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Systems Engineering: Bridging Systems Engineering: Bridging Industry, Government and AcademiaIndustry, Government and Academia
The Tradespace Exploration ParadigmAdam Ross and Daniel Hastings
MITINCOSE International Symposium
July 14, 2005
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Motivation
Conceptual Design is a high leverage phase in system development
Conceptual/preliminary
Design
Detaildesign/
development
Productionand/or
construction
Product use/support/
phaseout/disposal
100%
80%
66%
Ease of Change
LCC committed
Cost Incurred
Lifecycle Cost
NEED
~66%
ConclusionApplicationsAdvancedInsightsMATEMotivation
Concept Selected
Need Captured
Resources Scoped
DesignDesign
In SituIn Situ
Top-side sounderTop-side soundervs.In SituIn Situ
Top-side sounderTop-side soundervs.
from Fabrycky, 1991
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Glossary
Attribute. A decision-maker perceived metric that measures how well a decision maker-defined objective is met
Decision Maker. A type of stakeholder that has significant influence over defining system objectives or allocation of resources (DM)
Design Variables. Designer controlled ‘knobs’ that represent aspects of the system concept
MATE-CON. Multi-Attribute Tradespace Exploration with Concurrent Design couples broad tradespace exploration with explicit decision maker value functions
Tradespace. The space spanned by the completely enumerated design variable set, often represented by (cost, utility) per DM
Utility. Dimensionless parameter that reflects the ‘perceived value under uncertainty’ of an attribute; a ‘rational’ DM seeks to maximize utility
Value. Like Beauty, perception of goodness (value) is often subjective
ConclusionApplicationsAdvancedInsightsMATEMotivation
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Avoiding Point Designs
Cost
Utility
Tradespace exploration enables big picture understanding
Differing types of trades
1. Local point solution trades
2. Frontier subset solutions
3. Frontier solution set
Designi = {X1, X2, X3,…,Xj}
4. Full tradespace exploration
ConclusionApplicationsAdvancedInsightsMATEMotivation
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Multi-Attribute Tradespace Exploration
Context: “Engineering Systems Thinking”
Model/ SimulationInputs Outputs
• Decision Theory
• Design Theory• Lean Research• …
• Similarity analysis• Sensitivity
analyses• Portfolio Theory• Real options• …
• Parametric Models
• Dynamic Models• Integrated
Concurrent Design
• …
“Value-centric Design”
Advanced Model/Sim
Tradespace Analysis Techniques
1
2 3 4Focus of talk
ConclusionApplicationsAdvancedInsightsMATEMotivation
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What is an Architecture Tradespace?
Total Lifecycle Cost($M2002)
Assessment of cost and utility of large space of possible system architectures
ATTRIBUTES: Architectural decision metrics– Data Lifespan (yrs)– Equatorial Time (hrs/day)– Latency (hrs)– Latitude Diversity (deg)– Sample Altitude (km)
Orbital Parameters– Apogee Altitude (km)– Perigee Altitude (km)– Orbit Inclination (deg)
Spacecraft Parameters– Antenna Gain – Communication Architecture– Propulsion Type– Power Type– Total Delta V
DESIGN VARIABLES: Architectural trade parameters
Each point is a specific architecture
Value Attributes Utility
Concept Design Cost
Stakeholders Analysis
Tradespace: {Design,Attributes} {Cost,Utility}Tradespace: {Design,Attributes} {Cost,Utility}
X-TOSSmall low-altitude science mission
kmKm
kmKm
kmKm
Cost, Utility
ConclusionApplicationsAdvancedInsightsMATEMotivation
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40 42 44 46 48 50 52 54 56 58 600.2
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40 42 44 46 48 50 52 54 56 58 600.2
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Original Revised0
0.05
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Latency Latitude EquatorTime
Lifespan Altitude
Weight Factors of each Attribute (k values)
Original Revised
User changed preference weighting
for lifespan
Architecture tradespace re-
evaluated in less than one hour
X-TOSFrom Ross, 2003
Insights: ∆ “Rqmts” Easily Assessed
ConclusionApplicationsAdvancedInsightsMATEMotivation
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0.00
500.00
1000.00
1500.00
2000.00
2500.00
3000.00
3500.00
4000.00
0.00 0.20 0.40 0.60 0.80 1.00
Utility (dimensionless)
Low BipropMedium BipropHigh BipropExtreme BipropLow CryoMedium CryoHigh CryoExtreme CryoLow ElectricMedium ElectricHigh ElectricExtreme ElectricLow NuclearMedium NuclearHigh NuclearExtreme Nuclear
Cos
t ($M
)
Spacetug Tradespace
Insights: Understanding Limiting Physical or Mission Constraints
Hits “wall” of either physics (can’t change!) or utility (can)
BipropCryoElectricNuclear
Prop Type
See McManus and Schuman, 2003
ConclusionApplicationsAdvancedInsightsMATEMotivation
SPACETUG• General purpose orbit
transfer vehicles • Different propulsion
systems and grappling/observation capabilities
• Lines show increasing fuel mass fraction
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Tradespace Exploration w/ Uncertainty
Often learn a lot by simple examinationBetter: Explicitly look at model sensitivities to uncertaintiesUncertainties can be market (shown), policy, or technicalMitigate with portfolio, real options methods
From Walton, 2002
0
100
200
300
400
500
0 0.2 0.4 0.6 0.8 1
Utility (dimensionless)
B Architectures:Changes (in anything)may cause large added cost
A Architectures:Changes (in anything) have less drastic affect; more value may be available for modest added cost
Cos
t
ConclusionApplicationsAdvancedInsightsMATEMotivation
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Portfolio Analysis: 3 DM’s
Optimal Strategy Portfolio
Optimal Strategy Portfolio
Optimal Strategy Portfolio
High Risk AversionModerate Risk Aversion
Low Risk Aversion
Risk “covered” by investment in small system that only does one mission
Low Risk Aversion Portfolio contains “Best Value” Design (I.e. Highest TU/$)
Uncertainty in the High Risk Aversion Portfolio is less than each of its assets
From Walton, 2002
0.72.2Portfolio Value and Uncertainty100%
0.81.9{2,1,3.8,30,1,300}48%
0.92.4{26,4,14.1,60,2,700}52%
UncertaintyTotal Utility/$
Architecture Design Vector{sats/swarm,suborbs,size,yaw,subplaces,alt}
Percentage of Portfolio
0.72.2Portfolio Value and Uncertainty100%
0.81.9{2,1,3.8,30,1,300}48%
0.92.4{26,4,14.1,60,2,700}52%
UncertaintyTotal Utility/$
Architecture Design Vector{sats/swarm,suborbs,size,yaw,subplaces,alt}
Percentage of Portfolio
1.74.2{4,2,3.8,30,1,500}28%
1.13.2Portfolio Value and Uncertainty100%
1.64.1{4,1,14.1,0,1,700}15%
0.92.4{26,4,14.1,60,2,700}57%
UncertaintyTotal Utility/$
Architecture Design Vector{sats/swarm,suborbs,size,yaw,subplaces,alt}
Percentage of Portfolio
1.74.2{4,2,3.8,30,1,500}28%
1.13.2Portfolio Value and Uncertainty100%
1.64.1{4,1,14.1,0,1,700}15%
0.92.4{26,4,14.1,60,2,700}57%
UncertaintyTotal Utility/$
Architecture Design Vector{sats/swarm,suborbs,size,yaw,subplaces,alt}
Percentage of Portfolio
2.25.2Portfolio Value and Uncertainty100%
1.74.2{4,2,3.8,30,1,500}17%
2.35.4{8,4,14.1,30,1,700}83%
UncertaintyTotal Utility/$
Architecture Design Vector{sats/swarm,suborbs,size,yaw,subplaces,alt}
Percentage of Portfolio
2.25.2Portfolio Value and Uncertainty100%
1.74.2{4,2,3.8,30,1,500}17%
2.35.4{8,4,14.1,30,1,700}83%
UncertaintyTotal Utility/$
Architecture Design Vector{sats/swarm,suborbs,size,yaw,subplaces,alt}
Percentage of Portfolio
ConclusionApplicationsAdvancedInsightsMATEMotivation
high
low
A portfolio is investment in multiple designs
If designs are anticorrelated with respect to uncertainties, portfolios can have lower uncertainty than individual designs
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Technology and Programmatic Flexibility Over Time for Space-Based Radar (SBR)
Flexibility Over Time: SBR
“Transitionability” relates to “distance” between architectures
Transition costs vary widely based on transition path
“Optimality” not defined when fitness function changes over time
Pareto Front may not provide best answers
ConclusionApplicationsAdvancedInsightsMATEMotivation
2 3 4 5 6 7 8 9 10 110.8
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98Number of Transition Possibilities: 2002 to 2005 (decreasing alt
Life cycle Cost ($B)
Util
ity
123456789
Cost of flexibility
2 3 4 5 6 7 8 9 10 110.8
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
Life cycle Cost ($B)
Util
ity
123456789
2 3 4 5 6 7 8 9 10 110.8
0.82
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0.86
0.88
0.9
0.92
0.94
0.96
0.98
Life cycle Cost ($B)
Util
ity
123456789
Cost of flexibility
2 3 4 5 6 7 8 9 10 110.8
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98Number of Transition Possibilities: 2002 to 2005 (decreasing altitude)
Life cycle Cost ($B)
Util
ity
123456789
2 3 4 5 6 7 8 9 10 110.8
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
Life cycle Cost ($B)
Util
ity
123456789
Cost of flexibility
2 3 4 5 6 7 8 9 10 110.8
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
Life cycle Cost ($B)
Util
ity
123456789
2 3 4 5 6 7 8 9 10 110.8
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
Life cycle Cost ($B)
Util
ity
123456789
Cost of flexibility?
From Shah, 2004
Additional tech transitions possible based on investment
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Application: Spiral Development
5 6 7 8 9 10x 10
8
0.7
0.75
0.8
0.85
0.9
0.95
Exp Cost ($)
Exp
MA
U
TradespaceParetoFrontFirst Spiral Pareto Set
From Derleth, 2003
Evolution of Capability in Second Spiral for Small Diameter Bomb
Tradespace gives insights in planning for spiral development
Pareto set differentiates over time
Marginal cost versus rank statistics reveal best “baselines”
ConclusionApplicationsAdvancedInsightsMATEMotivation
System concept is a small, airplane-carried bombSpiral two adds new
attributes to utility function
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Application: Comparing Point Designs
Designs from traditional process
From Jilla, 2002
TPF• Terrestrial Planet
Finder - a large astronomy system
• Design space: Apertures separated or connected, 2-D/3-D, sizes, orbits
• Images vs. cost
[Beichman et al, 1999]
ConclusionApplicationsAdvancedInsightsMATEMotivation
Existence of dominated point designs focus discussion
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Cost-capping policy pushes Pareto Front to the right
System concept is a satellite swarm that
samples Earth’s ionosphere
Policy Intervention: $35M Annual Program Budget Cap Imposed by Congress
Application: Budget Cap Policy
From Weigel, 2002
0.98
0.985
0.99
0.995
1
100
Lifecycle Cost ($M)
Util
ity
200 300 400 500 600
0.98
0.985
0.99
0.995
1
100
Lifecycle Cost ($M)
Util
ity
200 300 400 500 600
Nominal architecture
Pareto front, nominal architectures
Cost-capped budget architecture
Pareto front, cost-capped architectures
Key:Nominal architecture
Pareto front, nominal architectures
Cost-capped budget architecture
Pareto front, cost-capped architectures
Key: Policy results in differential cost increases
Policy-robust points remain in Pareto set
ConclusionApplicationsAdvancedInsightsMATEMotivation
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In general, no optimum solution to problem with multiple stakeholders having conflicting needs (See Arrow’s Impossibility Theorem in Hazelrigg, 1996)
Tradespace aids negotiation1. Finding the win-win changes
(moving toward Pareto front)2. Finding the real trades (along
the Pareto front)Negotiation advantage for stakeholders who understand tradespace
Explicit value conflict and congruity become apparent in tradespace
Multiple Decision Maker Tradespace
Application: Multi-DM Negotiation
ConclusionApplicationsAdvancedInsightsMATEMotivation
DM2
Pareto surface
1
2
DM1
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Conclusion
Tradespace exploration is a unified framework that enables…
Consideration of diverse and dynamic value functionsComparison of diverse and dynamic conceptsCharacterization and mitigation of various uncertaintiesQuantification of system properties (e.g., flexibility, robustness)
Applications have included: policy sensitivity analysis, spiral development, cross-proposal evaluation On-going research seeks to standardize TSE, including theory, method and applications
ConclusionApplicationsAdvancedInsightsMATEMotivation
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Citations
Beichman, C.A., Woolf, N.J., and Lindensmith, C.A., "The Terrestrial Planet Finder (TPF): A NASA Origins Program to Search for Habitable Planets," JPL Publication 99-3, May 1999, pp. 1-11, 49-55, 87-89.
Derleth, Jason E. "Multi-Attribute Tradespace Exploration and Its Application to Evolutionary Acquisition." SM, Massachusetts Institute of Technology, 2003.
Fabrycky, W.J. “Life Cycle Cost and Economic Analysis.” Prentice-Hall, NJ. 1991.Hazelrigg, George A. Systems Engineering: An Approach to Information-based Design. Upper
Saddle River, NJ: Prentice Hall, 1996.Jilla, Cyrus D. "A Multiobjective, Multidisciplinary Design Optimization Methodology for the
Conceptual Design of Distributed Satellite Systems." Ph.D., Massachusetts Institute of Technology, 2002.
McManus, H. and T. E. Schuman. Understanding the Orbital Transfer Vehicle Trade Space. AIAA Space 2003 Conference and Exhibition, Long Beach, CA, 2003.
Ross, Adam M. "Multi-Attribute Tradespace Exploration with Concurrent Design as a Value-Centric Framework for Space System Architecture and Design." Dual-SM, Massachusetts Institute of Technology, 2003.
Shah, Nirav B. "Modularity as an Enabler for Evolutionary Acquisition." SM, Massachusetts Institute of Technology, 2004.
Walton, Myles. "Managing Uncertainty in Space Systems Conceptual Design Using Portfolio Theory." PhD, Massachusetts Institute of Technology, 2002.
Weigel, Annalisa L. "Bringing Policy into Space Systems Conceptual Design: Quantitative and Qualitative Methods." PhD, Massachusetts Institute of Technology, 2002.
ConclusionApplicationsAdvancedInsightsMATEMotivation
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More Sources
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“MATE” References (1)
MATE (8 MS theses)Derleth, Jason E. "Multi-Attribute Tradespace Exploration and Its Application to Evolutionary
Acquisition." SM, Massachusetts Institute of Technology, 2003.Diller, Nathan P. "Utilizing Multiple Attribute Tradespace Exploration with Concurrent Design for
Creating Aerospace Systems Requirements." SM, Massachusetts Institute of Technology, 2002.Roberts, Christoper J. "Architecting Strategies Using Spiral Development for Space Based Radar."
SM, Massachusetts Institute of Technology, 2003.Ross, Adam M. "Multi-Attribute Tradespace Exploration with Concurrent Design as a Value-Centric
Framework for Space System Architecture and Design." Dual-SM, Massachusetts Institute of Technology, 2003.
Seshasai, Satwiksai. "A Knowledge Based Approach to Facilitate Engineering Design." M.Eng., Massachusetts Institute of Technology, 2002.
Shah, Nirav B. "Modularity as an Enabler for Evolutionary Acquisition." SM, Massachusetts Institute of Technology, 2004.
Spaulding, Timothy J. "Tools for Evolutionary Acquisition: A Study of Multi-Attribute Tradespace Exploration (MATE) Applied to the Space Based Radar (SBR)." SM, Massachusetts Institute of Technology, 2003.
Stagney, David B. "The Integrated Concurrent Enterprise." SM, Massachusetts Institute of Technology, 2003.
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“MATE” References (2)
MATE-related (3 PhD dissertations)Jilla, Cyrus D. "A Multiobjective, Multidisciplinary Design Optimization Methodology for the
Conceptual Design of Distributed Satellite Systems." Ph.D., Massachusetts Institute of Technology, 2002.
Walton, Myles. "Managing Uncertainty in Space Systems Conceptual Design Using Portfolio Theory." PhD, Massachusetts Institute of Technology, 2002.
Weigel, Annalisa L. "Bringing Policy into Space Systems Conceptual Design: Quantitative and Qualitative Methods." PhD, Massachusetts Institute of Technology, 2002.
Precursor thesesBrowning, Tyson R. "Modeling and Analyzing Cost, Schedule, and Performance in Complex System
Produce Development." PhD, Massachusetts Institute of Technology, 1998.Delquie, Philippe. "Contingent Weighting of the Response Dimension in Preference Matching."
Ph.D., Massachusetts Institute of Technology, 1989.Nolet, Simon. "Development of a Design Environment for Integrated Concurrent Engineering in
Academia." M. Eng., Massachusetts Institute of Technology, 2001.Shaw, Graeme B. "The Generalized Information Network Analysis Methodology for Distributed
Satellite Systems." Sc.D., Massachusetts Institute of Technology, 1999.