A hybrid algorithm to solve a capacitated location allocation problem in fuzzy and stochastic environment

In this article, a capacitated location-allocation problem is considered in which customer’s demands and locations are uncertain. While the demands are fuzzy, the locations are stochastic and follow a normal distribution. The distance between the locations and the customers are Euclidean and squared Euclidean. Two closed-form expressions based on Euclidean and Euclidean square are used to evaluate the expected distance between the customers and the facilities. Moreover, three types of programming (fuzzy expected cost, fuzzy 􀈕-cost minimization, and credibility maximization) are developed to model the problem. In order to solve the models, a hybrid intelligent algorithm is applied where the simplex algorithm, fuzzy simulation and a modified genetic algorithm are integrated. Finally, to illustrate the efficiency of the proposed algorithm some numerical examples are presented