Uncertain association rule mining algorithm for the cell formation problem in cellular manufacturing systems

Although data mining has enjoyed popularity in recent years with advances in both academia and industry, the application of data mining to cellular manufacturing, one of the most powerful management innovations in job-shop or batch-type production, is still under-utilized. Based on association rule mining, Chen initially developed a cell formation approach. One problem of such a cell formation algorithm is that various real-life production factors were ignored. In this paper we propose a new cell formation algorithm by way of uncertain association rule mining. The proposed algorithm incorporates several key production factors, such as operation sequence, production volume, batch size, alternative process routings, cell size, the number of cells, and the path coefficient of material flow. The efficacy and efficiency of the proposed algorithm were tested using several numerical problems.