Abstract
The design of cellular manufacturing layouts is a very important process, because an adequate placement of machines can reduce costs and waiting times, and ultimately improve the yield of the system. The design process includes two main optimization sub-problems. The first one is a clustering problem, the so-called cell formation, consisting in the definition of groups (the cells) of machines that produce sets of related product parts. The second step is a location-allocation problem, which has to be solved to define the relative position of the cells and of the machines inside each cell. Both problems offer significant challenges from a computational point of view. This paper presents a novel approach for the design of cellular manufacturing layouts through an optimization algorithm based on bacterial chemotaxis. The proposed approach solves simultaneously the two optimization sub-problems mentioned above by minimizing transport cost and maximizing clustering of cells, taking into account the sequencing of production steps, the volume of production and the batch sizes. The performance of the proposed algorithm was tested through benchmark problems, and the results were compared with a genetic algorithm and analytical solutions modeled in GAMS. In all cases our proposal achieves better performance than Genetic Algorithm in quality and time, and comparable results with exact analytical solutions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
In the following link, are available the five benchmark problems in GAMS format: https://sites.google.com/view/dbcoa-cml/problems.
- 2.
References
Tompkins, J.A.: Facilities Planning. Wiley, Hoboken (2010)
Pattanaik, L.N., Sharma, B.P.: Implementing lean manufacturing with cellular layout: a case study. Int. J. Adv. Manufact. Technol. 42(7–8), 772–779 (2008)
Mejía-Moncayo, C., Lara-Sepúlveda, D.F., Córdoba-Nieto, E.: Technological kinship circles. Ingeniería e Investigación 30(1), 163–167 (2010)
Halevi, G.: Expectations and Disappointments of Industrial Innovations. LNMIE, pp. 15–33. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-50702-6
Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. 22(3), 52–67 (2002)
Wemmerlov, U., Johnson, D.J.: Cellular manufacturing at 46 user plants: implementation experiences and performance improvements. Int. J. Prod. Res. 35(1), 29–49 (1997)
Romero, G.A., Mejía-Moncayo, C., Torres, J.A.: Modelos matemáticos para la definición del layout de las celdas de manufactura. Revisión de literatura. Revista Tecnura 19(46), 135–148 (2015)
Selim, H.M., Askin, R.G., Vakharia, A.J.: Cell formation in group technology: review, evaluation and directions for future research. Comput. Ind. Eng. 34(1), 3–20 (1998)
Papaioannou, G., Wilson, J.M.: The evolution of cell formation problem methodologies based on recent studies (1997–2008): review and directions for future research. Eur. J. Oper. Res. 206(3), 509–521 (2010)
Yin, Y., Yasuda, K.: Similarity coefficient methods applied to the cell formation problem: a taxonomy and review. Int. J. Prod. Econ. 101(2), 329–352 (2006)
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)
Lei, D., Wu, Z.: Tabu search approach based on a similarity coefficient for cell formation in generalized group technology. Int. J. Prod. Res. 43(19), 4035–4047 (2005)
Onwubolu, G., Mutingi, M.: A genetic algorithm approach to cellular manufacturing systems. Comput. Ind. Eng. 39(1–2), 125–144 (2001)
Li, X., Baki, M.F., Aneja, Y.P.: An ant colony optimization metaheuristic for machinepart cell formation problems. Comput. Oper. Res. 37(12), 2071–2081 (2010)
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)
Nouri, H., Tang, S.H., Hang Tuah, B.T., Anuar, M.K.: BASE: a bacteria foraging algorithm for cell formation with sequence data. J. Manufact. Syst. 29(2–3), 102–110 (2010)
Mejia-Moncayo, C., Rojas, A.E., Dorado, R.: Manufacturing cell formation with a novel Discrete Bacterial Chemotaxis Optimization Algorithm. In: Figueroa-García, J.C., López-Santana, E.R., Villa-Ramírez, J.L., Ferro-Escobar, R. (eds.) WEA 2017. CCIS, vol. 742, pp. 579–588. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66963-2_51
Saeedi, S.: Heuristic approaches for cell formation in cellular manufacturing. J. Softw. Eng. Appl. 03(07), 674–682 (2010)
Hamann, T., Vernadat, F.: The intra-cell layout problem in automated manufacturing systems. Technical report (1992)
Elwany, M., Khairy, A.B., Abou-Ali, M., Harraz, N.: A combined multicriteria approach for cellular manufacturing layout. CIRP Ann.- Manuf. Technol. 46(1), 369–371 (1997)
Solimanpur, M., Vrat, P., Shankar, R.: An ant algorithm for the single row layout problem in flexible manufacturing systems. Comput. Oper. Res. 32(3), 583–598 (2005)
Suresh Kumar, C., Chandrasekharan, M.P.: Grouping efficacy: a quantitative criterion for goodness of block diagonal forms of binary matrices in group technology. Int. J. Prod. Res. 28(2), 233–243 (1990)
Niu, B., Fan, Y., Tan, L., Rao, J., Li, L.: A review of bacterial foraging optimization part II : applications and challenges. In: Huang, D.-S., McGinnity, M., Heutte, L., Zhang, X.-P. (eds.) ICIC 2010. CCIS, vol. 93, pp. 544–550. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14831-6_71
Nouri, H.: Development of a comprehensive model and BFO algorithm for a dynamic cellular manufacturing system. Appl. Math. Model. 40(2), 1514–1531 (2016)
Atasagun, Y., Kara, Y.: Bacterial foraging optimization algorithm for assembly line balancing. J. Neural Comput. Appl. 25(1), 237–250 (2015)
Gen, M., Lin, L., Zhang, H.: Evolutionary techniques for optimization problems in integrated manufacturing system: State-of-the-art-survey. Comput. Ind. Eng. 56(3), 779–808 (2009)
Vitanov, V., Tjahjono, B., Marghalany, I.: Heuristic rules-based logic cell formation algorithm. Int. J. Prod. Res. 46(2), 321–344 (2008)
Seifoddini, H., Djassemi, M.: A new grouping measure for evaluation of machine-component matrices. Int. J. Prod. Res. 34(5), 1179–1193 (1996)
King, J.R.: Machine-component group formation in group technology. Omega 8(2), 193–199 (1980). https://doi.org/10.1016/0305-0483(80)90023-7
Burbidge, J.L.: The Introduction of Group Technology. Wiley, Hoboken (1975)
Chandrasekharan, M.P., Rajagopalan, R.: MODROC: an extension of rank order clustering for group technology. Int. J. Prod. Res. 24(5), 1221–1233 (1986)
Czyzyk, J., Mesnier, M., More, J.: The NEOS server. IEEE Comput. Sci. Eng. 5(3), 68–75 (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Mejía-Moncayo, C., Rojas, A.E., Mura, I. (2018). A Discrete Bacterial Chemotaxis Approach to the Design of Cellular Manufacturing Layouts. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10960. Springer, Cham. https://doi.org/10.1007/978-3-319-95162-1_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-95162-1_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95161-4
Online ISBN: 978-3-319-95162-1
eBook Packages: Computer ScienceComputer Science (R0)