Goal-oriented decision support using Big Bang-Big Crunch learning based Fuzzy Cognitive Map: An ERP management case study

In this study, a new learning method called Big Bang-Big Crunch (BB-BC) is proposed for Fuzzy Cognitive Map (FCM), which is an approach to knowledge representation and inference. FCMs are basically fuzzy signed directed graphs with feedbacks, and they model the world as a collection of concepts and causal relations between concepts. Till now, little research has been done on the goal-oriented analysis with FCM. Therefore a methodology based on the use of Fuzzy cognitive map and BBBC algorithm is proposed to find the initial state of the model from among a large number of possible states for goal-oriented decision support. This optimization method is preferred for learning purpose since it has a low computational time and a high convergence speed. An ERP management model is used as the illustrative example, its results for different 8 scenarios show that the method is capable of goal-oriented decision support. Since, the proposed method is not limited with the number of concept or causal relations between these concepts; it can easily be used for the goal-oriented decision analysis of complex systems.