AN IMPROVED NEURAL-NETWORK LEADER ALGORITHM FOR PART-MACHINE GROUPING IN-GROUP TECHNOLOGY

In this paper, a robust neural network-based leader algorithm is proposed for the part-machine grouping problem in group technology. This clustering method involves a modification to the normal use of Carpenter and Grossberg's ART1 neural network. The robustness of the modified algorithm to random ordering of the input data is tested using three data sets. The data sets include an industry-size problem consisting of 10000 parts and 100 machine types. The experiments revealed that the method results in the identification of clusters and block diagonal structures rapidly and to a good degree of perfection, even for large, industry-size data sets. The solutions obtained were also found to be robust to the order of presentation of the input data. The proposed method offers a promising solution to a cellular manufacturing problem that is yet to be solved satisfactorily.