The Tradespace Exploration Paradigm - SEAri at...

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

INCOSE 2005, © 2005 by Adam Ross and Daniel Hastings

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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



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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



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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



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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



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40 42 44 46 48 50 52 54 56 58 600.2

0.3

0.4

0.5

0.6

0.7

0.8

40 42 44 46 48 50 52 54 56 58 600.2

0.3

0.4

0.5

0.6

0.7

0.8

Original Revised0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

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



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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


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



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Portfolio Analysis: 3 DM’s

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


0.81.9{2,1,3.8,30,1,300}48%

0.92.4{26,4,14.1,60,2,700}52%




1.74.2{4,2,3.8,30,1,500}28%


1.64.1{4,1,14.1,0,1,700}15%

0.92.4{26,4,14.1,60,2,700}57%




1.74.2{4,2,3.8,30,1,500}28%


1.64.1{4,1,14.1,0,1,700}15%

0.92.4{26,4,14.1,60,2,700}57%





1.74.2{4,2,3.8,30,1,500}17%

2.35.4{8,4,14.1,30,1,700}83%





1.74.2{4,2,3.8,30,1,500}17%

2.35.4{8,4,14.1,30,1,700}83%





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


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


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


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)


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


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


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


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”


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]


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



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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


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



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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.



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.

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