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Transcript of 1 S ystems Analysis Laboratory Helsinki University of Technology Multiple Criteria Optimization and...
1
S ystemsAnalysis LaboratoryHelsinki University of Technology
Multiple Criteria Optimization and Analysis in the Planning of Effects-Based Operations (EBO)
Jouni Pousi, Kai Virtanen and Raimo P. Hämäläinen
Systems Analysis Laboratory
Helsinki University of Technology
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Concept for planning and executing military operations(e.g., Davis, 2001)
– Complex military operations, systems perspective
How to produce effects in a system?– Single action produces multiple effects
Effects-based operations (EBO)
CONTENTS
Planning of EBO = MCDM problem
Multiple criteria influence diagrams in EBO
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S ystemsAnalysis LaboratoryHelsinki University of Technology
1. Identify higher level objective
2. Describe operation as a system
3. Derive effects from thehigher-level objective
First described qualitatively
4. Find actions which contribute to the fulfillment of effects
How to measure the fulfillmentof effects?
Criteria
Steps in EBO planning
Threateningmilitary buildup
in a country
• Public unrest• Etc.
• Economic sanctions
• Missile strike• Etc.
Actions Effects
System
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Functionally related elements Elements have states
– E.g. works / out of order
Description of the systemCountry
ElementCar factory
ElementSteel mill
DependencyCar factory goes out of businessif steel mill doesn’t produce steel
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Effects described by one or multiple criteria Criteria defined in terms of system elements
– Multiple elements related to single criterion
Criteria make effects measurable
Qualitative modeling
Criterion
Unemployment
Criterion
Media coverage
Effect
Publicunrest
Country
Car factory
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S ystemsAnalysis LaboratoryHelsinki University of Technology
System model– Elements = System variables
– Dependencies between elements
Actions : Element states Criteria
The EBO problem
Planning EBO as an MCDM problem
dx
xd
x
xxxd
and feasible
),(
)(..
))(,),(),((max 21
jjj
iii
n
gx
hxts
fff
));,(()( jgff jjkk xdx
),( jjj gx xdd
],,[ 1 mxx x,)( iii hx x ],,,,,[ 111 miii xxxx x
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Planning EBO as an MCDM problem
CriteriaActions
d )(xkf
System
)( iii hx x
],,[ 1 mxx x
],,,,[ 111 miii xxxx x
),( jjj gx xd
• Public unrest• Etc.
• Economic sanctions
• Missile strike• Etc.
Actions Effects
Country
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Probabilistic modeling (Davis, 2001) System dynamics (Bakken et al., 2004) Bayesian networks (Tu et al., 2004)
– Single criterion Combination of Bayesian networks and Petri nets
(Wagenhals & Levis, 2002; Haider & Levis, 2007)– Effects over time– Efficient set not determined
Agent-based modeling (Wallenius & Suzic, 2005)– Calculates criteria given an action– Efficient set not determined
Outranking methods (Guitouni et al., 2008)– No system model
Previous literature
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Bayesian network used as a system model
– Elements: chance nodes /
random variables
– Dependencies: arcs /
conditional probabilities
MCID (Diehl & Haimes, 2004)
– Actions represented by decision nodes
– Criteria represented by utility nodes
Multiple criteria influence diagram (MCID)
1x
6x
4x
1D
3x
7x
1U mU
2x
CriteriaActions
System
nD... ...
5x
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S ystemsAnalysis LaboratoryHelsinki University of Technology
EBOLATOR - Decision support tool
Implementation utilizing MCID Construction of system model
(GeNIe, 2009)
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S ystemsAnalysis LaboratoryHelsinki University of Technology
EBOLATOR - Graphical user interface Visualization of actions Calculation of efficient set Criteria weights Single action
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S ystemsAnalysis LaboratoryHelsinki University of Technology
EBOLATOR - Sensitivity analysis
Weights MCID probabilities
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S ystemsAnalysis LaboratoryHelsinki University of Technology
EBOLATOR - Example analysis
Defensive air operation System
– Civil and military infrastructure
Actions– Aircraft positioning and
air combat tactics MCID
– 12000 probabilities– 729 actions
Analysis– 13 efficient actions– Sensitivity analysis
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S ystemsAnalysis LaboratoryHelsinki University of Technology
Multiple criteria and systems perspectiveessential in planning EBO
Similar philosophy applicable in other application areas (e.g., hospital, marketing)
Previous modeling techniques improved by MCDM
Successful implementation: EBOLATOR
Multiple criteria influence diagram is an interesting modeling approach in MCDM
Conclusions
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S ystemsAnalysis LaboratoryHelsinki University of Technology
B. T. Bakken, M. Ruud and S. Johannessen, “The System Dynamics Approach to
Network Centric Warfare and Effects-Based Operations - Designing a ``Learning Lab''
for Tomorrow's Military Operations”, Proceedings of the 22nd International Conference
of the System Dynamics Society, Oxford, England, July 25-29, 2004
P. K. Davis, “Effects-Based Operations: A Grand Challenge for the Analytical
Community”, RAND, 2001
M. Diehl and Y. Y. Haimes, “Influence Diagram with Multiple Objectives and Tradeoff
Analysis” , IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and
Humans, vol. 34, no. 3, 2004
A. Guitouni, J. Martel, M. Bélanger and C. Hunter, “Multiple Criteria Courses of
Action Selection”, MOR Journal, vol. 13, no. 1, 2008
Decision Systems Laboratory of the University of Pittsburgh, “Graphical Network
Interface”, http://dsl.sis.pitt.edu, 2009
References 1/2
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S ystemsAnalysis LaboratoryHelsinki University of Technology
S. Haider and A. H. Levis, ”Effective Course-of-Action Determination to Achieve
Desired Effects”, IEEE Transactions on Systems, Man and Cybernetics - Part A:
Systems and Humans, vol. 37, no. 2, 2007
H. Tu, Y. N. Levchuk and K. R. Pattipati, “Robust Action Strategies to Induce Desired
Effects”, IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and
Humans, vol. 34, no. 5, 2004
L. W. Wagenhals and A. H. Levis, “Modeling Support of Effects-Based Operations in
War Games”, Proceedings of the Command and Control Research and Technology
Symposium, Monterey, California, USA, June 11-13, 2002
K. Wallenius and R. Suzic, “Effects Based Decision Support For Riot Control:
Employing Influence Diagrams and Embedded Simulation”, Proceedings of the Military
Communications Conference, Atlantic City, New Jersey, USA, October 17-20, 2005
References 2/2