Skip to main content
Log in

Algebra of Efficient Sets for Multiobjective Complex Systems

  • Published:
Journal of Optimization Theory and Applications Aims and scope Submit manuscript

Abstract

Complex systems are modeled as collections of multiobjective programs representing interacting subsystems of the overall system. Since the calculation of efficient sets of these complex systems is challenging, it is desirable to decompose the overall system into component multiobjective programs, that are more easily solved and then construct the efficient set of the overall system. For some classes of complex systems, algebraic properties of set operations and relations are developed between the efficient set of the overall system and the efficient sets of subproblems. The properties indicate that multiple decomposition and coordination schemes, with varying assumptions regarding the system, may be applied to the same initial system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Abbreviations

n :

The set of all n×1 real vectors

X :

The feasible set

\(\mathbb{R}^{p}_{\geqq}\) :

The set of all p×1 real vectors whose components are all nonnegative

\(\mathcal{E}\) :

The efficiency operator, see definition (2)

fr:

Standard function composition, (fr)(x)=f(r(x)) for all xX

References

  1. Lee, S.M., Rho, B.H.: Multicriteria decomposition model for two-level, decentralized organizations. Int. J. Policy Inf. 9(1), 119–133 (1985)

    Google Scholar 

  2. Li, D., Haimes, Y.Y.: Hierarchical generating method for large-scale multiobjective systems. J. Optim. Theory Appl. 54(2), 303–333 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  3. Li, D., Haimes, Y.Y.: Multilevel methodology for a class of non-separable optimization problems. Int. J. Syst. Sci. 21(11), 2351–2360 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  4. Haimes, Y.Y., Tarvainen, K., Shima, T., Thadathil, J.: Hierarchical Multiobjective Analysis of Large-Scale Systems. Hemisphere, New York (1990)

    Google Scholar 

  5. Tanino, T., Satomi, H.: Optimization methods for two-level multiobjective problems. In: Lewandowski, A., Volkovich, V. (eds.) Multiobjective Problems of Mathematical Programming, pp. 128–137. Springer, Berlin (1991)

    Google Scholar 

  6. Lieberman, E.R.: Multi-Objective Programming in the USSR. Academic Press, Boston (1991)

    MATH  Google Scholar 

  7. Gomez, T., Gonzalez, M., Luque, M., Miguel, M., Ruiz, F.: Multiple objectives decomposition-coordination methods for hierarchical organizations. Eur. J. Oper. Res. 133(2), 323–341 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  8. Caballero, R., Gomez, T., Luque, M., Miguel, F., Ruiz, R.: Hierarchical generation of Pareto optimal solutions in large-scale multiobjective systems. Comput. Oper. Res. 29, 1537–1558 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  9. Tappeta, R.V., Renaud, J.E.: Multiobjective collaborative optimization. J. Mech. Des. 119, 403–411 (1997)

    Article  Google Scholar 

  10. Huang, Ch.H., Bloebaum, C.L.: Multi-objective Pareto concurrent subspace optimization for multidisciplinary design. In: Proceedings of the 42nd AIAA Aerospace Sciences Meeting and Exhibit. AIAA 2004-278 (2004)

    Google Scholar 

  11. Huang, Ch.H., Bloebaum, C.L.: Visualization as a solution aid for multi-objective concurrent subspace optimization in a multidisciplinary design environment. In: Proceedings of the 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. AIAA 2004-4464 (2004)

    Google Scholar 

  12. Huang, Ch.H., Bloebaum, C.L.: Incorporation of preferences in multi-objective concurrent subspace optimization for multidisciplinary design. In: Proceedings of the 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. AIAA 2004-4548 (2004)

    Google Scholar 

  13. Rabeau, S., Dépincé, P., Bennis, F.: Collaborative optimization of complex systems: a multidisciplinary approach. Int. J. Interact. Des. Manuf. 1, 209–218 (2007)

    Article  Google Scholar 

  14. Azarm, S., Sobieszczanski-Sobieski, J.: Reduction method with system analysis for multiobjective optimization-based design. NASA Contractor Report 191456—ICASE Report No. 93-22 (1993)

  15. Lazimy, R.: Solving multiple criteria problems by interactive decompositions. Math. Program. 35(3), 334–361 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  16. Benson, H.P., Sun, E.: Outcome space partition of the weight set in multiobjective linear programming. J. Optim. Theory Appl. 105(1), 17–36 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  17. Wierzbicki, A.P., Granat, J., Makowski, M.: Discrete decision problems with large number of criteria. Interim Report IR-07-025, International Institute for Applied Systems Analysis, Laxenburg, Austria (2007)

  18. Engau, A.: Domination and Decomposition in Multiobjective Programming. Ph.D. Thesis, Department of Mathematical Sciences, Clemson University (2007)

  19. Engau, A., Wiecek, M.M.: Interactive coordination of objective decompositions in multiobjective programming. Manag. Sci. 54(7), 1350–1363 (2008)

    Article  Google Scholar 

  20. Ward, J.: Structure of efficient sets for convex objectives. Math. Oper. Res. 14(2), 249–257 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  21. Malivert, Ch., Boissard, N.: Structure of efficient sets for strictly quasi-convex objectives. J. Convex Anal. 1(2), 143–150 (1995)

    MathSciNet  Google Scholar 

  22. Ehrgott, M., Nickel, S.: On the number of criteria needed to decide Pareto optimality. Math. Methods Oper. Res. 55(3), 329–345 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  23. Popovici, N.: Pareto reducible multicriteria optimization problems. Optimization 54(3), 253–263 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  24. Li, D., Haimes, Y.Y.: The envelope approach for multiobjective optimization problems. IEEE Trans. Syst. Man Cybern. 6, 1026–1038 (1987)

    MathSciNet  Google Scholar 

  25. Haftka, R.T., Watson, L.T.: Multidisciplinary design optimization with quasiseparable subsystems. Optim. Eng. 6, 9–20 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  26. Haftka, R.T., Watson, L.T.: Decomposition theory for multidisciplinary design optimization problems with mixed integer quasiseparable subsystems. Optim. Eng. 7, 135–149 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  27. Gardenghi, M.: Multiobjective Optimization for Complex Systems. Ph.D. Thesis, Department of Mathematical Sciences, Clemson University (2009)

  28. Faulkenberg, S.: Bilevel mathematical programs: Methodology and application in packaging. M.S. Project, Department of Mathematical Sciences, Clemson University (2005)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Margaret M. Wiecek.

Additional information

Communicated by H.P. Benson.

M. Gardenghi partially supported by the National Science Foundation Grant CMMI 0621055.

M.M. Wiecek partially supported by the National Science Foundation Grant CMMI 0621055.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gardenghi, M., Gómez, T., Miguel, F. et al. Algebra of Efficient Sets for Multiobjective Complex Systems. J Optim Theory Appl 149, 385–410 (2011). https://doi.org/10.1007/s10957-010-9786-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10957-010-9786-y

Keywords

Navigation