Machine-Part Formation for Cellular Manufacturing in Group Technology: An Application for Furniture Company

Year 2021, , 466 - 483, 15.04.2021

İlker Güven , Fuat Şimşir

https://doi.org/10.16984/saufenbilder.842423

Cited By: 1

Abstract

Group technology’s basic logic is grouping and producing products of the same type together. An important reason behind Group Technology becoming such an important topic is that nowadays companies have quite an extensive range and workshop type production has increased. Both fuzzy clustering and rank order clustering methods use for grouping parts and machines based on a part-machine matrix created from the production flow technique in order to increase productivity and reduce cost and workmanship required. In this study, Group Technology techniques such as the rank order clustering and fuzzy clustering methods were applied in order to increase the efficiency of the production line, reduce transportation between machines, and form a machine-parts groups in the wood cutting department of a furniture company producing modular furniture in Istanbul. The TOPSIS method was used to determine which products to take into account. According to results of the study, it is shown that fuzzy clustering method has overperformed rank order clustering method based on the evaluation criteria which are group productivity with 21,36%, group efficiency with 43,21% and grouping measure with 82,33%.

Keywords

Group Technology, Fuzzy Clustering, Rank Order Clustering, TOPSIS, Cellular Manufacturing

References

• [1] T. Tunacan, “Machine and Part Cell Formation Using Fuzzy and K-Means Clustering Methods,” Electron. Lett. Sci. Eng., vol. 1, no. 1, pp. 33–41, 2005.
• [2] E. A. Demirtaş, “Hücre oluşturma yöntemleri̇ne i̇li̇şki̇n bi̇r değerlendi̇rme,” Osmangazi Üniversitesi Müh.Mim.Fak.Dergisi, vol. 17, no. 2, 2004.
• [3] M. C. Kaplan, “Grup Teknolojilerinde Kümelendirme Yöntemlerine Sezgisel Yaklaşımlar ve Bir Uygulama,” İstanbul Üniversitesi, 2008.
• [4] E. Babalı, “Grup Teknolojisinde Parça Ailesi ve İmalat Hücresi Oluşturma: Bir Örnek İnceleme,” Sakarya Üniversitesi, 2007.
• [5] Y. Gökşen and S. Erdem, “Hücresel Üretim Sisteminde Makine-Parça Ailelerinin Oluşturulmasında Dengeli Talep-Kapasite ve Dengesiz Talep-Kapasite Durumunun Analizi,” D.E.Ü.İ.İ.B.F.Dergisi, vol. 18, no. 2, pp. 99–111, 2003.
• [6] M. Imran, C. Kang, Y. Hae Lee, J. Zaib, and H. Aziz, “Cell Formation in a Cellular Manufacturing System Using Simulation Integrated Hybrid Genetic Algorithm,” Comput. Ind. Eng., vol. 105, pp. 123–135, 2016, doi: 10.1016/j.cie.2016.12.028.
• [7] A. Tariq, I. Hussain, and A. Ghafoor, “A hybrid genetic algorithm for machine-part grouping,” Comput. Ind. Eng., vol. 56, no. 1, pp. 347–356, 2009, doi: 10.1016/j.cie.2008.06.007.
• [8] T. L. James, E. C. Brown, and K. B. Keeling, “A hybrid grouping genetic algorithm for the cell formation problem,” Comput. Oper. Res., vol. 34, no. 7, pp. 2059–2079, 2007, doi: 10.1016/j.cor.2005.08.010.
• [9] I. Mahdavi, M. M. Paydar, M. Solimanpur, and A. Heidarzade, “Genetic algorithm approach for solving a cell formation problem in cellular manufacturing,” Expert Syst. Appl., vol. 36, no. 3 PART 2, pp. 6598–6604, 2009, doi: 10.1016/j.eswa.2008.07.054.
• [10] T.-H. Wu, C.-C. Chang, and S.-H. Chung, “A simulated annealing algorithm for manufacturing cell formation problems,” Expert Syst. Appl., vol. 34, no. 3, pp. 1609–1617, 2008, doi: 10.1016/j.eswa.2007.01.012.
• [11] A. M. Zohrevand, H. Rafiei, and A. H. Zohrevand, “Multi-objective dynamic cell formation problem: A stochastic programming approach,” Comput. Ind. Eng., vol. 98, pp. 323–332, 2016, doi: 10.1016/j.cie.2016.03.026.
• [12] S. Karthikeyan, M. Saravanan, and K. Ganesh, “GT machine cell formation problem in scheduling for cellular manufacturing system using meta-heuristic method,” Procedia Eng., vol. 38, pp. 2537–2547, 2012, doi: 10.1016/j.proeng.2012.06.299.
• [13] C. R. Shiyas and V. Madhusudanan Pillai, “A mathematical programming model for manufacturing cell formation to develop multiple configurations,” J. Manuf. Syst., vol. 33, no. 1, pp. 149–158, 2014, doi: 10.1016/j.jmsy.2013.10.002.
• [14] H. Nouri and T. S. Hong, “Development of bacteria foraging optimization algorithm for cell formation in cellular manufacturing system considering cell load variations,” J. Manuf. Syst., vol. 32, no. 1, pp. 20–31, 2013, doi: 10.1016/j.jmsy.2012.07.014.
• [15] B. Bootaki, I. Mahdavi, and M. M. Paydar, “A hybrid GA-AUGMECON method to solve a cubic cell formation problem considering different worker skills,” Comput. Ind. Eng., vol. 75, no. 1, pp. 31–40, 2014, doi: 10.1016/j.cie.2014.05.022.
• [16] Z. Güngör and F. Arıkan, “Application of fuzzy decision making in part-machine grouping,” Int. J. Prod. Econ., vol. 63, pp. 181–193, 2000, doi: 10.1016/S0925-5273(99)00010-9.
• [17] M. M. Paydar and M. Saidi-Mehrabad, “A hybrid genetic-variable neighborhood search algorithm for the cell formation problem based on grouping efficacy,” Comput. Oper. Res., vol. 40, no. 4, pp. 980–990, 2013, doi: 10.1016/j.cor.2012.10.016.
• [18] I. Mahdavi, E. Teymourian, N. T. Baher, and V. Kayvanfar, “An integrated model for solving cell formation and cell layout problem simultaneously considering new situations,” J. Manuf. Syst., vol. 32, no. 4, pp. 655–663, 2013, doi: 10.1016/j.jmsy.2013.02.003.
• [19] S. E. Cömert, S. H. Gökler, and H. R. Yazgan, “Hücresel İmalat Sistemlerinin K-Means Algoritması ve Genetik Algoritma İle Tasarlanması: Bir Uygulama,” Acad. Platf. J. Eng. Sci., vol. 4, no. 3, Oct. 2016, doi: 10.21541/apjes.06335.
• [20] L. Jie, W. Liu, Z. Sun, and S. Teng, “Hybrid fuzzy clustering methods based on improved self-adaptive cellular genetic algorithm and optimal-selection-based fuzzy c-means,” Neurocomputing, vol. 0, pp. 1–17, 2017, doi: 10.1016/j.neucom.2017.03.068. [21] R. G. Özdemir, G. Gençyilmaz, and T. Aktin, “The modified fuzzy art and a two-stage clustering approach to cell design,” Inf. Sci. (Ny)., vol. 177, no. 23, pp. 5219–5236, 2007, doi: 10.1016/j.ins.2007.06.027.
• [22] A. Rostami, M. M. Paydar, and E. Asadi-Gangraj, “A hybrid genetic algorithm for integrating virtual cellular manufacturing with supply chain management considering new product development,” Comput. Ind. Eng., vol. 145, p. 106565, 2020, doi: https://doi.org/10.1016/j.cie.2020.106565.
• [23] S. Büyüksaatçı Kiriş and F. Tüysüz, “İmalat Hücresi Oluşturulması İçin Farklı Kümeleme Yöntemlerinin Performans Karşılaştırması,” SAÜ Fen Bilim. Enstitüsü Derg., pp. 1–1, Oct. 2017, doi: 10.16984/saufenbilder.310267.
• [24] C. Mejía-Moncayo and O. Battaia, “A hybrid optimization algorithm with genetic and bacterial operators for the design of cellular manufacturing systems,” IFAC-PapersOnLine, vol. 52, no. 13, pp. 1409–1414, 2019, doi: https://doi.org/10.1016/j.ifacol.2019.11.396.
• [25] S. Kaparthi, N. C. Suresh, and R. P. Cerveny, “An improved neural network leader algorithm for part-machine grouping in group technology,” Eur. J. Oper. Res., vol. 69, no. 3, pp. 342–356, 1993, doi: 10.1016/0377-2217(93)90020-N.
• [26] B. Adenso-Díaz, S. Lozano, and I. Eguía, “Part-machine grouping using weighted similarity coefficients,” Comput. Ind. Eng., vol. 48, no. 3, pp. 553–570, May 2005, doi: 10.1016/j.cie.2003.03.008.
• [27] C. Andrés and S. Lozano, “A particle swarm optimization algorithm for part-machine grouping,” Robot. Comput. Integr. Manuf., vol. 22, no. 5–6, pp. 468–474, 2006, doi: 10.1016/j.rcim.2005.11.013.
• [28] Y. Won and K. R. Currie, “Fuzzy ART/RRR-RSS: a two-phase neural network algorithm for part-machine grouping in cellular manufacturing.,” Int. J. Prod. Res., vol. 45, no. 9, pp. 2073–2104, 2007, doi: 10.1080/00207540600635227.
• [29] J. W. Owsiński, J. Stańczak, K. Sep, and H. Potrzebowski, “Machine-Part Grouping in Flexible Manufacturing: Formalisation and the Use of Genetic Algorithms,” IFAC Proc. Vol., vol. 43, no. 4, pp. 216–221, 2010, doi: 10.3182/20100701-2-PT-4011.00038.
• [30] B. Shirazi, H. Fazlollahtabar, and I. Mahdavi, “A six sigma based multi-objective optimization for machine grouping control in flexible cellular manufacturing systems with guide-path flexibility,” Adv. Eng. Softw., vol. 41, no. 6, pp. 865–873, 2010, doi: 10.1016/j.advengsoft.2010.02.002.
• [31] S. Zolfaghari and M. Liang, “A new genetic algorithm for the machine/part grouping problem involving processing times and lot sizes,” Comput. Ind. Eng., vol. 45, no. 4, pp. 713–731, 2003, doi: 10.1016/j.cie.2003.09.003.
• [32] E. Atmaca, “Grup Teknolojisi Hücrelerinin Tasarımı ve Amaç Programlama Yaklaşımının Uygulanması,” Süleyman Demirel Üniversitesi İktisadi ve İdari Bilim. Fakültesi Derg., vol. 7, no. 2, pp. 285–298, 2002.
• [33] N. Amruthnath and T. Gupta, “Modified Rank Order Clustering Algorithm Approach by Including Manufacturing Data,” IFAC-PapersOnLine, vol. 49, no. 5, pp. 138–142, 2016, doi: 10.1016/j.ifacol.2016.07.103.
• [34] T. Kataoka, “A multi-period mixed integer programming model on reconfigurable manufacturing cells,” Procedia Manuf., vol. 43, pp. 231–238, 2020, doi: https://doi.org/10.1016/j.promfg.2020.02.147.
• [35] J. R. Brown, “A capacity constrained mathematical programming model for cellular manufacturing with exceptional elements,” J. Manuf. Syst., vol. 37, pp. 227–232, 2015, doi: 10.1016/j.jmsy.2014.09.005.
• [36] I. Mahdavi, A. Aalaei, M. M. Paydar, and M. Solimanpur, “A new mathematical model for integrating all incidence matrices in multi-dimensional cellular manufacturing system,” J. Manuf. Syst., vol. 31, no. 2, pp. 214–223, 2012, doi: 10.1016/j.jmsy.2011.07.007.
• [37] S. Arumugam, J. Saral, and A. Somasundaram, “Minimizing the Number of Exceptional Edges in Cellular Manufacturing Problem,” Electron. Notes Discret. Math., vol. 53, pp. 465–472, 2016, doi: 10.1016/j.endm.2016.05.040.
• [38] V. Saddikuti and V. Pesaru, “NSGA Based Algorithm for Energy Efficient Scheduling in Cellular Manufacturing,” Procedia Manuf., vol. 39, pp. 1002–1009, 2019, doi: https://doi.org/10.1016/j.promfg.2020.01.379.
• [39] Z. Hong, Z. Zeng, and L. Gao, “Energy-efficiency scheduling of multi-cell manufacturing system considering total handling distance and eligibility constraints,” Comput. Ind. Eng., p. 106998, 2020, doi: https://doi.org/10.1016/j.cie.2020.106998.
• [40] A. Iqbal and K. A. Al-Ghamdi, “Energy-efficient cellular manufacturing system: Eco-friendly revamping of machine shop configuration,” Energy, vol. 163, pp. 863–872, 2018, doi: https://doi.org/10.1016/j.energy.2018.08.168.
• [41] H. Seifoddini and B. Tjahjana, “Part-family formation for cellular manufacturing: A case study at Harnischfeger,” Int. J. Prod. Res., vol. 37, no. 14, pp. 3263–3273, 1999, doi: 10.1080/002075499190275.
• [42] D. Yu and T. Pan, “Tracing knowledge diffusion of TOPSIS: A historical perspective from citation network,” Expert Syst. Appl., vol. 168, p. 114238, 2021, doi: https://doi.org/10.1016/j.eswa.2020.114238.
• [43] F. Sari, “Forest fire susceptibility mapping via multi-criteria decision analysis techniques for Mugla, Turkey: A comparative analysis of VIKOR and TOPSIS,” For. Ecol. Manage., vol. 480, p. 118644, 2021, doi: https://doi.org/10.1016/j.foreco.2020.118644.
• [44] M. N. Kasirian and R. M. Yusuff, “An integration of a hybrid modified TOPSIS with a PGP model for the supplier selection with interdependent criteria,” Int. J. Prod. Res., vol. 51, no. 4, pp. 1037–1054, 2013, doi: 10.1080/00207543.2012.663107.
• [45] M. Yurdakul and Y. T. Ic, “Development of a performance measurement model for manufacturing companies using the AHP and TOPSIS approaches,” Int. J. Prod. Res., vol. 43, no. 21, pp. 4609–4641, 2005, doi: 10.1080/00207540500161746.
• [46] J. R. King, “Machine-component group formation in group technology,” Omega, vol. 8, no. 2, pp. 193–199, 1980, doi: 10.1016/0305-0483(80)90023-7.
• [47] H. Küçükönder, T. Ayaşan, and H. Hızlı, “Classification of Holstein Dairy Cattles in Terms of Parameters Some Milk Component Belongs by Using The Fuzzy Cluster Analysis,” Kafkas Univ. Vet. Fak. Derg., vol. 23, no. 4, pp. 601–606, 2015, doi: 10.9775/kvfd.2015.12987.
• [48] L. Kaufman and P. J. Rousseuw, “Finding Groups in Data: An Introduction to Cluster Analysis.,” Biometrics, vol. 47, no. 2, p. 788, Jun. 1991, [Online]. Available: https://www.jstor.org/stable/2532178?origin=crossref.
• [49] C. Suresh Kumar and M. P. Chandrasekharan, “Grouping efficacy: a quantitative criterion for goodness of block diagonal forms of binary matrices in group technology,” Int. J. Prod. Res., vol. 28, no. 2, pp. 233–243, 1990, doi: 10.1080/00207549008942706.
• [50] H. S. Shih, H. J. Shyur, and E. S. Lee, “An extension of TOPSIS for group decision making,” Math. Comput. Model., vol. 45, no. 7–8, pp. 801–813, 2007, doi: 10.1016/j.mcm.2006.03.023.