To solve a mathematical model for American put option with uncertainty, we utilize two essentials, i.e., a λ−weighting function and a mean value of fuzzy random variables simultaneously. Estimation of randomness and fuzziness as uncertainty should be important when we deal with a reasonable and natural model extended from the original optimization/decision making. Three kinds of mean values by ...

jahanshahloo has suggested a method for the solving linear programming problems with zero-one variables‎. ‎in this paper we formulate fully fuzzy linear programming problems with zero-one variables and a method for solving these problems is presented using the ranking function and also the branch and bound method along with an example is presented.

Fuzziness is discussed in the context of multivalued lo&, and a corresponding view of fuzzy sets is given. Fuzzy random variables are introduced as random variables whose values are not real but fuzzy numbers, and subsequently redefined as a particular kind of fuzzy set. Expectations of fuzzy random variables, characteristic f~cti~ of fuzzy events, probabilities connected to fuzzy random variab...

-A neural network approach for solving fuzzy linear programming problems is proposed in which fuzzy concepts are not used. This approach is totally based on the level-sum method introduced Pandian [11]. The nearest optimal solution of the fuzzy linear programming original problem can be obtained by the proposed approach. The large scale fuzzy linear programming problems can be solved efficientl...

Solving systems of fuzzy linear inequalities could lead to the solutions of fuzzy linear programs. It is shown that a system of fuzzy linear inequalities can be converted to a regular min-max problem. An entropic regularization method is introduced for solving such a problem. Some computational results are included. 1. Introduction. Over the past years, the field of fuzzy linear programming has...

Many optimization problems contain fuzzy information. Possibility theory [1] has been well developed and applied to this kind of optimization problems [2—5]. Fuzzy programming is an important tool to handle the optimization problems, which usually includes three types of models: fuzzy expected value model [6] , fuzzy chance-constrained programming model [7,8] , and fuzzy dependent-chance progra...

the purpose of this paper is to develop a methodology for solving a new type of matrix games in which payoffs are expressed with triangular intuitionistic fuzzy numbers (tifns). in this methodology, the concept of solutions for matrix games with payoffs of tifns is introduced. a pair of auxiliary intuitionistic fuzzy programming models for players are established to determine optimal strategies...

This paper considers multiobjective integer programming problems involving random variables in constraints. Using the concept of simple recourse, the formulated multiobjective stochastic simple recourse problems are transformed into deterministic ones. For solving transformed deterministic problems efficiently, we also introduce genetic algorithms with double strings for nonlinear integer progr...