Multiple Criteria Optimization and Analysis in the Planning of Effects-Based Operations (EBO)
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Transcript of Multiple Criteria Optimization and Analysis in the Planning of Effects-Based Operations (EBO)
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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 LaboratoryHelsinki 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 problemMultiple criteria influence diagrams in EBO
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1. Identify higher level objective2. Describe operation as a system3. Derive effects from the
higher-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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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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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
CriterionUnemployment
CriterionMedia coverage
Effect
Publicunrest
Country
Car factory
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System model– Elements = System variables – Dependencies between elements
Actions : Element states Criteria
The EBO problem
Planning EBO as an MCDM problem
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and feasible
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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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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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EBOLATOR - Decision support tool Implementation utilizing MCID Construction of system model
(GeNIe, 2009)
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EBOLATOR - Graphical user interface Visualization of actions Calculation of efficient set Criteria weights Single action
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EBOLATOR - Sensitivity analysis Weights MCID probabilities
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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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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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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. 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