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This research project aims at developing an enterprise-wide modelling and optimisation framework to support CBP. There are a number of challenges involved in this project. The first challenge is to identify the key CBP processes and functions common across large scale organizations. The second challenge is to identify the information sources associated with these processes and functions and develop their abstract models at appropriate levels of fidelity including strategic, tactical and operational levels. The third challenge is to capture and incorporate the uncertainty inherent in these models at multiple levels. The fourth challenge is to develop multi-scale dynamic optimization methods for these models, both at different temporal as well as fidelity scales. And the fifth challenge is to coordinate the output of different optimization functions in an integrated computational framework that can support decision making processes under CBP. This project will apply computational intelligence approaches, including multi and many-objective evolutionary optimization algorithms, genetic-based machine learning techniques, and evolutionary game theory concepts to address these challenges and realize this computational framework.