1. 1 S ystems Analysis Laboratory Helsinki 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. 2 S ystems Analysis Laboratory Helsinki 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. 3 S ystems Analysis Laboratory Helsinki University of Technology 1.Identify higher level objective 2.Describe operation as a system 3.Derive effects from the higher-level objective  First described qualitatively 4.Find actions which contribute to the fulfillment of effects  How to measure the fulfillment of effects?  Criteria Steps in EBO planning Threatening military buildup in a country Public unrest Etc. Economic sanctions Missile strike Etc. ActionsEffects System

4. 4 S ystems Analysis Laboratory Helsinki University of Technology  Functionally related elements  Elements have states –E.g. works / out of order Description of the system Country Element Car factory Element Steel mill Dependency Car factory goes out of business if steel mill doesn’t produce steel

5. 5 S ystems Analysis Laboratory Helsinki 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 Public unrest Country Car factory

6. 6 S ystems Analysis Laboratory Helsinki University of Technology  System model –Elements = System variables –Dependencies between elements  Actions : Element states  Criteria The EBO problem Planning EBO as an MCDM problem

7. 7 S ystems Analysis Laboratory Helsinki University of Technology Planning EBO as an MCDM problem CriteriaActions System Public unrest Etc. Economic sanctions Missile strike Etc. ActionsEffects Country

8. 8 S ystems Analysis Laboratory Helsinki 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. 9 S ystems Analysis Laboratory Helsinki 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) CriteriaActions System...

10. 10 S ystems Analysis Laboratory Helsinki University of Technology EBOLATOR - Decision support tool  Implementation utilizing MCID  Construction of system model (GeNIe, 2009)

11. 11 S ystems Analysis Laboratory Helsinki University of Technology EBOLATOR - Graphical user interface  Visualization of actions  Calculation of efficient set  Criteria weights  Single action

12. 12 S ystems Analysis Laboratory Helsinki University of Technology EBOLATOR - Sensitivity analysis  Weights  MCID probabilities

13. 13 S ystems Analysis Laboratory Helsinki 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. 14 S ystems Analysis Laboratory Helsinki University of Technology  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 Conclusions

15. 15 S ystems Analysis Laboratory Helsinki 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. 16 S ystems Analysis Laboratory Helsinki 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