Linear programming problems and ranking of fuzzy numbers
Fuzzy Sets and Systems
Fuzziness and randomness in an optimization framework
Fuzzy Sets and Systems
On fuzzy stochastic optimization
Fuzzy Sets and Systems - Special issue on fuzzy optimization
Ranking and defuzzification methods based on area compensation
Fuzzy Sets and Systems
An inexact approach for linear programming problems with fuzzy objective and resources
Fuzzy Sets and Systems
Linear programming with fuzzy variables
Fuzzy Sets and Systems
Fuzzy Sets and Systems - Fuzzy mathematical programming
Ranking fuzzy numbers based on decomposition principle and signed distance
Fuzzy Sets and Systems - Special issue on fuzzy numbers and uncertainty
Fuzzy linear programming using a penalty method
Fuzzy Sets and Systems
Information Sciences—Informatics and Computer Science: An International Journal
Random fuzzy dependent-chance programming and its hybrid intelligent algorithm
Information Sciences—Informatics and Computer Science: An International Journal
A class of fuzzy random optimization: expected value models
Information Sciences: an International Journal
Fuzzy random programming with equilibrium chance constraints
Information Sciences—Informatics and Computer Science: An International Journal
Fuzzy random chance-constrained programming
IEEE Transactions on Fuzzy Systems
Fuzzy random dependent-chance programming
IEEE Transactions on Fuzzy Systems
Expected value of fuzzy variable and fuzzy expected value models
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
Supremum metric on the space of fuzzy sets and common fixed point theorems for fuzzy mappings
Information Sciences: an International Journal
Information Sciences: an International Journal
Interactive multiobjective fuzzy random programming through the level set-based probability model
Information Sciences: an International Journal
Information Sciences: an International Journal
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In this paper, the author presents a model to measure the superiority and inferiority of fuzzy numbers/fuzzy stochastic variables. Then, the new measures are used to convert the fuzzy (stochastic) linear program into the corresponding deterministic linear program. Numerical examples are provided to illustrate the effectiveness of the proposed method.