a hybrid artificial neural network for selecting product investment solution

in today’s business competitive world, decision makers of companies try to employ standard, efficient, theoretical and operational proven methods as a competitive advantage for making their critical strategic business decisions in order to survive in their industry. in this paper, a hybrid model based on fuzzy analytic hierarchy process (fahp) and artificial neural network (ann) is presented. this model can be used as a decision support system (dss) to capture and represent the decision makers’ preferences, without their direct or interference, in order to represent them the ability to select the best product among candidates. the priority of products is determined by the factors elicited from interviews and surveys. firstly, the weight of each factor is determined by the means of fahp technic. then, the priority of products will be determined by weighing each of them with respect to the factors. but, the output of fahp is just a possible answer. the topology of the neural network model is developed to train the model and give the decision makers the best possible answer. in fact, neural network is used to learn the relation among criteria and alternatives and rank the alternatives. a method based on ahp is developed and used as the mcdm method, and multi level perceptron is used as our selected kind of artificial neural network. a case study is discussed and developed through the paper. the comparison of the real investment data of the studying case and the results of the model proves the effectiveness of the proposed model.