Single linkage versus average linkage clustering in machine cells formation applications
Computers and Industrial Engineering
Cell formation in group technology: review, evaluation and directions for future research
Computers and Industrial Engineering - Cellular manufacturing systems: design, analysis and implementation
Cell formation performance measures—determining when to change an existing layout
Computers and Industrial Engineering - Cellular manufacturing systems: design, analysis and implementation
ACM Computing Surveys (CSUR)
Alternatives to the k-means algorithm that find better clusterings
Proceedings of the eleventh international conference on Information and knowledge management
An evolutionary algorithm for manufacturing cell formation
Computers and Industrial Engineering
A mathematical approach for the formation of manufacturing cells
Computers and Industrial Engineering - Special issue: Selected papers from the 31st international conference on computers & industrial engineering
A hybrid grouping genetic algorithm for the cell formation problem
Computers and Operations Research
The complexity of the generalized Lloyd - Max problem (Corresp.)
IEEE Transactions on Information Theory
Expert Systems with Applications: An International Journal
A novel hybrid K-harmonic means and gravitational search algorithm approach for clustering
Expert Systems with Applications: An International Journal
Computers and Operations Research
| Hi-index | 12.05 |
Cellular manufacturing system (CMS) is an application of group technology (GT) to the production environment. There are many advantages of CMS over traditional manufacturing systems like reduction in setup-time, throughput time, etc. The grouping of machine cells and their associated part families so as to minimize the cost of material handling is a major step in CMS and it is called as cell formation (CF) problem. Cell formation is important to the effective performance of manufacturing. In this paper, an attempt has been made to effectively apply the K-harmonic means clustering technique to form machine cells and part families simultaneously, which we call K-harmonic means cell formation (KHM-CF). A set of 20 test problems with various sizes drawn from the literature are used to test the performance of the proposed algorithm. Then, the results are compared with the optimal solution, and the efficacy of the proposed algorithms is discussed. The comparative study shows that the proposed KHM-CF algorithm improves the grouping efficacy for 70% of the test problems, and gives the same results for 30% of the test problems.