sufficient conditions for convulgence?
If the algorithm is discovered to be ineffective what are the
reasons in support of the determination.
Technical Approach
“Selecting the Best Algorithms”
Artificial neural networks
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Able to learn, memorize, and create relationship
between data
Able to work with the non-linearities
Used for the accurate prediction of events
Decision trees
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Useful for handling a lot of complex information
Genetic algorithms
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Multi objective solutions can be defined
Project Success
Initial Startup
– Identifying key factors
– Interview coordination
Interview Results & Project Definition
– Conduction Interviews
– Completing Project Plan
Project Success
Implementation
– Algorithms
– Functional Software Specification
Testing
– Scenario Example
– Needs further testing
Project Success
End Product
– Guide
Algorithms Report
 Functional Software Specification
 Reference Material
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– Final Report
Software Package
Recommendations for
Further Work
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Create detailed models of selected
algorithms
Consult with professionals to evaluate
algorithms
Develop a functional software package
Personnel Budget
Planned
Revised
Actual
Dave Cohen 77
87
90
Amy
Kalbacken
Theodore
Scott
88
95
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75
86
90
Marvin Choo 81
81
85
Natasha
Khan
79
79
83
Jesse Smith 89
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93
Budgeted Hours Vs.
Actual Hours
Hours
Personnel Effort
100
90
80
70
60
50
40
30
20
10
0
Budget
To Date
David
Amy
HongViet Theodore Marvin
Cohen Kalbacken Nguyen
Scott
Choo
Personnel
Natasha
Khan
Jesse
Smith
Financial Budget
Item
Original Estimate
Cost
Printing
$60.00
Transportation
$0.00
Labor
$0.00
Equipment &
Parts
$0.00
Telephone
$0.00
Total Estimated
Cost
$0.00
Revised
Estimate Cost
Actual Cost to
Date
$45.00
$42.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
Lessons Learned
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Essential team attributes
– Teamwork
– Time Management
– Brainstorming
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Knowledge acquired
– Division of labor, team goals, task management
– Interview information
– Defining scope of project
Lessons Learned
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Algorithms
– Complexity
– Need to study more carefully
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Issues faced in decision-making
process
– Time vs. Money
– Who is involved in decision-making process
– Engineering vs. Business Processes
Closing Summary
Conclusion
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A tool created to aid during the decision-making
process would be well worth developing
Benefits
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Identifies key factors in the decision process
Characterizes the decision-making process
Determines the best decision processes
Aid in further analyzing a particular decision
Narrows in on the optimum decision
Questions