This paper we propose a multi-objective optimization model to deal with the capacity planning in semiconductor manufacturing system, which is a typical multi-objective problem. Unlike traditional optimization methods, our model combines case-based reasoning with adaptive resonance theory and multi-objective genetic algorithm, in which some constraints such as maximizing profits, utilization of capacities, minimizing unmet demands etc are considered. An illustration shows how these techniques work effectively is also presented.

In parallel with the quick expansion of embedded devices come the rapid growth of embedded software. The commonly used software developing methods can't meet the fast growing demands of variable software when concerning user experience, cross-platforms, upgrade, hardware resources, etc. This paper proposes a new embedded software developing architecture based on the neural network organization and component principles. With the advantages of the network's dynamic weight adjusting and flexible self-learning structure, the approach can not only enable the software better adapt to the users' attributes, but also make the software be easily to extend and upgrade.