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Constraints

, Volume 19, Issue 2, pp 174–185 | Cite as

Strategic decision making on complex systems

  • Michela Milano
  • Michele Lombardi
Article

Abstract

In this paper, we propose a challenging research direction for Constraint Programming and optimization techniques in general. We address problems where decisions to be taken affect and are affected by complex systems, which exhibit phenomena emerging from a collection of interacting objects, capable to self organize and to adapt their behaviour according to their history and feedback. Such systems are unfortunately impervious to modeling efforts via state-of-the-art combinatorial optimization techniques. We provide some hints on approaches to connect and integrate decision making and optimization technology with complex systems via machine learning, game theory and mechanism design. In the first case, the aim is to extract modeling components to express the relation between global decisions and observables emerging from the real system, or from an accurate predictive model (e.g. a simulator). In the second case, the idea is to exploit game theory, mechanism design and distributed decision making to drive the process toward realistic equilibrium points avoiding globally optimal, but unrealistic, configurations. We conclude by observing how dealing with the complexity of the considered problems will require to greatly extend the capabilities of state of the art solvers: in this context, we identify some key issues and highlight future research directions.

Keywords

Combinatorial optimization Complex systems Constraint programming Machine learning Simulation Game theory Hybrid optimization Large scale problems 

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References

  1. 1.
    Lallouet, A., Lopez, M., Martin, L., Vrain, C. (2010). On learning constraint problems. In Proceedings of ICTAI.Google Scholar
  2. 2.
    Anderson, P.W. (1995). Physics: the opening to complexity? In Proceedings of the national academic science.Google Scholar
  3. 3.
    Bartolini, A., Lombardi, M., Milano, M., Benini, L. (2011). Neural constraint for solving real world problems. In Proceedings of CP 2011.Google Scholar
  4. 4.
    Bartolini, A., Lombardi, M., Milano, M., Benini, L. (2012). Optimization and controlled systems: a case study on thermal aware workload dispatching. In Proceedings of AIII 2012.Google Scholar
  5. 5.
    Beldiceanu, N., & Simonis, H. (2011). A constraint seeker: finding and ranking global constraints from examples. In Proc. of CP 2011.Google Scholar
  6. 6.
    Benders, J.F. (1962). Partitioning procedures for solving mixed-variables programming problems. Numerische Mathematik, 4, 238–252.CrossRefMATHMathSciNetGoogle Scholar
  7. 7.
    Bertsekas, D.P., & Tsitsiklis, J. (1996). Neuro-dynamic programming. Belmont: Athena Scientific.MATHGoogle Scholar
  8. 8.
    Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Computing Surveys, 35(3).Google Scholar
  9. 9.
    Camacho, E., & Alba, C. (2013). Model predictive control. Berlin: Springer.Google Scholar
  10. 10.
    Deng, G. (2007). Simulation-based optimization. PhD thesis, University of Wisconsin – Madison.Google Scholar
  11. 11.
    EURECOM. Survey on mobility models for vehicular ad hoc networks: a survey and taxonomy. http://www.eurecom.fr/util/publidownload.fr.htm?id=1951.
  12. 12.
    Fischetti, M., & Lodi, A. (2003). Local branching. Mathematical Programming, 98, 23–47.Google Scholar
  13. 13.
    Focacci, F., Laburthe, F., Lodi, A. (2003). Local search and constraint programming: Ls and cp illustrated on a transportation problem. In Constraint and integer programming. Kluwer.Google Scholar
  14. 14.
    Fu, M.C. (1994). Optimization via simulation: a review. Annals of Operations Research, 53, 199–247.Google Scholar
  15. 15.
    Glover, F., Kelly, J.P., Laguna, M. (1999). New advances for wedding optimization and simulation. In Proceedings of the winter simulation conference.Google Scholar
  16. 16.
    Grüne, L., & Pannek, J. (2013). Nonlinear model predictive control: theory and algorithms. Berlin: Springer.Google Scholar
  17. 17.
    Loughlin, D.H., Ranjithan Jr., S.R., Baugh, J.W., Brill Jr., E.D. (2000). Application of gas for the design of ozone control strategies. Journal of the Air and Waste Management Association, 50, 1050–1063.Google Scholar
  18. 18.
    Helbing, D. (2001). Traffic and related self-driven many-particle systems. Reviews of Modern Physics, 73, 1067.Google Scholar
  19. 19.
    Holland, A., & O’Sullivan, B. (2012). Survey of game theoretic tools in dynamic environments for policy management. Technical report, 4C. ePolicy Project Deliverable 5.1.Google Scholar
  20. 20.
    Hooker, J.N. (2003). Logic-based benders decomposition. Mathematical Programming, 96, 33–60.Google Scholar
  21. 21.
    Junker, U., Karisch, S.E., Kohl, N., Vaaben, B., Fahle, T., Sellmann, M. (1999). A framework for constraint programming based column generation. In Proceedings of CP1999.Google Scholar
  22. 22.
    Kaneco, K. (2006). Life: an introduction to complex systems biology. Berlin: Springer.Google Scholar
  23. 23.
    Law, A.M., & Kelton, W.D. (2000). Simulation modeling and analysis. New York: Mc Graw Hill.Google Scholar
  24. 24.
    Levy, M., Levy, H., Solomon, S. (2000). Microscopic simulation of financial markets; from investor behaviour to market phenomena. New York: Academic.Google Scholar
  25. 25.
    Lubin, B., Kephart, J.O., Das, R., Parkes, D.C. (2009). Expressive power-based resource allocation for data centers. In Proceedings of IJCAI 2009.Google Scholar
  26. 26.
    Marriott, K., & Stuckey, P. (1998). Programming with constraints: an introduction. New York: MIT Press.MATHGoogle Scholar
  27. 27.
    Milano, M. (2003). Constraint and integer programming: toward a unified methodology. Norwell: Kluwer.Google Scholar
  28. 28.
    Milano, M., & Van Hentenryck, P. (2010). Hybrid optimization: the ten years of CPAIOR. Berlin: Springer.Google Scholar
  29. 29.
    Nemhauser, G., & Wolsey, L. (1988). Integer and combinatorial optimization. Wiley Interscience Series in Discrete Mathematics and Optimization.Google Scholar
  30. 30.
    Sasaki, S., Obayashi, D., Takeguchi, Y., Hiroses, N. (2000). Multiobjective evolutionary computation for supersonic wing-shape optimization. IEEE Transactions on Evolutionary Computation, 4, 182–187.Google Scholar
  31. 31.
    Ohrimenko, O., Stuckey, P.J., Codish, M. (2009). Propagation via lazy clause generation. Constraints, 14(3), 357–391.Google Scholar
  32. 32.
    Haupt, R.L., & Haupt, S.E. (2004). Practical genetic algorithms. New York: Wiley.MATHGoogle Scholar
  33. 33.
    Ruggiero, M., Guerri, A., Bertozzi, D., Milano, M., Benini, L. (2010). A fast and accurate technique for mapping parallel applications on stream-oriented mpsoc platforms with communication awareness. International Journal of Parallel Programming, 36(1), 3–36.CrossRefGoogle Scholar
  34. 34.
    Smith, D.M.D., & Johnson, N.F. (2006). Predictability, risk and online management in a complex system of adaptive agents. arXiv:0605065.
  35. 35.
    Bar Yam, Y. (1997). Dynamic of complex systems. Reading: Addison Wesley.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.DISIUniversity of BolognaBolognaItaly

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