ADM

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1. $/RPM Equation
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
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) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
It can be continuous or discrete


2. MODM
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
PE(i)=?Ft
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.


3. What are K- factors applied to?
F(x)=1/(s(2p)^(.5) )exp?(-(x-
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
Technology space limits


4. In what regions of the graph is UTE applicable?
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
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
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
Regions 1 to 3.


5. TIES Step 3: Model and Simulation
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
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
#=2^n = 2^15
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)


6. With 15 technologies - what is the number of possible combinations?
It can be continuous or discrete
Has a natural zero - is a cardinal scale
#=2^n = 2^15
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


7. Ratio scale
Has a natural zero - is a cardinal scale
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
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
Active UTE (additive) - Product UTE (multiplicative)


8. What does TOPSIS stand for?
Technique for Order Preference by Similarity to Ideal Solution
(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
Allows designer to assess feasibility of design
F(x)=1/(s(2p)^(.5) )exp?(-(x-


9. What is probability density contour plot
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
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
Regions 1 to 3.
RDTE - Investment/Acquisition - Operations and Support - Disposal


10. 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.
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
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.
Gaussian Distribution


11. Is CDF discrete or continuous - if it is discrete give the continuous equivalent - if it continuous give the discrete equivalent.
It can be continuous or discrete
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
Technique for Order Preference by Similarity to Ideal Solution
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)


12. MADM
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 technique that determines the best alternative based on a multi- attribute utlity function which is closest to hypothetical best solution. Used for product selection.
Gaussian Distribution
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.


13. Why use uniform dist for input variables (Gap Analysis)
A pareto frontier represents points of a non - dominated solution based on preferences
RDTE - Investment/Acquisition - Operations and Support - Disposal
Cumulative Distribution Function
Allows designer to assess feasibility of design


14. Assumptions Used in TOPSis...
(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
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
Has a natural zero - is a cardinal scale


15. What can be done about uncertainty in requirement?
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
PE(i)=?Ft
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
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


16. What are the parameters for a standard normal distribution?
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
X~N(0 -1)
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
Mean =0 Variance =1


17. What can management do to mitigate the risk associated with infusing new technologies?
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
Has a natural zero - is a cardinal scale
(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


18. What is TCM? What is the size and what value can it take?
#=2^n = 2^15
PE(i)=?Ft
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
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.


19. Why are scaling parameters important?
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
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
X~N(0 -1)


20. TIES Step 5: Feasible?
CDF= ?_(-8)^8
Technique for Order Preference by Similarity to Ideal Solution
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective


21. What is the goal of robust design?

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22. Does TIES use MADM or MODM? Why?
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
X+Y and X-Y are normally distributed. - (X
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
(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


23. Other than infusing technologies - how can you create design space?
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
X~N(0 -1)
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
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


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

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25. What is the normal distribution that results from adding x+y and x[sub]y?
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
X+Y and X-Y are normally distributed. - (X
It can be continuous or discrete
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


26. Name the advantages of UTE.
PE(i)=?Ft
X~N(0 -1)
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.
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)


27. 3 Probabilistic Design Methods
Determine the design space - baseline Method: Morphological Matrix
A pareto frontier represents points of a non - dominated solution based on preferences
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration


28. If you have two compatible mature technologies to infuse - or one not mature technology - which will have the most variance?
The interest i such that 0=PE(i^)
No way to tell without more information. It depends on the relation between s12+s22 and s32
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.
Sample size is 4 - the sample is the sum of the five dice.


29. 8 Steps in TIES
P(between B and A)=F(B)-F(A)
is bottom- up - you look at certain technologies and see what improvements they offer
(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
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'


30. What two variables are necessary to define a normal distribution?
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
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
F(x)=1/(s(2p)^(.5) )exp?(-(x-
Mean and variance


31. 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.
RDTE - Investment/Acquisition - Operations and Support - Disposal
#=2^n = 2^15
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


32. interval scale
Allows designer to assess feasibility of design
Does not have a natural zero - is a cardinal scale
OEC = W1X/Xbsl + W2Nbsl/N
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.


33. TIES Step 7: Assess Technology
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 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 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
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.


34. 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.
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 technique that determines the best alternative based on a multi- attribute utlity function which is closest to hypothetical best solution. Used for product selection.
Active UTE (additive) - Product UTE (multiplicative)


35. Why is the normal distribution useful or important?

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36. Why do we use a sample?
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
Technology space limits
It gives the probability that a value will be met or exceeded.
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population


37. Weaknesses of TOPSis...
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Cumulative Distribution Function
(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
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective


38. TIES
X~N(0 -1)
To analytically answer 'How much design margin is really necessary?'
is bottom- up - you look at certain technologies and see what improvements they offer
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


39. What is the equation for present equivalent value? Define variables.
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
PE(i)=?Ft
Sample size is 4 - the sample is the sum of the five dice.


40. TIES Step 2: Design Space Conception
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
(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
Determine the design space - baseline Method: Morphological Matrix
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee


41. Strengths of TOPSis...
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
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
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order


42. How is inflation measured?

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43. TIF

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44. Show and explain a pareto frontier
P(between B and A)=F(B)-F(A)
A pareto frontier represents points of a non - dominated solution based on preferences
(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
(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


45. Write down a formula for a normal distribution
F(x)=1/(s(2p)^(.5) )exp?(-(x-
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
No way to tell without more information. It depends on the relation between s12+s22 and s32


46. Name two uncertainties accounted for by UTE. What metric does UTE use to quantify this risk?
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
(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
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)


47. What is the difference between price and cost?
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
CDF= ?_(-8)^8


48. What are the different types of UTEs?
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
Active UTE (additive) - Product UTE (multiplicative)
It gives the probability that a value will be met or exceeded.


49. Why is learning curve used (or what is it?)
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
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: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)


50. 4 Measures of Dispersion
A pareto frontier represents points of a non - dominated solution based on preferences
Gaussian Distribution
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
RDTE - Investment/Acquisition - Operations and Support - Disposal