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

jouni.pousi@hut.fi, kai.virtanen@hut.fi, raimo@hut.fi

2

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

3

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

4

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

5

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

6

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

7

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

8

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

9

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

10

S ystemsAnalysis LaboratoryHelsinki University of Technology

EBOLATOR - Decision support tool

Implementation utilizing MCID Construction of system model

(GeNIe, 2009)

11

S ystemsAnalysis LaboratoryHelsinki University of Technology

EBOLATOR - Graphical user interface Visualization of actions Calculation of efficient set Criteria weights Single action

12

S ystemsAnalysis LaboratoryHelsinki University of Technology

EBOLATOR - Sensitivity analysis

Weights MCID probabilities

13

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

14

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

15

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

16

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