Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
On the optimization of fuzzy decision trees
Fuzzy Sets and Systems
Theory and Practice of Uncertain Programming
Theory and Practice of Uncertain Programming
Expected value operator of random fuzzy variable and random fuzzy expected value models
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
Applications of Stochastic Programming (Mps-Siam Series on Optimization) (Mps-Saimseries on Optimization)
The development of fuzzy decision trees in the framework of Axiomatic Fuzzy Set logic
Applied Soft Computing
Information Sciences: an International Journal
Uncertainty Theory
Expected value of fuzzy variable and fuzzy expected value models
IEEE Transactions on Fuzzy Systems
A random fuzzy minimum spanning tree problem through a possibility-based value at risk model
Expert Systems with Applications: An International Journal
Random fuzzy multi-objective linear programming: Optimization of possibilistic value at risk (pVaR)
Expert Systems with Applications: An International Journal
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In this study, an investment problem associated with purchasing machine is solved by using decision trees method. The main difference of this paper from the others in the decision tree literature is using random fuzzy variables, which includes randomness and fuzziness simultaneously, for modeling demand quantities and demand state probabilities. Demand quantities and demand state probabilities are modeled as a continuous and discrete random fuzzy variables, respectively. The random fuzzy variables are transformed to fuzzy variables and crisp numbers by using the properties of the random fuzzy variable, fuzzy variable and normal distribution. After the transformation, the chance and decision nodes removals processes are performed to solve the problem.