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ADM

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Answer 50 questions in 15 minutes.
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Match each statement with the correct term.
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1. What is the notation for a standard normal distribution?
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
X+Y and X-Y are normally distributed. - (X
X~N(0 -1)
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)

2. What two variables are necessary to define a normal distribution?
Mean and variance
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) 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

3. How do you get the CDF from the PDF?
CDF= ?_(-8)^8
is bottom- up - you look at certain technologies and see what improvements they offer
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
RDTE - Investment/Acquisition - Operations and Support - Disposal

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

5. What is TCM? What is the size and what value can it take?
P(between B and A)=F(B)-F(A)
To analytically answer 'How much design margin is really necessary?'
Active UTE (additive) - Product UTE (multiplicative)
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

6. Why is the normal distribution useful or important?

7. Indirect Operating Cost
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.
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
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)

8. interval scale
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
Does not have a natural zero - is a cardinal scale
X~N(0 -1)

9. 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.
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
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

10. What is the definition of CDF?
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
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
It gives the probability that a value will be met or exceeded.
Cumulative Distribution Function

11. MADM
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+Y and X-Y are normally distributed. - (X
F(x)=1/(s(2p)^(.5) )exp?(-(x-
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.

12. What is the equation for OEC if X is a benefit (maximize) and N is a cost (minimize)?
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
OEC = W1X/Xbsl + W2Nbsl/N
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Cumulative Distribution Function

13. 3 Measures of Central Tendency (& Defs)
It can be continuous or discrete
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
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value

14. What is another name for a normal distribution?
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
Gaussian Distribution
PE(i)=?Ft
F(x)=1/(s(2p)^(.5) )exp?(-(x-

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.
Technology space limits
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
It can be continuous or discrete

16. TIES Step 7: Assess Technology
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
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.
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
(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

17. Name two uncertainties accounted for by UTE. What metric does UTE use to quantify this risk?
X+Y and X-Y are normally distributed. - (X
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)

18. What is the goal of robust design?

19. What can management do to mitigate the risk associated with infusing new technologies?
To analytically answer 'How much design margin is really necessary?'
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
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
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)

20. Why use uniform dist for input variables (Gap Analysis)
Cumulative Distribution Function
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
Allows designer to assess feasibility of design
Has a natural zero - is a cardinal scale

21. What are the different types of UTEs?
Mean and variance
X+Y and X-Y are normally distributed. - (X
Active UTE (additive) - Product UTE (multiplicative)
Technology space limits

22. 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?
Sample size is 4 - the sample is the sum of the five dice.
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
To analytically answer 'How much design margin is really necessary?'
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers

23. TIES Step 3: Model and Simulation
Technology space limits
Gaussian Distribution
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
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

24. Why do we use a sample?
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
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
(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

25. TIES Step 8: Selecting Technology

26. What is probability density contour plot
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
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD

27. Ratio scale
Has a natural zero - is a cardinal scale
Central limit theorem
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
Does not have a natural zero - is a cardinal scale

28. What is the difference between price and cost?
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
P(between B and A)=F(B)-F(A)
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space

29. Is CDF discrete or continuous - if it is discrete give the continuous equivalent - if it continuous give the discrete equivalent.
CDF= ?_(-8)^8
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
It can be continuous or discrete
A pareto frontier represents points of a non - dominated solution based on preferences

30. Define fixed cost and variable cost.
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
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-
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.

31. What does TOPSIS stand for?
Regions 1 to 3.
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
Technique for Order Preference by Similarity to Ideal Solution
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population

32. $/RPM Equation
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
Mean =0 Variance =1
Has a natural zero - is a cardinal scale

33. 4 Measures of Dispersion
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) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
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.
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo

34. Why is learning curve used (or what is it?)
(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
Technique for Order Preference by Similarity to Ideal Solution
X~N(0 -1)
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.

35. TIES
is bottom- up - you look at certain technologies and see what improvements they offer
(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
A pareto frontier represents points of a non - dominated solution based on preferences
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

36. If you have a two values on a CDF what is the probability of getting a value between them?
X~N(0 -1)
Select final tech comb. For any multi attribute - constraint - or criteria problem - the selection of the 'best' family of alternatives is inherently subjective. Various selection techniques are used to provide decision maker with extensive info. Met
P(between B and A)=F(B)-F(A)
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.

37. What is the normal distribution that results from adding x+y and x[sub]y?
RDTE - Investment/Acquisition - Operations and Support - Disposal
Determine the design space - baseline Method: Morphological Matrix
X+Y and X-Y are normally distributed. - (X
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.

38. Name the advantages of UTE.
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. 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
No way to tell without more information. It depends on the relation between s12+s22 and s32
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.

39. What can be done about uncertainty in requirement?
P(between B and A)=F(B)-F(A)
is bottom- up - you look at certain technologies and see what improvements they offer
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value

40. TIES Step 4: Investigate Design Space
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Does not have a natural zero - is a cardinal scale
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.
(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

41. If you have two compatible mature technologies to infuse - or one not mature technology - which will have the most variance?
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
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
No way to tell without more information. It depends on the relation between s12+s22 and s32

42. TIES Step 1: Problem Definition
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
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
Central limit theorem

43. Assumptions Used in TOPSis...
No way to tell without more information. It depends on the relation between s12+s22 and s32
Has a natural zero - is a cardinal scale
CDF= ?_(-8)^8
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln

44. What is the definition of ROI?
Mean and variance
The interest i such that 0=PE(i^)
Regions 1 to 3.
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.

45. TIES Step 2: Design Space Conception
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
Determine the design space - baseline Method: Morphological Matrix
Central limit theorem
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)

46. What are K- factors applied to?
Technology space limits
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Technique for Order Preference by Similarity to Ideal Solution

47. What is satisficing - what is optimizing?
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
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.
CDF= ?_(-8)^8

48. What is the equation for the learning curve?
Sample size is 4 - the sample is the sum of the five dice.
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
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

49. 8 Steps in TIES
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
Regions 1 to 3.
To analytically answer 'How much design margin is really necessary?'
(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

50. TIES Step 5: Feasible?
X~N(0 -1)
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.
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'