This effort aims to make contributions in two areas of research. One is the area of neural network design. We present a new approach for neural network design in solving optimization problems, which is different from the traditional approach. Several limitations of traditional neural networks are discussed and the new hybrid neural network model is introduced to overcome those limitations. Neural network as a search technique in optimization has to prove itself not only capable of solving an optimization problem, but also be able to compete with other techniques on performance measures. We focus our study on two performance measures: efficiency and effectiveness. By definition, an efficient search technique can solve problems fast where as an effective technique will provide high quality solutions. Our research is successful in both directions.
The second major contribution we seek to make is in the area of processor scheduling. We test our hybrid neural network by solving a processor scheduling problem, and we compare our neural network solutions with the solutions from the existing best heuristics from literature. The hybrid neural network outperforms the heuristics in most cases and is able to improve the solutions from starting with a heuristic solution. The solution time is fairly short, i.e., in the same order of the heuristics. The processor scheduling problem we solved here is the Flexible Flow Shop (FFS) scheduling. The problem is NP-complete, a category of hard combinatorial optimization problems in literature. Applications of this problem include, but not limited to: computing systems with multiple processors at each phase where all tasks must be done through a series of phases; manufacturing systems with multiple machines at each stage where all jobs need to go through a series of stages. Therefore, solving the FFS problem is not only significant in the theory of optimization, but also in real world applications.