A neuro-based expert system for facility layout construction

Motivated by the success of implementing expert systems (ESs) based on artificial neural networks (ANNs) to improved classical rule-based expert systems (RBESs), this paper reports on the development of a neuro-based expert system (NBES) for facility layout construction in a manufacturing system. In an artificial intelligence (AI) technique such as the NBES, the semantic structure of If-Then rules is preserved, while incorporating the learning capability of ANNs into the inference mechanism. Unlike implementing a popular back propagation network (BPN) as an ES, the proposed BAMFLO (Bidirectional Associative Memories for Facility LayOut) system is an intelligent layout consultant system consisting of pipeline BAM neural networks with simple, fast incremental learning and multiple bidirectional generalization characteristics. This incrementability makes BAMFLO effective at acquiring, adding or adapting learned layout knowledge; thus it is possible to memorize newly extended If-Then layout rules without retraining old ones. The multi-bidirectionality gives BAMFLO the ability to quickly and reliably generalize a layout solution, and to further infer unknown facts from known facts through a complex knowledge base (memorization) without losing information. The solution process of BAMFLO contains three essential steps: training example generation, incremental learning and solution generalization. The examples (layout knowledge) can be generated from practical experience and/or classical layout software solutions for incrementally training BAMFLO; the process then derives multiply bidirectionally generalized construction layout solutions. The experimental results show that the BAMFLO scheme outperforms five classical layout methods used to generate training examples.