2012/12/20 by Sourav Sengupta, Sengupta, Sourav, Tamal Ghosh +6
Computer Science · Engineering · #Advanced Manufacturing and Logistics Optimization #Assembly Line Balancing Optimization #FOS: Computer and information sciences #Machine Learning (cs.LG) #Optimization and Packing Problems #cs.LG
paper · pdf · doi:10.48550/arxiv.1212.5101
Proceedings of International Conference on Operational Excellence for Global Competitiveness (ICOEGC 2011)
arxiv created 2012/12/20 · openalex publication_date 2012/12/20 · arxiv updated 2012/12/21 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
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.