Hybrid Fuzzy-ART based K-Means Clustering Methodology to Cellular Manufacturing Using Operational Time

TL;DR

This paper introduces a hybrid Fuzzy-ART and K-Means clustering method for cellular manufacturing that improves efficiency and reduces computational effort, tested on literature problems and outperforming existing models.

Contribution

A novel hybrid clustering approach combining Fuzzy-ART and K-Means for efficient part-machine grouping in manufacturing systems.

Findings

Proposed method outperforms existing clustering models in efficiency.

The hybrid approach reduces computational time significantly.

Results demonstrate improved grouping efficiency in manufacturing applications.

Abstract

This paper presents a new hybrid Fuzzy-ART based K-Means Clustering technique to solve the part machine grouping problem in cellular manufacturing systems considering operational time. The performance of the proposed technique is tested with problems from open literature and the results are compared to the existing clustering models such as simple K-means algorithm and modified ART1 algorithm using an efficient modified performance measure known as modified grouping efficiency (MGE) as found in the literature. The results support the better performance of the proposed algorithm. The Novelty of this study lies in the simple and efficient methodology to produce quick solutions for shop floor managers with least computational efforts and time.