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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/1317 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/1317 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/1317 archive 351 disk0/00/00/12/99 2016-01-15 01:47:34 2021-08-27 17:09:43 2016-01-15 01:47:34 monograph show 2 Avenhaus R. AL0013 Beedgen R. AL0022 Chernavsky S. AL0068 Schrattenholzer L. AL1189 Hoelzl A. AL0165 pub iis_ene iis_met iis_mod prog_enp A frequently used approach to linear programming problems with only vaguely known coefficients of the objective function is to treat these coefficients as random variables; this means that the lack of knowledge is described by a distribution function. For the case in which such a procedure cannot be justified, S.Ya. Chernavsky and A.D. Virtzer of the working Consultative Group for the President of the Academy of Sciences of the USSR developed a decision theoretical approach, some aspects of which are described here for pedagogical purposes. In this paper first the problem of handling uncertainties in linear programming models is outlined, and the decision criteria to be used are explained. Thereafter, a method of finding optimal strategies under uncertain values of the objective function coefficients is described. Finally, the method is applied to a simple uncertainty case of the MESSAGE model. 1980-11 published WP-80-170 WP-80-170 1699 1719 1808 2366 1960 public working_paper IIASA, Laxenburg, Austria 56 FALSE info:eu-repo/semantics/book