A Comparative Study of Integrating Feasibility Assessment and Cell Formation Problem Using Modified Similarity Coefficient Method

Abstract

The focus of this research is on cellular manufacturing systems based on group technology concepts. The primary goal of CM is to organize machines and parts into cells and families. The cell creation problem is the focus of the majority of suggested models, algorithms, and approaches. Only a few people have thought about the feasibility study and the cell formation issue. A new similarity coefficient strategy for handling cell formation challenges in cellular manufacturing is proposed along with operation sequence in this research. Three strategies are used to integrate a feasibility assessment with the cell formation challenge in this study. This study takes into account performance indicators such as the percentage of Exceptional Elements (PE), Machine Utilization (MU), Bond Efficiency (BE), Grouping Technology Efficiency (GTE), and Efficiency of Intra-cell Movements (EIM). The proposed similarity coefficient approach is used to identify the optimal number of cells before generating a cell formation. The proposed method has been tested on some of the most well-known cases in the literature, such as real-occasion industrialized data. The computational findings show that in the majority of the situations, the proposed similarity coefficient approach produces the best outcomes. The suggested technique is evaluated to two familiar clustering approaches, Rusell & Rao and the ROC algorithm, which were chosen from the literature. Five performance measures are used for comparison and evaluation. Finally, the data are analyzed to see if the proposed similarity coefficient approach can be tested and validated. The results show that using the novel similarity coefficient approach produces results that are on par with or better than those obtained using other clustering methods.