Preventive Maintenance Operations Scheduling Based on Eigenvalue and Clustering Methods

Abstract

Maintenance is the vital parts of any manufacturing operations, especially in the current global intense competitiveness pressure were companies are looking for any source of advantages to compete. This research paper introduces a new approach to forming maintainable machines into virtual cells to conduct the required maintenance tasks. After developing machine - failure incidence matrix, the proposed approach works in two stages. In the first stage, an eigenvector is used to develop a similarity matrix that identifies the relative weight relation between failures and machines. In the second stage, agglomerative hierarchal methods are used on the similarity matrix: a well-known clustering algorithm called complete linkage clustering is applied to the development of machine cells and the assignment of failures to the most suitable machine cells. The proposed mathematical approach allows the designer the flexibility to select the number of cells and find the level of similarity between machine cells or failure types.