Investigations on the input processing schema for the machine-part cell formation using CARI Heuristic

Abstract

Abstract This paper aims to demystify the possibility of using Hamann, Yule (value ranges from -1 to +1) and Jaccard (value ranges from 0 to +1) similarity measures for the machine-part cell formation (MPCF) using the CARI heuristic [17] that uses correlation coefficient (value ranges from -1 to +1) as the similarity measure. It has been found that grouping efficacy (GE) achieved by CARI heuristic while using Hamann and Yule as similarity measure is less for 71.42% and 51% of the dataset respectively compared to the GE achieved while using correlation coefficient as similarity measure. Jaccard similarity measure has been found unsuitable for MPCF using CARI heuristic due to the formation of single large cluster for all the benchmark dataset. CARI heuristic in its current version produces machine-part cells with high GE, only while using correlation coefficient as similarity measure whereas while using the other similarity measures, it produces machine-part cells with low GE or single large cluster is created. To overcome this issue, a normalizing procedure is appended to the current version of CARI heuristic, thus making it capable of producing machine-part cells with any of the similarity measures. Computational performance of the proposed version of CARI heuristic was tested using 35 dataset. The proposed method resulted in increasing GE for 42.84% and 74.27% of the dataset for Hamann and Jaccard similarity measures respectively