A Comparative Investigation of Similarity Coefficients Applied to the Cell Formation Problem using Hybrid Clustering Algorithms
Pachayappan, M.; Panneerselvam, R.
Materials Today Proceedings 5(5): 12285-12302
2018
ISSN/ISBN: 2214-7853 DOI: 10.1016/j.matpr.2018.02.207
Accession: 080970740
Full-Text Article emailed within 0-6 h
Payments are secure & encrypted

References
Yin, Y.; Yasuda, K. 2005: Similarity coefficient methods applied to the cell formation problem: a comparative investigation Computers-Industrial Engineering 48(3): 471-489Asoo, J. Vakharia; Urban Wemmerlöv 1995: A comparative investigation of hierarchical clustering techniques and dissimilarity measures applied to the cell formation problem Journal of Operations Management 13(2): 117-138
Mosier, C.T. 1989: An experiment investigating the application of clustering procedures and similarity coefficients to the GT machine cell formation problem International Journal of Production Research 27(10): 1811-1835
Seifoddini, H.; Hsu, C. 1994: Comparative study of similarity coefficients and clustering algorithms in cellular manufacturing Journal of Manufacturing Systems 13(2): 119-127
Yong Yin; Kazuhiko Yasuda 2006: Similarity coefficient methods applied to the cell formation problem: A taxonomy and review International Journal of Production Economics 101(2): 329-352
Thanh, L.T.; Ferland, J. A.; Elbenani, B.; Dinh Thuc, N.; Hien Nguyen, V. 2016: A computational study of hybrid approaches of metaheuristic algorithms for the cell formation problem Journal of the Operational Research Society 67(1): 20-36
Durga Rajesh, K.; Mani Krishna, M.; Ali, M.; Chalapathi, P. 2017: A Modified Hybrid Similarity Coefficient Based Method for Solving the Cell Formation Problem in Cellular Manufacturing System Materials Today: Proceedings 4(2): 1469-1477
Fraboni, M.; Cooper, D. 1989: Six clustering algorithms applied to the WAIS-R: the problem of dissimilar cluster results Journal of Clinical Psychology 45(6): 932-935
John Miltenburg; Wenjiang Zhang 1991: A comparative evaluation of nine well-known algorithms for solving the cell formation problem in group technology Journal of Operations Management 10(1): 44-72
Al Khalifa, A.; Haranczyk, M.; Holliday, J. 2009: Comparison of nonbinary similarity coefficients for similarity searching, clustering and compound selection Journal of Chemical Information and Modeling 49(5): 1193-1201
Martins, I.C.; Pinheiro, R.G.; Protti, F.; Ochi, L.S. 2015: A hybrid iterated local search and variable neighborhood descent heuristic applied to the cell formation problem Expert Systems with Applications 42(22): 8947-8955
Taguchi, T.; Yokota, T.; Gen, M. 1998: Reliability Optimal design problem with interval coefficients using Hybrid Genetic Algorithms International Conference on Computers and Industrial Engineering 35(1-2): 373-376
Ezugwu, A.E.; Agbaje, M.B.; Aljojo, N.; Els, R.; Chiroma, H.; Elaziz, M.A. 2020: A Comparative Performance Study of Hybrid Firefly Algorithms for Automatic Data Clustering IEEE Access 8: 121089-121118
Singh, G.; Gavel, S.; Raghuvanshi, A.S. 2020: Comparative Study of PSO-Based Hybrid Clustering Algorithms for Wireless Sensor Networks Lecture Notes in Electrical Engineering 587: 133-140
Moreira De Souza Amorim, F.; Da Silva Arantes, M.; Motta Toledo, C.F.; Frisch, P.E.; Almada-Lobo, B. 2018: Hybrid Genetic Algorithms Applied to the Glass Container Industry Problem 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings: 8477762
Carvalho, V.P.; Sant'anna, I.C.; Nascimento, M.; Nascimento, A.C.C.; Cruz, C.D.; Arbex, W.A.; Oliveira, F.C.; Silva, F.F. 2018: Support vector machines applied to the genetic classification problem of hybrid populations with high degrees of similarity Genetics and Molecular Research 17(4): gmr18122
Golneshini, F.P.; Fazlollahtabar, H. 2019: Meta-heuristic algorithms for a clustering-based fuzzy bi-criteria hybrid flow shop scheduling problem Soft Computing 23(22): 12103-12122
Sáez, D.; Cortés, C.E.; Núñez, A. 2008: Hybrid adaptive predictive control for the multi-vehicle dynamic pick-up and delivery problem based on genetic algorithms and fuzzy clustering Computers-Operations Research 35(11): 3412-3438
Al-Mudhafar, W.; Rostami, A. 2014: Comparative applied multivariate geostatistical algorithms for formation permeability modeling 20th Formation Evaluation Symposium of Japan 2014
Balakrishnan, J.; Jog, P.D. 1995: Manufacturing cell formation using similarity coefficients and a parallel genetic TSP algorithm: formulation and comparison Math. Comput. Model 21(12): 61-73