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Given a binary product-machines n-by-m matrix, the algorithm proceeds [2] by the following steps: . Compute the similarity coefficient = / (+) for all with being the number of products that need to be processed on both machine i and machine j, u comprises the number of components which visit machine j but not k and vice versa.
Rank order clustering: an algorithm which groups machines and product families together, used for designing manufacturing cells; single-point scheduling, the opposite of the traditional push approach; multi-process handling: when one operator is responsible for operating several machines or processes
Model-based clustering [1] based on a statistical model for the data, usually a mixture model. This has several advantages, including a principled statistical basis for clustering, and ways to choose the number of clusters, to choose the best clustering model, to assess the uncertainty of the clustering, and to identify outliers that do not ...
Centroid-based clustering problems such as k-means and k-medoids are special cases of the uncapacitated, metric facility location problem, a canonical problem in the operations research and computational geometry communities. In a basic facility location problem (of which there are numerous variants that model more elaborate settings), the task ...
Group technology is based on a general principle that many problems are similar and by grouping similar problems, a single solution can be found to a set of problems, thus saving time and effort. The group of similar parts is known as part family and the group of machineries used to process an individual part family is known as machine cell. It ...
Direct clustering algorithm (DCA) is a methodology for identification of cellular manufacturing structure within an existing manufacturing shop. The DCA was introduced in 1982 by H.M. Chan and D.A. Milner [1] The algorithm restructures the existing machine / component (product) matrix of a shop by switching the rows and columns in such a way that a resulting matrix shows component families ...
In this case, the learning-to-rank problem is approximated by a classification problem — learning a binary classifier (,) that can tell which document is better in a given pair of documents. The classifier shall take two documents as its input and the goal is to minimize a loss function L ( h ; x u , x v , y u , v ) {\displaystyle L(h;x_{u},x ...
Therefore, most research in clustering analysis has been focused on the automation of the process. Automated selection of k in a K-means clustering algorithm, one of the most used centroid-based clustering algorithms, is still a major problem in machine learning. The most accepted solution to this problem is the elbow method.