Fuzzy Multi-Criteria Optimization: Possibilistic and Fuzzy/Stochastic Approaches

An Intuitionistic Fuzzy Stochastic Decision‐Making Method Based on Case‐Based Reasoning and Prospect Theory

“…In [3], a fuzzy stochastic dynamic fractional programming was proposed to deal with stochastic fuzzy dual uncertainty. In [1], a multiple objective programming was discussed for interactive fuzzy stochastic programming issues, and some interactive algorithms were proposed to seek a satisfying solution of DMs. These methods pay more attention to programming or simulating methods but have not considered that DMs are bounded rational facing stochastic uncertainty.…”

“…The first way is that the DM can propose the best alternative according to his/her experiences. The second is taking the average of all of the alternatives in 1 by using IFWA operator (see (1)).…”

“…With the increasing complexity of socioeconomic environments, it is very hard for decision-makers (DMs) to express their preferences with crisp values. Fuzzy set (FS) theory was proposed and has been deeply studied from different disciplines [1][2][3][4][5][6][7].…”

Goal programming models with interval coefficients for the sustainable selection of marine renewable energy projects in the UK

“…Nonetheless, in some real-world problems uncertainty and perturbations in the initial data are inherent components of the problem due to lack of knowledge or the uncertain nature of the coefficients (Korotkov and Wu 2020). Examples of such uncertainties are rate of return in investment, demands for products, or time for a manual operations (Inuiguchi et al 2016). In stochastic programming, such uncertain parameters are treated as random variables.…”

Decision space robustness for multi-objective integer linear programming

“…For a survey about different concepts of robustness in multi-objective optimization see, for example, Ide and Schöbel (2016). The following is a brief and non-exhaustive summary of main approaches found in the literature: stochastic programming (Inuiguchi et al, 2016;Słowiński et al, 1990); fuzzy/possibilistic programming (Adeyefa and Luhandjula, 2011;Inuiguchi et al, 2016;Słowiński et al, 1990); interval programming (Oliveira and Henggeler-Antunes, 2007); parametric programming (Dellnitz and Witting, 2009;Witting et al, 2013); minimax like programming (Aissi et al, 2009;Ehrgott et al, 2014); set valued optimization (Ide and Köbis, 2014); and, Monte Carlo simulation (Mavrotas et al, 2015). The different approaches have in common to find solutions which are more or less robust with respect to changes in some parameters which occur in the constraints and/or objectives of an optimization problem.…”