No abstract

No abstract

The important step in the design of a cellular manufacturing (CM) system is to identify the part families and machine groups and consequently to form manufacturing cells. The scope of this article is to formulate a multivariate approach based on a correlation analysis for solving cell formation problem. The proposed approach is carried out in three phases. In the first phase, the correlation matrix is used as similarity coefficient matrix. In the second phase, Principal Component Analysis (PCA) is applied to find the eigenvalues and eigenvectors on the correlation similarity matrix. A scatter plot analysis as a cluster analysis is applied to make simultaneously machine groups and part families while maximizing correlation between elements. In the third stage, an algorithm is improved to assign exceptional machines and exceptional parts using respectively angle measure and Euclidian distance.The proposed approach is also applied to the general Group Technology (GT) problem in which exceptional machines and part are considered. Furthermore, the proposed approach has the flexibility to consider the number of cells as a dependent or independent variable.Two numerical examples for the design of cell structures are provided in order to illustrate the three phases of proposed approach. The results of a comparative study based on multiple performance criteria show that the present approach is very effective, efficient and practical.

No abstract

Abstract:The maintenance department in a hospital is responsible for ensuring the safety of medical equipment and their availability while keeping the operation costs minimal. The selection of the best maintenance strategy is a key decision to reduce the equipment downtime, increase the availability, and bring down the maintenance costs. In this paper, we use an integrated approach that includes several tools from the literature, namely, the Analytical Hierarchy Process (AHP), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the mathematical optimization (especially mixed integer problems MILP) to provide the decision maker of the maintenance department with an entire solution to the problem at hand. These three tools are introduced to 1) determine the criticality of medical equipment based on a multi-criteria analysis, 2) rank the different maintenance strategies based on their (benefits) importance to the hospital and 3) select the optimal maintenance strategy for each device while keeping the total maintenance costs within a predetermined budget. We applied our approach to a case study at the Hospital of "Habib Bourguiba" in Tunisia, and the numerical results show the efficiency of our approach to improve the availability and the reliability of high risk medical devices.

No abstract

No abstract

No abstract