@article{ZADEH1965338,
title = {Fuzzy sets},
journal = {Information and Control},
volume = {8},
number = {3},
pages = {338-353},
year = {1965},
issn = {0019-9958},
doi = {https://doi.org/10.1016/S0019-9958(65)90241-X},
url = {https://www.sciencedirect.com/science/article/pii/S001999586590241X},
author = {L.A. Zadeh},
abstract = {A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one. The notions of inclusion, union, intersection, complement, relation, convexity, etc., are extended to such sets, and various properties of these notions in the context of fuzzy sets are established. In particular, a separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.}
}

@article{EKEL2006179,
title = {Fuzzy preference modeling and its application to multiobjective decision making},
journal = {Computers & Mathematics with Applications},
volume = {52},
number = {1},
pages = {179-196},
year = {2006},
note = {Global Optimization, Control and Games, V},
issn = {0898-1221},
doi = {https://doi.org/10.1016/j.camwa.2006.08.012},
url = {https://www.sciencedirect.com/science/article/pii/S0898122106001891},
author = {P.Ya. Ekel and M.R. Silva and F. {Schuffner Neto} and R.M. Palhares},
abstract = {This paper reflects results of research into the construction and analysis of 〈X, R〉 models within the framework of a general approach to solving optimization problems with fuzzy coefficients. This approach involves a modification of traditional mathematical programming methods and is associated with formulating and solving one and the same problem within the framework of mutually interrelated models. The use of the approach allows one to maximally cut off dominated alternatives. The subsequent contraction of the decision uncertainty region is based on reducing the problem to models of multiobjective choosing alternatives in a fuzzy environment with the use of fúzzy preference relation techniques for analyzing these models. Three techniques for fuzzy preference modeling are discussed in the paper. The first technique is based on the construction of membership functions of subsets of nondominated alternatives with simultaneous considering of all criteria (fuzzy preference relations). The second technique is of a lexicographic character and consists of step-by-step introducing of fuzzy preference relations. The third technique is based on aggregating membership functions of subsets of nondominated alternatives corresponding to each preference relation. These techniques have served for developing a corresponding system for multiobjective decision making (MDMS). C++ windows of the MDMS are presented for input, output, and some intermediate procedures. The results of the paper are of a universal character and are already being used to solve power engineering, naval engineering, and management problems.}
}
