In reliability optimization problems diverse situation occurs due to which it is not always possible to get relevant precision in system reliability. The imprecision in data can often be represented by triangular fuzzy numbers. In this manuscript, we have considered different fuzzy environment for reliability optimization problem of redundancy. We formulate a redundancy allocation problem for a...

This paper deals with the competitive location problems using fuzzy sets. The basic notions on fuzzy optimization and linear programming using fuzzy sets are briefly reviewed. The standard leader-follower location problem and its linear mathematical programming formulation are described. The works appeared in the literature concerning the use of fuzzy sets are analyzed. Keywords— Fuzzy Optimiza...

We examine a linear programming problem formulation in which the constraint coefficients are not precisely given in the work. We investigate the possibility of applying GAs to solve this kind of fuzzy linear programming problem without defining membership functions for fuzzy numbers, using the extension principle, interval arithmetic, and α-cut operations for fuzzy computations, and using a pen...

Fuzzy Linear Programming models are quite frequent in practice. The dynamic nature of the real problems often requires reoptimize from the optimal solutions found, what may mean a significant consumption of time and funds. In this paper, in order to efficiently solve this problem, first the optimality conditions and the duality results for fully fuzzy linear programming problems (all parameters...

This paper proposes a modified non-dominated sorting genetic algorithm (NSGA-II) for a bi-objective location-allocation model. The purpose is to define the best places and capacity of the distribution centers as well as to allocate consumers, in such a way that uncertain consumers demands are satisfied. The objectives of the mixed-integer non-linear programming (MINLP) model are to (1) minimize...

Abstract In this paper, we use interval-valued fuzzy numbers to fuzzify the crisp linear programming to three cases. The first case, we use intervalvalued fuzzy numbers to fuzzify the coefficients in the objective function. We get a linear programming in the fuzzy sense. The second case, we use interval-valued fuzzy numbers to fuzzify the coefficients akj in the constraints about xj, j = 1, 2, ...

This paper presents a new method for regression model prediction in an uncertain environment. In practical engineering problems, in order to develop regression or ANN model for making predictions, the average of set of repeated observed values are introduced to the model as an input variable. Therefore, the estimated response of the process is also the average of a set of output values where th...