Academic Resource Planning via Simulation and Inductive Classification

The ability to automate the evaluation of resource requirements with a given set of strategies is extremely important in the deployment of a cost effective academic plan. Very limited optimization/simulation approaches have been reported and the optimization approaches are very restrictive. Even though the simulation models can analyze practical scenarios, simulation analysis must be done every time a decision is required. We propose a hybrid simulation-artificial intelligence approach that overcomes the practical limitations of the simulation approach. We describe this approach in the context of academic resource planning in the Engineering Department at CSUEB.