TY - JOUR
AU - Wong, Xian Xiang
AU - Ong, Wen Eng
AU - Abd Rahman, Siti Amirah
PY - 2022/12/31
Y2 - 2024/04/24
TI - Surface Mount Technology Line Optimisation using Modified k-means with Feature Weight Constraints
JF - MATEMATIKA
JA - MATEMATIKA
VL - 38
IS - 3
SE - Articles
DO - 10.11113/matematika.v38.n3.1445
UR - https://matematika.utm.my/index.php/matematika/article/view/1445
SP - 195-208
AB - <p>The common setup problem for chip mounters in a Surface Mount Technology (SMT) line is to group different device models, each of them consisting of different components, into minimum number of clusters where each cluster has a maximum component size. This type of setup problem is classified as a clustering problem with feature weight constraints which cannot be fulfilled by traditional data clustering. In this study, we introduce some vital modifications on standard k-means algorithm such that it can incorporate feature weight constraints adapted from cluster size constraints. We also propose a modification to the elbow method to determine the number of clusters of the clustering problem with feature weight constraints. The results show that the proposed algorithm (modified k-means with feature weight constraints) is able to fulfill the feature weight constraints and solve the common setup problem. The results verify the proposed algorithm has superior performance over standard k-means algorithm in clustering problem with feature weight constraints. For the common setup problem on a given data set, the analysis shows that 1000 runs of the proposed algorithm implemented using MATLAB is able to obtain at least one valid clustering result within a reasonable run time.</p>
ER -