Clustering and group selection of interim product in shipbuilding

Cellular manufacturing of the interim products based on group technology (GT) is a useful way to increase the productivity and improving the process flows of shipbuilding. In this paper, we analyze the similarity relationship on interim product to form product families and establish requirement mode of similarity measurement based on shipbuilding GT. The classifier of ART2 artificial neural network is proposed on the basis of the adaptive resonance theory. It can classify and identify automatically the input data through analyzing the characteristics of interim product in shipbuilding. With the modified algorithm, the interim product can easily and efficiently be formed product families and controlled to group related interim product into families in reason by similar coefficient. Meanwhile, through analyzing the production constraint to form assembly cell, the set of evaluating indexes is founded whose weights are decided by the entropy weight method and the comprehensive assessment values of product families design schemes can be computed to judge the final optimization scheme through the grey correlation analysis. The formation of sub-assembly interim product family in a bulk carrier is taken as an example to verify the proposed method, and the results show that it is an effective method for solving the group manufacturing problem in shipbuilding activities.