An association rule mining approach for shop floor material handling based on real-time manufacturing big data

Abstract

In recent years, radio frequency identification and smart sensors are widely used by manufacturers to assist their daily production and management. Manufacturing resources such as machines, operators and materials are made smart by configuring with these facilities. As a result, a smart manufacturing environment is created. Under such environment, a large amount of manufacturing big data can be analyzed to support shop floor decisions. In order to get a better decision-making based on the collected manufacturing big data, in this paper, an association rule mining approach for shop floor material handling based on real-time manufacturing big data is proposed to discovery the optimal trajectory of material handling. An application scenario and a simulation experiment are designed and conducted to verify the availability of the presented approach.