| This thesis is devoted to solve fuzzy optimization problems using Monte Carlo methods. In the first four chapters, we are finding a fuzzy function that best explains the fuzzy data. We study different models; linear, polynomial, exponential and logarithmic. To find the best model that explains some data, we look for the one that minimizes some error measure. We use several popular error measures. Also, we discuss and compare our results with previous published results.;Then we study a fuzzy optimization problem in queuing theory where it is used to model a web site. The variables are a mix of crisp variables -to express the system capacity and the number of servers- and fuzzy variables- to express the arrival rate and service rate of any server-. The objective is to maximize the fuzzy profit function. This problem has no known algorithmic solution; and previous results have investigated only 16 cases.;The last problem is a two-person zero-sum game with fuzzy payoffs and fuzzy mixed strategy. We, first, define the fuzzy values ( VI, VII) and an optimal fuzzy mixed strategy for the players. Then, we use our Monte Carlo Method to investigate the conjecture V I = VII... |
