
## 5. Computational Result

To demonstrate the performance of the proposed visual decipherable clustering of Self Organizing Map algorithm, we tested the SOM algorithm on 10 GT instances collected from the literature. The selected matrices range from dimension 57 to 2035 and comprise well-structured, as well as unstructured matrices. The matrix sizes and their sources are presented in Table 3. We compare the grouping efficacy obtained by our SOM algorithm with the best grouping efficacies obtained by the seven different approaches (Goncalves and Resende, 2004). These seven approaches provide the best results, found in the literature, for the ten problems used for comparison. The experimental test was run on a personal computer having a Windows XP with Intel® 
dual-core technology 3.0 GHz processor. The algorithm was coded in Matlab programming. The test results are presented in Table 3. In Appendix B (figure 4) we present the block-diagonal matrices, found after execution of the proposed SOM algorithm, for each of the 10 problems mentioned in Table 3.

As can be seen in Table 3, the SOM algorithm proposed in this paper obtained machine/part groupings, which have a grouping efficacy that is never smaller than any of the best reported results. More specifically, the proposed SOM algorithm obtains for 3 (30%) problems values of the grouping efficacy that are equal to the best ones found in the literature and improves the values of the grouping efficacy for 7 (70%) problems. In 2 
(20%) problems, the percentage improvement is higher than 10%. 

## 6. Concluding Remarks

A new and novel approach for obtaining machine cells and part families has been presented. The approach proposed a visual decipherable clustering of machine-part cell formation using self organizing map algorithm. Computational experience with the SOM algorithm, on a set of 10 GT problems from the literature, has shown that it performs remarkably well. The algorithm obtained solutions that are at least as good as the ones found the literature. For 70% of the problems, the algorithm improved the previous solutions, in some cases by as much as 11%.The work can be further extended in future incorporating production data like operation sequence, operation time, layout considerations and dynamic manufacturing etc. enhancing it to a more generalized manufacturing environment.