An Effective Machine-Part Grouping Algorithm to Construct Manufacturing Cells

TL;DR

This paper introduces a hybrid clustering algorithm for machine-part cell formation in manufacturing, effectively reducing movement and improving machine utilization compared to existing methods.

Contribution

It presents a novel hybrid clustering approach combining Sorenson's similarity coefficient with other techniques, outperforming previous soft computing methods in cell formation quality.

Findings

Outperforms existing methods on benchmark datasets

Reduces inter-cell and intra-cell movement

Increases machine utilization

Abstract

The machine-part cell formation problem consists of creating machine cells and their corresponding part families with the objective of minimizing the inter-cell and intra-cell movement while maximizing the machine utilization. This article demonstrates a hybrid clustering approach for the cell formation problem in cellular manufacturing that conjoins Sorenson s similarity coefficient based method to form the production cells. Computational results are shown over the test datasets obtained from the past literature. The hybrid technique is shown to outperform the other methods proposed in literature and including powerful soft computing approaches such as genetic algorithms, genetic programming by exceeding the solution quality on the test problems.