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A method of approximating Pareto sets for assessments of implicit Pareto set elements

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Języki publikacji
EN
Abstrakty
EN
Deriving efficient variants in complex multiple criteria decision making problems requires optimization. This hampers greatly broad use of any multiple criteria decision making method. In multiple criteria decision making Pareto sets, i.e. sets of efficient vectors of criteria values corresponding to feasible decision alternatives, are of primal interest. Recently, methods have been proposed to calculate assessments for any implicit element of a Pareto set (i.e. element which has not been derived explicitly but has been designated in a form which allows its explicit derivation, if required) when a finite representation of the Pareto set is known. In that case calculating respective bounds involves only elementary operations on numbers and does not require optimization. In this paper the problem of approximating Pareto sets by finite representations which assure required tightness of bounds is considered for bicriteria decision making problems. Properties of a procedure to derive such representations and its numerical behavior are investigated.
Rocznik
Strony
367--381
Opis fizyczny
Bibliogr. 13 poz., wykr.
Twórcy
Bibliografia
  • BEASLEY’S OR-LiBRARY at http://people.brunel.ac.uk/~mastjjb/jeb/orlib/ portinfo.html.
  • BURKARD, R.E., HAMACHER, H.W. and ROTE, G. (1987) Approximation of convex functions and applications in mathematical programming. Report 89, Institut für Mathematik, Technische Universität Graz.
  • COHON, J.L. (1978) Multiobjective Programming and Planning. Academic Press, New York.
  • CHANG, T.-J., MEADE, N., BEASLEY, J.E. and SHARAIHA, Y.M. (2000) Heuristics for cardinality constrained portfolio optimisation. Computers & Operations Reasearch 27, 1271-1302.
  • ELTON, E.J. and GRUBER, M.J. (1995) Modern Portfolio Theory and Investment Analysis. John Wiley & Sons, New York.
  • FRUHWIRTH, B., BURKARD, R.E. and ROTHE, G. (1989) Approximation of convex curves with applications to the bicriterial minimum cost flow problem. European Journal of Operational Research 42, 326-338.
  • KALISZEWSKI, I. (2004) Out of the mist-towards decision-maker-friendly multiple criteria decision making support. European Journal of Operational Research 158, 293-307.
  • KALISZEWSKI, I. (2006) Soft Computing for Complex Multiple Criteria Decision Making. Springer.
  • MARKOWITZ, H.M. (1959) Portfolio Selection, Efficient Diversification of Investments. John Wiley & Sons, New York.
  • MIETTINEN, K.M. (1999) Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, Dordrecht.
  • OGRYCZAK, W. (2002) Multiple criteria optimization and decision under risk. Control and Cybernetics 31, 975-1003.
  • RUHE, G. and FRUHWIRTH B. (1990) ε-optimality for bicriteria programs and its application to minimum cost flows. Computing 44, 21-34.
  • YANG, X.Q. and GOH, C.J. (1997) A method for convex curve approximation. European Journal of Operations Research 97, 205-212.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0017-0045
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