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ADM

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1. Name the advantages of UTE.
Optimizing - finds the set of criteria that maximizes or minimizes a design criteria or several design criteria - Satisficing - finds the conditions where the constraints or requires are met but no optimization occurs.
F(x)=1/(s(2p)^(.5) )exp?(-(x-
#=2^n = 2^15
Provide for rapid trade- off capability between the three elements and search for feasible solutions - Allow graphical visualization of the combined space - Address mission requirements ambiguity and technology uncertainty.

2. What is TIM? What is the size and what value can it take?

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3. You have a group of 5 dice. You roll the groups and sum the results of the 5 dice 4 times. What is the sample size? What are you sampling?
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
Sample size is 4 - the sample is the sum of the five dice.
Scaling parameters photographically scale the size of the vehicle to take full advantage of technology -(e.g. increase CL -> Can decrease S -> Decreases D -> Decreases Fuel Consumed -> etc...) This assumes that the physics of the problem remains the

4. Direct Operating Costs
Technique for Order Preference by Similarity to Ideal Solution
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
X~N(0 -1)

5. What two variables are necessary to define a normal distribution?
Determine the design space - baseline Method: Morphological Matrix
Gaussian Distribution
Mean and variance
To analytically answer 'How much design margin is really necessary?'

6. What is another name for a normal distribution?
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
Gaussian Distribution

7. Write down a formula for a normal distribution
RDTE - Investment/Acquisition - Operations and Support - Disposal
#=2^n = 2^15
X~N(0 -1)
F(x)=1/(s(2p)^(.5) )exp?(-(x-

8. What can management do to mitigate the risk associated with infusing new technologies?
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Determine the design space - baseline Method: Morphological Matrix
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)

9. Why is the normal distribution useful or important?

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10. With 15 technologies - what is the number of possible combinations?
No way to tell without more information. It depends on the relation between s12+s22 and s32
#=2^n = 2^15
A technique that determines the best alternative based on a multi- attribute utlity function which is closest to hypothetical best solution. Used for product selection.
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order

11. Name two uncertainties accounted for by UTE. What metric does UTE use to quantify this risk?
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
CDF= ?_(-8)^8

12. TIES
OEC = W1X/Xbsl + W2Nbsl/N
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
Technology Compatability Matrix - For n techs - is nxn matrix - Tells whether the intersecting technologies are compatible - It only has 0s and 1s - 0 means the technologies are not compatible with each other - 1 means techs are compatible with each
is bottom- up - you look at certain technologies and see what improvements they offer

13. What is the definition of ROI?
Technique for Order Preference by Similarity to Ideal Solution
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
The interest i such that 0=PE(i^)
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.

14. $/RPM Equation
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
X+Y and X-Y are normally distributed. - (X
(1) Problem def - (2) Design space conception (3) Model and Simulation (4) Investigate Design Space (5) Feasible? (6) Identify Technologies (7) Evaluate Technologies (8) Select Technology

15. Does TIES use MADM or MODM? Why?
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
Mean =0 Variance =1
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
RDTE - Investment/Acquisition - Operations and Support - Disposal

16. Why is learning curve used (or what is it?)
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
P(between B and A)=F(B)-F(A)
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)

17. TIES Step 6: Identify Technology
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
M&S environment is needed to facilitate rapid assessments with minimal time and monetary expenditures of the alternative concepts identified in the Morphological Matrix Method: DoE
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
(1) Identify potential technologies that may improve technical & economical feasibility (2) Establish physical compatibility rules for diff techs (3) Determine expected impact (improvements and degradations) to systems of interest Method: TRL - Techn

18. What is the difference between price and cost?
(1) Mission Requirements - Input: Mission metrics and requirements Output: Delta response for requirements (2) Design Variables - Input: Geometric and economic design variables Output: Delta response for design variable - (3) Technologies Input: P
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
Gaussian Distribution
As you add n number of identical & independent distributions (IIDs) together - as n --> inf - the resulting distribution will be normal - regardless of the shape of the IIDs

19. Why are scaling parameters important?
Scaling parameters photographically scale the size of the vehicle to take full advantage of technology -(e.g. increase CL -> Can decrease S -> Decreases D -> Decreases Fuel Consumed -> etc...) This assumes that the physics of the problem remains the
Has a natural zero - is a cardinal scale
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order

20. In what regions of the graph is UTE applicable?
Regions 1 to 3.
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Range is always between zero and 1 monotonically increasing

21. 8 Steps in TIES
Active UTE (additive) - Product UTE (multiplicative)
(1) Problem def - (2) Design space conception (3) Model and Simulation (4) Investigate Design Space (5) Feasible? (6) Identify Technologies (7) Evaluate Technologies (8) Select Technology
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.

22. Strengths of TOPSis...
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
Mean =0 Variance =1
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)

23. What are the four difference life cycle costs?
RDTE - Investment/Acquisition - Operations and Support - Disposal
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
(1) Mission Requirements - Input: Mission metrics and requirements Output: Delta response for requirements (2) Design Variables - Input: Geometric and economic design variables Output: Delta response for design variable - (3) Technologies Input: P
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)

24. Indirect Operating Cost
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
X~N(0 -1)
Range: Gives the magnitude of the spread - min and max - Variance: Indicates how spread out the data is - Skewness: Indicates if the distribution is biased - Kurtosis: Peakness

25. Assumptions Used in TOPSis...
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
It gives the probability that a value will be met or exceeded.
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln

26. What are the parameters for a standard normal distribution?
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
Mean =0 Variance =1
(1) Identify potential technologies that may improve technical & economical feasibility (2) Establish physical compatibility rules for diff techs (3) Determine expected impact (improvements and degradations) to systems of interest Method: TRL - Techn
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.

27. MODM
Range is always between zero and 1 monotonically increasing
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.

28. What is the equation for OEC if X is a benefit (maximize) and N is a cost (minimize)?
Determine the design space - baseline Method: Morphological Matrix
Has a natural zero - is a cardinal scale
OEC = W1X/Xbsl + W2Nbsl/N
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.

29. What is the equation for present equivalent value? Define variables.
PE(i)=?Ft
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
It gives the probability that a value will be met or exceeded.

30. What is TRL? Range? What does a high TRL mean?
A technique that determines the best alternative based on a multi- attribute utlity function which is closest to hypothetical best solution. Used for product selection.
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
Technology Readiness Level Ranges 1-9 - where 1 means that the basic principle have been observed and reported and 9 means the technology has had successful missions A high tech means the technology is pretty developed and should be (or is) ready for
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor

31. What are the three snapshots of UTE?
Range: Gives the magnitude of the spread - min and max - Variance: Indicates how spread out the data is - Skewness: Indicates if the distribution is biased - Kurtosis: Peakness
(1) Mission Requirements - Input: Mission metrics and requirements Output: Delta response for requirements (2) Design Variables - Input: Geometric and economic design variables Output: Delta response for design variable - (3) Technologies Input: P
Allows designer to assess feasibility of design
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.

32. What are properties of a CDF?
P(between B and A)=F(B)-F(A)
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
Range is always between zero and 1 monotonically increasing
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor

33. What does CLT stand for?
Mean =0 Variance =1
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
Central limit theorem
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo

34. What is the equation for the learning curve?
X+Y and X-Y are normally distributed. - (X
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order

35. MADM
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
A technique that determines the best alternative based on a multi- attribute utlity function which is closest to hypothetical best solution. Used for product selection.
Technology Readiness Level Ranges 1-9 - where 1 means that the basic principle have been observed and reported and 9 means the technology has had successful missions A high tech means the technology is pretty developed and should be (or is) ready for

36. Why do we use a sample?
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
RDTE - Investment/Acquisition - Operations and Support - Disposal

37. What does TOPSIS stand for?
(1) Mission Requirements - Input: Mission metrics and requirements Output: Delta response for requirements (2) Design Variables - Input: Geometric and economic design variables Output: Delta response for design variable - (3) Technologies Input: P
Allows designer to assess feasibility of design
F(x)=1/(s(2p)^(.5) )exp?(-(x-
Technique for Order Preference by Similarity to Ideal Solution

38. Why use uniform dist for input variables (Gap Analysis)
Mean and variance
Allows designer to assess feasibility of design
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.

39. Weaknesses of TOPSis...
Does not have a natural zero - is a cardinal scale
Technology Compatability Matrix - For n techs - is nxn matrix - Tells whether the intersecting technologies are compatible - It only has 0s and 1s - 0 means the technologies are not compatible with each other - 1 means techs are compatible with each
No way to tell without more information. It depends on the relation between s12+s22 and s32
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective

40. What does CDF stand for?
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
Regions 1 to 3.
Cumulative Distribution Function
Gaussian Distribution

41. Show and explain a pareto frontier
Trying to determine the metric values for any combination of design variables/ what the metrics are as a function of design variables Method: RSE: Response Surface Eqn.
A pareto frontier represents points of a non - dominated solution based on preferences
Determine the design space - baseline Method: Morphological Matrix
Inflation is the decrease in the buying power/value of money. It is caused by the when amount of available money changes wrt amount of product/services available

42. What is probability density contour plot
X~N(0 -1)
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
Trying to determine the metric values for any combination of design variables/ what the metrics are as a function of design variables Method: RSE: Response Surface Eqn.
A probability density contour plot is a visualization method for Joint probability density function (a 2D representation). Their shapes (contour shapes) tell if the metric analyzed in each axis are correlated or not (Circular -> no correlation) (elli

43. 3 Probabilistic Design Methods
Sample size is 4 - the sample is the sum of the five dice.
A technique that determines the best alternative based on a multi- attribute utlity function which is closest to hypothetical best solution. Used for product selection.
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD

44. TIES Step 4: Investigate Design Space
Trying to determine the metric values for any combination of design variables/ what the metrics are as a function of design variables Method: RSE: Response Surface Eqn.
RDTE - Investment/Acquisition - Operations and Support - Disposal
No way to tell without more information. It depends on the relation between s12+s22 and s32
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers

45. What are the different types of UTEs?
Active UTE (additive) - Product UTE (multiplicative)
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
A pareto frontier represents points of a non - dominated solution based on preferences
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.

46. What is the notation for a standard normal distribution?
X~N(0 -1)
M&S environment is needed to facilitate rapid assessments with minimal time and monetary expenditures of the alternative concepts identified in the Morphological Matrix Method: DoE
Gaussian Distribution
Sample size is 4 - the sample is the sum of the five dice.

47. What is satisficing - what is optimizing?
P(between B and A)=F(B)-F(A)
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
Cumulative Distribution Function
Optimizing - finds the set of criteria that maximizes or minimizes a design criteria or several design criteria - Satisficing - finds the conditions where the constraints or requires are met but no optimization occurs.

48. Is CDF discrete or continuous - if it is discrete give the continuous equivalent - if it continuous give the discrete equivalent.
Regions 1 to 3.
X~N(0 -1)
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
It can be continuous or discrete

49. TIES Step 8: Selecting Technology

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50. If you have two compatible mature technologies to infuse - or one not mature technology - which will have the most variance?
No way to tell without more information. It depends on the relation between s12+s22 and s32
(1) Mission Requirements - Input: Mission metrics and requirements Output: Delta response for requirements (2) Design Variables - Input: Geometric and economic design variables Output: Delta response for design variable - (3) Technologies Input: P
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
P(between B and A)=F(B)-F(A)