Fuzzy Sets and Systems
A new approach to some possibilistic linear programming problems
Fuzzy Sets and Systems
Chance constrained programming with fuzzy parameters
Fuzzy Sets and Systems
Multi-item stochastic and fuzzy-stochastic inventory models under two restrictions
Computers and Operations Research
Uncertain probabilities II: the continuous case
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Uncertain probabilities III: the continuous case
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Buyer-seller fuzzy inventory model for a deteriorating item with discount
International Journal of Systems Science
Fuzzy Probabilities: New Approach and Applications (Studies in Fuzziness and Soft Computing)
Fuzzy Probabilities: New Approach and Applications (Studies in Fuzziness and Soft Computing)
A new solution method for fuzzy chance constrained programming problem
Fuzzy Optimization and Decision Making
A new methodology for crisp equivalent of fuzzy chance constrained programming problem
Fuzzy Optimization and Decision Making
Linear programming under randomness and fuzziness
Fuzzy Sets and Systems
Inventory based two-objective job shop scheduling model and its hybrid genetic algorithm
Applied Soft Computing
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Most of the real world decision making problems involve uncertainty, which arise due to incomplete information or linguistic information on data. Stochastic programming and fuzzy programming are two powerful techniques to solve such type of problems. Fuzzy stochastic programming is concerned with optimization problems in which some or all parameters are treated as fuzzy random variables in order to capture randomness and fuzziness under one roof. A method for solving multi-objective fuzzy probabilistic programming problem is proposed in this paper. The uncertain parameters are considered as fuzzy log-normal random variables. Since the existing methods are not enough to solve fuzzy probabilistic programming problem directly, therefore the mathematical programming model is transformed to an equivalent multi-objective crisp model. Finally, a fuzzy programming technique is used to solve the multi-objective crisp model. The resulting model is then solved by standard non-linear programming methods. In order to illustrate the methodology a numerical example is provided.