A mean-absolute deviation-skewness portfolio optimization model
Annals of Operations Research
Robust Solutions to Least-Squares Problems with Uncertain Data
SIAM Journal on Matrix Analysis and Applications
Fuzzy Sets and Systems - Fuzzy mathematical programming
Portfolio selection based on fuzzy probabilities and possibility distributions
Fuzzy Sets and Systems
Portfolio selection under independent possibilistic information
Fuzzy Sets and Systems - Special issue on soft decision analysis
Lectures on modern convex optimization: analysis, algorithms, and engineering applications
Lectures on modern convex optimization: analysis, algorithms, and engineering applications
A possibilistic approach to selecting portfolios with highest utility score
Fuzzy Sets and Systems - Special issue: Soft decision analysis
Robust portfolio selection problems
Mathematics of Operations Research
A class of possibilistic portfolio selection model with interval coefficients and its application
Fuzzy Optimization and Decision Making
A review of credibilistic portfolio selection
Fuzzy Optimization and Decision Making
Portfolio selection problems with random fuzzy variable returns
Fuzzy Sets and Systems
A portfolio selection model using fuzzy returns
Fuzzy Optimization and Decision Making
Robust solutions of uncertain linear programs
Operations Research Letters
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This paper considers a robust portfolio selection problem with an uncertainty set of future returns and satisfaction levels in terms of the total return and robustness parameter. Since the proposed model is formulated as an ill-defined problem due to uncertainty and is bi-objective, that is, to maximize both the abovementioned satisfaction levels, it is difficult to solve the model directly without introducing some criterion of optimality for the bi-objective functions. Therefore, by introducing fuzzy goals and an interactive fuzzy satisficing method, the proposed model is transformed into a deterministic equivalent problem. Furthermore, to obtain the exact optimal portfolio analytically, a solution method is developed by introducing the auxiliary problem and performing equivalent transformations. In order to compare the proposed model with previous useful models, numerical examples are provided, and the results show that it is important to maximize the robustness parameter and total return using the interactive process for adjusting investor's satisfaction levels.