Fuzzy Sets and Systems
Capital budgeting techniques using discounted fuzzy versus probabilistic cash flows
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Intelligent information systems and applications
Project Selection by Constrained Fuzzy AHP
Fuzzy Optimization and Decision Making
A fuzzy decision support system for strategic portfolio management
Decision Support Systems
A fuzzy approach to R&D project portfolio selection
International Journal of Approximate Reasoning
Chance-constrained programming models for capital budgeting with NPV as fuzzy parameters
Journal of Computational and Applied Mathematics
Mean-semivariance models for fuzzy portfolio selection
Journal of Computational and Applied Mathematics
Mean-variance model for fuzzy capital budgeting
Computers and Industrial Engineering
Investment project valuation based on a fuzzy binomial approach
Information Sciences: an International Journal
Expected value of fuzzy variable and fuzzy expected value models
IEEE Transactions on Fuzzy Systems
Multiobjective credibilistic portfolio selection model with fuzzy chance-constraints
Information Sciences: an International Journal
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This paper deals with the problems of both project valuation and portfolio selection under the assumption that the investment capitals and the net cash flows of the projects are fuzzy variables. Using the credibilistic expected value and the credibilistic lower semivariance of fuzzy variables, this paper proposes both the credibilistic return index and the credibilistic risk index, which are measures of investment return and investment risk with annuity form for evaluating single project. Moreover, a composite risk-return index for selecting the optimal investment strategy is also presented. Then, we set up a general project portfolio optimization model with fuzzy returns and two specific models: triangle and interval fuzzy returns. Furthermore, we provide two algorithms: the improved heuristic rules based on genetic algorithm and the traversal algorithm. Finally, two numerical examples are presented to illustrate the efficiency and the effectiveness of these proposed optimization methods.