Survivability of complex system - Support vector machine based approach
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Logistic systems which are inherently distributed, in general can be classified as complex systems. Survivability of these systems under varying environment conditions is of paramount importance. Different environmental conditions in which the logistic system resides are translated into several stresses. These in turn will manifest as internal stresses. Logistic systems can be modeled as a collection of software agents. Each agent's behavior is a result of the stresses imposed. Predicting the agents' collective behavior is of paramount importance to ensure survivability. Analytical modeling of such systems becomes very difficult, albeit impossible. In this paper, we study a supply chain in which a real life scenario is used. We implement the supply chain in Cougaar (Cognitive Agent Architecture developed by DARPA) and develop a predictor, based on Support Vector Machine. We report our methodology and results with real-life experiments and stress scenarios.