Fuzzy network technique for technological forecasting

Abstract The stochastic network technique is known to be a powerful tool carrying out a technological forecast of complex systems. A network dealt with is characterized by a tetrad of essential elements: logical nodes with some inputs and outputs, probabilistics activity branches, feedback loops, and multiple sources and sinks. A set of network parameters is defined for each element and their values are estimated for practical analysis of the network. In the case where the system to be treated is very large and/or complex, it cannot always be represented by a definite network and therefore forecasted values of parameters are inevitably indefinite themselves. A conventional probabilistic approach is sometimes inadequate in such a case. In the light of these facts, the paper proposes a fuzzy network technique, in which among activity branches emanating from a node, a branch to be undertaken once the node is realized belongs to a fuzzy set; and the time required to complete an activity branch belongs to a fuzzy set. Operations of maximum and minimum for sum and product of fuzzy sets take the place of manipulations of addition and multiplication for probabilities, respectively. Although the operations are somewhat formal, the obtained results seem interesting. A numerical example is attached to show a comparison of the proposed technique with the conventional one.