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Composite Programming as an Extension of Compromise Programming

  • Andras Bárdossy
  • Istvan Bogárdi
  • Lucien Duckstein
Part of the International Centre for Mechanical Sciences book series (CISM, volume 289)

Abstract

Cost-effective control alternatives are selected by composite programming /an extension of compromise programming/ in order to find a trade-off among objectives or groups of criteria usually facing watershed management or observation network design: economic criteria /agricultural revenue and investment/, environmental criteria /yields of sediment and nutrient/, and hydrologic criteria /water yield/. Composite programming provides a two-level tradeoff analysis: first with different-L-norms within the criteria, then again with a different L norm among the three objectives.

An example of six interconnected watersheds draining into a multipurpose /water supply and recreation/ reservoir and a network design problem of aquifer parameters illustrate the methodology.

Keywords

Sediment Yield Ideal Point Water Yield Watershed Management Goal Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Zeleny, M., Compromise Programming in Multiple Criteria Decision Making, M.K. Starr and M. Zeleny eds., Univ. of South Carolina Press, 101, 1973Google Scholar
  2. Gershon, M.E., Model Choice in Multiobjective Decision Making in Water and Mineral Resource Systems, Technical Reports, The University of Arizona, Tucson, Arizona, 85721, 155, 1981.Google Scholar
  3. 3.
    BogArdi, I. and A. Bardossy, Network design for the spatial estimation of environmental variables, Applied Math. and Comput, 12, 339, 1983.CrossRefMATHGoogle Scholar
  4. 4.
    Miller, W.L. and D.M. Byers, Development and display of multiple objective projects impacts,,Water Resour. Res., 9 /4/, 11, 1973.Google Scholar
  5. 5.
    Das, P. and Y.Y. Haimes, Multiobjective optimization in water quality and land management, Water Resour. Res.Google Scholar
  6. 15.
    /, 1313, 1979.Google Scholar
  7. 6.
    Boga.rdi, I., David, L. and L. Duckstein, Trade-off between cost and effectiveness of control of nutrient loading into a waterbody, Research Report, RR-83–19, IIASA, Laxenburg, Austria, July 1983.Google Scholar
  8. 7.
    Williams, J.R. Sediment yield prediction with universal equation using runoff energy factor, Agr.Res.Serv., ARS-S-40, USDA, Washington D.C., 244, 1975.Google Scholar
  9. 8.
    Bârdossy, A., Bog.rdi, I, and L. Duckstein, Accuracy of sediment yield calculation, Working paper, Tiszadata, Budapest, 1984.Google Scholar
  10. 9.
    Soil Conservation Service, National Engineering Handbook, Section 4, Hydrology, USDA, Washington D.C., 1971.Google Scholar
  11. 10.
    Bogdrdi, I. and A. Bdrdossy, A concentration model of P stemming from agricultural watersheds, Research Report /in Hungarian/, Tiszadata, Budapest, 1984.Google Scholar
  12. 11.
    Volpi, G., G. Gambolati, L. Carbognin, P. Gatto and G. Mozzi, Groundwater contour mapping in Venice by stochastic interpolators, Water Resour. Res., 15 /2/, 291, 1979.Google Scholar
  13. 12.
    Matheron, G., Les variables régionalisées et leur estimation, 306, Masson, Paris, 1967.Google Scholar
  14. Dunford, N. and J.T. Schwartz, Linear Operators Part I., Interscience Publisher, New York, 358, 1976.Google Scholar
  15. Freimer, M. and P.I. Yu, Some new results on compromise solutions for group decision problems, Management Science, 22 /6/, 688, February 1976.Google Scholar
  16. 15.
    Yu, P.L. and G. Leitmann, Compromise Solutions, Domination Structures, and Szlukyadze’s Solution, in Multi-criteria Decision Making and Differential Games, edited by G. Leitmann, Plenum Press, New York, 85, 1976.Google Scholar
  17. 16.
    Duckstein, L. and S. Opricovic, Multiobjective optimization in river basin developments, Water Resour. Res., 16 /1/, 14, 1980.Google Scholar
  18. 17.
    Wilde, D.J. and C.S. Beightler, Foundations of Optimization, Prentice-Hall, Inc., 480, 1967.Google Scholar
  19. 18.
    B6rdossy, A., Mathematics of composite programming, Working paper, Tiszadata, Mik6 u. 1. 1012. Budapest, Hungary, 1984.Google Scholar

Copyright information

© Springer-Verlag Wien 1985

Authors and Affiliations

  • Andras Bárdossy
  • Istvan Bogárdi
  • Lucien Duckstein
    • 1
  1. 1.Systems and Industrial Engineering DepartmentUniversity of ArizonaUSA

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