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Optimization in an Intuitionistic Fuzzy Environment

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Optimization in an Intuitionistic Fuzzy Environment. / Angelov, Plamen.
In: Fuzzy Sets and Systems, Vol. 86, No. 3, 16.03.1997, p. 299-306.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Angelov, P 1997, 'Optimization in an Intuitionistic Fuzzy Environment', Fuzzy Sets and Systems, vol. 86, no. 3, pp. 299-306. https://doi.org/10.1016/S0165-0114(96)00009-7

APA

Angelov, P. (1997). Optimization in an Intuitionistic Fuzzy Environment. Fuzzy Sets and Systems, 86(3), 299-306. https://doi.org/10.1016/S0165-0114(96)00009-7

Vancouver

Angelov P. Optimization in an Intuitionistic Fuzzy Environment. Fuzzy Sets and Systems. 1997 Mar 16;86(3):299-306. doi: 10.1016/S0165-0114(96)00009-7

Author

Angelov, Plamen. / Optimization in an Intuitionistic Fuzzy Environment. In: Fuzzy Sets and Systems. 1997 ; Vol. 86, No. 3. pp. 299-306.

Bibtex

@article{3a83c649c8be42aca8787fbd1f226f8b,
title = "Optimization in an Intuitionistic Fuzzy Environment",
abstract = "A new concept of the optimization problem under uncertainty is proposed and treated in the paper. It is an extension of fuzzy optimization in which the degrees of rejection of objective(s) and of constraints are considered together with the degrees of satisfaction. This approach is an application of the intuitionistic fuzzy (IF) set concept to optimization problems. An approach to solving such problems is proposed and illustrated with a simple numerical example. It converts the introduced intuitionistic fuzzy optimization (IFO) problem into the crisp (non-fuzzy) one. The advantage of the IFO problems is twofold: they give the richest apparatus for formulation of optimization problems and, on the other hand, the solution of IFO problems can satisfy the objective(s) with bigger degree than the analogous fuzzy optimization problem and the crisp one. (c) Elsevier",
keywords = "DCS-publications-id, art-548, DCS-publications-personnel-id, 82",
author = "Plamen Angelov",
note = "The final, definitive version of this article has been published in the Journal, Fuzzy Sets and Systems 86 (3), 1997, {\textcopyright} ELSEVIER.",
year = "1997",
month = mar,
day = "16",
doi = "10.1016/S0165-0114(96)00009-7",
language = "English",
volume = "86",
pages = "299--306",
journal = "Fuzzy Sets and Systems",
issn = "0165-0114",
publisher = "Elsevier",
number = "3",

}

RIS

TY - JOUR

T1 - Optimization in an Intuitionistic Fuzzy Environment

AU - Angelov, Plamen

N1 - The final, definitive version of this article has been published in the Journal, Fuzzy Sets and Systems 86 (3), 1997, © ELSEVIER.

PY - 1997/3/16

Y1 - 1997/3/16

N2 - A new concept of the optimization problem under uncertainty is proposed and treated in the paper. It is an extension of fuzzy optimization in which the degrees of rejection of objective(s) and of constraints are considered together with the degrees of satisfaction. This approach is an application of the intuitionistic fuzzy (IF) set concept to optimization problems. An approach to solving such problems is proposed and illustrated with a simple numerical example. It converts the introduced intuitionistic fuzzy optimization (IFO) problem into the crisp (non-fuzzy) one. The advantage of the IFO problems is twofold: they give the richest apparatus for formulation of optimization problems and, on the other hand, the solution of IFO problems can satisfy the objective(s) with bigger degree than the analogous fuzzy optimization problem and the crisp one. (c) Elsevier

AB - A new concept of the optimization problem under uncertainty is proposed and treated in the paper. It is an extension of fuzzy optimization in which the degrees of rejection of objective(s) and of constraints are considered together with the degrees of satisfaction. This approach is an application of the intuitionistic fuzzy (IF) set concept to optimization problems. An approach to solving such problems is proposed and illustrated with a simple numerical example. It converts the introduced intuitionistic fuzzy optimization (IFO) problem into the crisp (non-fuzzy) one. The advantage of the IFO problems is twofold: they give the richest apparatus for formulation of optimization problems and, on the other hand, the solution of IFO problems can satisfy the objective(s) with bigger degree than the analogous fuzzy optimization problem and the crisp one. (c) Elsevier

KW - DCS-publications-id

KW - art-548

KW - DCS-publications-personnel-id

KW - 82

U2 - 10.1016/S0165-0114(96)00009-7

DO - 10.1016/S0165-0114(96)00009-7

M3 - Journal article

VL - 86

SP - 299

EP - 306

JO - Fuzzy Sets and Systems

JF - Fuzzy Sets and Systems

SN - 0165-0114

IS - 3

ER -