Abstract
The manufacturing cell design problem (MCDP) proposes to divide an industrial production plant into a number of manufacturing cells. The main objective is to identify an organization of machines and parts in a set of manufacturing cells to allow the transport of parts to be minimized. In this research, the metaheuristic algorithm called Artificial Bee Colony (ABC) is implemented to solve the MCDP. The ABC algorithm is inspired by the ability of bees to get food, the way they look for it and exploit it. We performed two types of experiments using two and three cells, giving a total of 90 problems that have been used to solve the MCDP using ABC. In the results experiments, good results are obtained solving the 90 proposed problems and reaching the 90 global optimum values. Finally, the results are contrasted with two classical metaheuristics and two modern metaheuristics.
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References
Adil, G.K., Rajamani, D., Strong, D.: A mathematical model for cell formation considering investment and operational costs. Eur. J. Oper. Res. 69(3), 330–341 (1993)
Aljaber, N., Baek, W., Chen, C.L.: A tabu search approach to the cell formation problem. Comput. Ind. Eng. 32(1), 169–185 (1997)
Almonacid, B.: Dataset - Solving the Manufacturing Cell Design Problem using the Artificial Bee Colony, August 2017. https://figshare.com/articles/Dataset_-_Solving_the_Manufacturing_Cell_Design_Problem_using_the_Artificial_Bee_Colony_/5270983
Atmani, A., Lashkari, R., Caron, R.: A mathematical programming approach to joint cell formation and operation allocation in cellular manufacturing. Int. J. Prod. Res. 33(1), 1–15 (1995)
Boctor, F.F.: A linear formulation of the machine-part cell formation problem. Int. J. Prod. Res. 29(2), 343–356 (1991)
Burbidge, J.L.: Production flow analysis for planning group technology. J. Oper. Manag. 10(1), 5–27 (1991)
Deriche, R., Fizazi, H.: The artificial bee colony algorithm for unsupervised classification of meteorological satellite images. Int. J. Comput. Appl. 112(12) (2015)
Deutsch, S.J., Freeman, S.F., Helander, M.: Manufacturing cell formation using an improved p-median model. Comput. Ind. Eng. 34(1), 135–146 (1998)
Duran, O., Rodriguez, N., Consalter, L.A.: Hybridization of PSO and a discrete position update scheme techniques for manufacturing cell design. In: Gelbukh, A., Morales, E.F. (eds.) MICAI 2008. LNCS, vol. 5317, pp. 503–512. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88636-5_48
Durán, O., Rodriguez, N., Consalter, L.A.: Collaborative particle swarm optimization with a data mining technique for manufacturing cell design. Expert Syst. Appl. 37(2), 1563–1567 (2010)
James, T.L., Brown, E.C., Keeling, K.B.: A hybrid grouping genetic algorithm for the cell formation problem. Comput. Oper. Res. 34(7), 2059–2079 (2007)
Karaboga, D., Basturk, B.: Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. LNCS, vol. 4529, pp. 789–798. Springer, Heidelberg (2007). doi:10.1007/978-3-540-72950-1_77
Kusiak, A.: The part families problem in flexible manufacturing systems. Ann. Oper. Res. 3(6), 277–300 (1985)
Li, B., Liu, F., Bai, X.: Artificial bee colony algorithm optimization for human-machine interface layout of cabin driver’s desk. In: Proceedings of the Fourth International Conference on Information Science and Cloud Computing (ISCC 2015), Guangzhou, China, 18–19 December 2015 (2015)
Lozano, S., Adenso-Diaz, B., Eguia, I., Onieva, L., et al.: A one-step tabu search algorithm for manufacturing cell design. J. Oper. Res. Soc. 50(5), 509–516 (1999)
Menon, N., Ramakrishnan, R.: Brain tumor segmentation in MRI images using unsupervised artificial bee colony algorithm and FCM clustering. In: 2015 International Conference on Communications and Signal Processing (ICCSP), pp. 0006–0009. IEEE (2015)
Nsakanda, A.L., Diaby, M., Price, W.L.: Hybrid genetic approach for solving large-scale capacitated cell formation problems with multiple routings. Eur. J. Oper. Res. 171(3), 1051–1070 (2006)
Oliva-Lopez, E., Purcheck, G.: Load balancing for group technology planning and control. Int. J. Mach. Tool Des. Res. 19(4), 259–274 (1979)
Purcheck, G.F.: A linear programming method for the combinatorial grouping of an incomplete power set (1975)
Seifoddini, H., Hsu, C.P.: Comparative study of similarity coefficients and clustering algorithms in cellular manufacturing. J. Manuf. Syst. 13(2), 119–127 (1994)
Shargal, M., Shekhar, S., Irani, S.: Evaluation of search algorithms and clustering efficiency measures for machine-part matrix clustering. IIE Trans. 27(1), 43–59 (1995)
Soto, R., Crawford, B., Almonacid, B., Paredes, F.: A migrating birds optimization algorithm for machine-part cell formation problems. In: Sidorov, G., Galicia-Haro, S.N. (eds.) MICAI 2015. LNCS, vol. 9413, pp. 270–281. Springer, Cham (2015). doi:10.1007/978-3-319-27060-9_22
Soto, R., Crawford, B., Almonacid, B., Paredes, F.: Efficient parallel sorting for migrating birds optimization when solving machine-part cell formation problems. Sci. Program. (2016)
Soto, R., Crawford, B., Vega, E., Johnson, F., Paredes, F.: Solving manufacturing cell design problems using a shuffled frog leaping algorithm. In: Gaber, T., Hassanien, A.E., El-Bendary, N., Dey, N. (eds.) The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt. AISC, vol. 407, pp. 253–261. Springer, Cham (2016). doi:10.1007/978-3-319-26690-9_23
Soto, R., Kjellerstrand, H., Durán, O., Crawford, B., Monfroy, E., Paredes, F.: Cell formation in group technology using constraint programming and boolean satisfiability. Expert Syst. Appl. 39(13), 11423–11427 (2012)
Srinivasan, G.: A clustering algorithm for machine cell formation in group technology using minimum spanning trees. Int. J. Prod. Res. 32(9), 2149–2158 (1994)
Wu, T.H., Chang, C.C., Chung, S.H.: A simulated annealing algorithm for manufacturing cell formation problems. Expert Syst. Appl. 34(3), 1609–1617 (2008)
Xambre, A.R., Vilarinho, P.M.: A simulated annealing approach for manufacturing cell formation with multiple identical machines. Eur. J. Oper. Res. 151(2), 434–446 (2003)
Yurtkuran, A., Emel, E.: A discrete artificial bee colony algorithm for single machine scheduling problems. Int. J. Prod. Res. 1–19 (2016)
Acknowledgements
Ricardo Soto is supported by Grant CONICYT/FONDECYT/REGULAR/1160455. Broderick Crawford is supported by Grant CONICYT/FONDECYT/REGULAR/1171243. Boris Almonacid is supported by Postgraduate Grant Pontificia Universidad Católica de Valparaíso, Chile (VRIEA 2016 and INF-PUCV 2015) and by Animal Behavior Society, USA (Developing Nations Research Awards 2016). Also, we thank the anonymous reviewers for their constructive comments.
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Soto, R. et al. (2017). Solving the Manufacturing Cell Design Problem Using the Artificial Bee Colony Algorithm. In: Phon-Amnuaisuk, S., Ang, SP., Lee, SY. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2017. Lecture Notes in Computer Science(), vol 10607. Springer, Cham. https://doi.org/10.1007/978-3-319-69456-6_39
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