Transcript Multi-Criteria Optimization and Analysis in the Planning

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
[email protected], [email protected], [email protected]
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Effects-based operations (EBO)
 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
CONTENTS
Planning of EBO = MCDM problem
Multiple criteria influence diagrams in EBO
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Steps in EBO planning
1. Identify higher level objective
2. Describe operation as a system
3. Derive effects from the
higher-level objective
System
Threatening
military buildup
in a country
 First described qualitatively
4. Find actions which contribute to the
fulfillment of effects
 How to measure the fulfillment
of effects?
Actions
• Economic
sanctions
• Missile strike
• Etc.
Effects
• Public unrest
• Etc.
 Criteria
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Description of the system
Country
 Functionally related elements
 Elements have states
– E.g. works / out of order
Element
Car factory
Dependency
Car factory goes out of business
if steel mill doesn’t produce steel
Element
Steel mill
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Qualitative modeling


Effects described by one or multiple criteria
Criteria defined in terms of system elements
–

Country
Multiple elements related to single criterion
Criteria make effects measurable
Car factory
Effect
Criterion
Unemployment
Public
unrest
Criterion
Media coverage
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Planning EBO as an MCDM problem
 System model
–
–
Elements = System variables x  [ x1,, xm ]
Dependencies between elements xi  hi (xi ) , xi  [ x1,, xi 1 , xi 1 ,, xm ]
 Actions d : Element states x j  g j (d, x j )
 Criteria f k (x)  f k ( g j (d, x j );  j )
The EBO problem
max( f1 (x), f 2 (x),, f n (x))
d
s.t.
xi  hi (x i )
x j  g j (d, x  j )
feasible x and d
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Planning EBO as an MCDM problem
Country
System

Actions
• Economic
sanctions
• Missile strike
• Etc.
x  [ x1 ,, xm ]
xi  [ x1 ,, xi 1 , xi 1 , xm ]
xi  hi (xi )
x j  g j (d, x j )
Effects
• Public unrest
• Etc.
Actions
Criteria
d
f k (x)
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Previous literature
 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
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Multiple criteria influence diagram (MCID)
System
 Bayesian network used as a system model
– Elements: chance nodes /
random variables
x2
x1
x3
x4
– Dependencies: arcs /
conditional probabilities
x5
x6
x7
Dn
U1
 MCID (Diehl & Haimes, 2004)
– Actions represented by decision nodes
– Criteria represented by utility nodes
D1
...
Actions
...
Criteria
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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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Conclusions
 Multiple criteria and systems perspective
essential 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
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References 1/2

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
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References 2/2

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
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