Possibilistic linear programming: a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem

By M. Inuiguchi, Jaroslav Ramík. Fuzzy Sets Syst., 2000

771 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e3530da13c0296894e0d39dac009b1e8a1653e8d

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In Control and Systems Engineering

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Robust possibilistic programming for socially responsible supply chain network design: A new approach (2012, methodology): “Robust programming (RP) or optimization theory provides risk-averse methods to cope with uncertainty in optimization problems.”
A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry (2016, methodology): “It is worth noting that since using Nec is moremeaningful to satisfy chance constraints (Inuiguchi and Ramık, 2000), in this paper we have done the same and used the trapezoidal fuzzy distribution for modeling because it can be defined by four sensitive points ði:e: eq 1⁄4 eqð1Þ;eqð2Þ;eqð3Þ;eqð4ÞÞ (”
Supply chain risk management and artificial intelligence: state of the art and future research directions (2018, neutral): “This is in line with the observation in literature that fuzzy models are, in general, easier to solve than stochastic ones and, hence, may accommodate more parameters in comparison(Inuiguchi and Ramík 2000).”
A fuzzy set approach for R&D portfolio selection using a real options valuation model (2007, methodology): “In this paper, the approach of Inuiguchi and Ramik [29] has been extended with qualitative possibility theory to handle the fuzzy constraint containing both uncertain and flexible parameters.”
Expected value of fuzzy variable and fuzzy expected value models (2002, background): “On the other hand, many researchers such as Zimmermann [25], Luhandjula [11], [12], Yazenin [22], [23], Sakawa [17], Inuiguchi and Ramík [4], Tanaka et al. [20] applied the theory successfully to optimization problems.”
A possibilistic programming approach for closed-loop supply chain network design under uncertainty (2010, neutral): “, [29,34–37]).”
A Superiority-Inferiority-Based Inexact Fuzzy Stochastic Programming Approach for Solid Waste Management Under Uncertainty (2010, methodology): “Most of these methods focused on fuzzy mathematical programming, stochastic mathematical programming, and interval mathematical programming [4, 6, 7, 10, 12, 15, 20–22, 24–26, 28, 30–33, 38, 41].”
An interactive possibilistic programming approach for multiple objective supply chain master planning (2008, background): “Generally, fuzzy mathematical programming is classified into two following major classes [13]: • Fuzzy mathematical programming with vagueness when there is flexibility in the given target values of objective functions and the elasticity of constraints.”