A new bottleneck detecting approach to productivity improvement of knowledgeable manufacturing system

Manufacturing systems are usually restricted by one or more bottlenecks. Identification of bottleneck is a key factor to improving the throughput of a production system. However, locating the bottleneck is no easy task. This paper proposes a novel method of bottleneck detecting for knowledgeable manufacturing system (KMS). Presented a net-like model of the knowledgeable manufacturing system for bottleneck detection, which adapts well to the bottleneck analysis in flexible product lines. Based on the model, the concept of entire production net is defined and an approach to identifying bottlenecks in entire production nets is developed and proven effective theoretically. A self-learning method is introduced for storing the knowledge of bottlenecks and their conditions in the knowledge base to detect which cells are required to upgrade their capacity. Validity of the approach is verified by the numerical experiment.