Design of efficient hybrid neural networks for flexible flow shop scheduling

Although neural networks have been successfully used in performing pattern recognition, their application for solving optimization problems has been limited. In this paper we design a neural network to solve a well-known combinatorial problem, namely the flexible flow shop problem. A key feature of our neural network design is the integration of problem structure and heuristic information in the network structure and solution. We compare the performance of our neural network with well-known current heuristics with respect to solution quality. The results indicate that our approach outperforms the heuristics.