Abstract
In this paper, we present a novel optimization algorithm named Discrete Bacterial Chemotaxis Optimization Algorithm DBCOA to solve the formation of manufacturing cells (MCs) problem, which consists in assigning machines and parts to a specific cell or family, considering the similarities in their manufacturing processes. The algorithm is based on BFOA with a discrete and hierarchical chemotaxis process that explore the search space to get the best solution. To evaluate the performance of the proposal, we use seven benchmark problems. The results were compared with the optimum solution and the performance of a Genetic Algorithm. In all benchmark problems, the proposed algorithm outperformed the baseline, giving the optimum solution in a short time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mejia-Moncayo, C., Lara-Sepulveda, D.F., Cordoba-Nieto, E.: Technological kinship circles. Ing. e Investig. 30, 163–167 (2010)
Heragu, S.S.: Facilities Design, 3rd edn. CRC Press, Clermont (2008)
Romero, G.A., Mejia-Moncayo, C., Torres, J.A.: Mathematical models for the definition of cell manufacturing layout. Literature review. Rev. Tecnura 19, 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, 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, 509–521 (2010)
Yin, Y., Yasuda, K.: Similarity coefficient methods applied to the cell formation problem: a comparative investigation. Comput. Ind. Eng. 48, 471–489 (2005)
Yin, Y., Yasuda, K.: Similarity coefficient methods applied to the cell formation problem: a taxonomy and review. Int. J. Prod. Econ. 101, 329–352 (2006)
Tavakkoli-Moghaddam, R., Rahimi-Vahed, A.R., Ghodratnama, A., Siadat, A.: A simulated annealing method for solving a new mathematical model of a multi-criteria cell formation problem with capital constraints. Adv. Eng. Softw. 40, 268–273 (2009)
Lei, D., Wu, Z.: Tabu search approach based on a similarity coefficient for cell formation in generalized group technology. Int. J. Prod. Res. 43, 4035–4047 (2005)
Li, X., Baki, M.F., Aneja, Y.P.: An ant colony optimization metaheuristic for machine part cell formation problems. Comput. Oper. Res. 37, 2071–2081 (2010)
Duran, O., Rodriguez, N., Consalter, L.A.: A PSO-based clustering algorithm for manufacturing cell design. In: First International Workshop on Knowledge Discovery and Data Mining, (WKDD 2008), pp. 72–75 (2008)
Nouri, H., Hong, T.S.: Development of bacteria foraging optimization algorithm for cell formation in cellular manufacturing system considering cell load variations. J. Manuf. Syst. 32, 20–31 (2013)
Mejia-Moncayo, C., Garzon-Alvarado, D.A., Arroyo-Osorio, J.M.: Solution of cell manufacturing layout problem through a discrete hybrid Bfoa-Ga. In: 3rd International Conference Engineering Optimization, vol. 1, pp, 1–5 (2012)
Mejia-Moncayo, C., Garzon, D.A., Arroyo, J.M.: Discrete methods based on bacterial chemotaxis and genetic algorithms to solve the cell manufacturing. Cienc. e Ing. Neogranadina 24, 6–28 (2014)
Al-Sultan, K.S., Fedjki, C.A.: A genetic algorithm for the part family formation problem. Prod. Plan. Control 8, 788–796 (1997)
Mak, K.L., Wong, Y.S., Chan, F.T.S.: A genetic algorithm for facility layout problems. Comput. Integr. Manuf. Syst. 11, 113–127 (1998)
Mak, K.L., Wong, Y.S., Wang, X.X.: An adaptive genetic algorithm for manufacturing cell formation. Int. J. Adv. Manuf. Technol. 16, 491–497 (2000)
Mahdavi, I., Paydar, M.M., Solimanpur, M., Heidarzade, A.: Genetic algorithm approach for solving a cell formation problem in cellular manufacturing. Expert Syst. Appl. 36, 6598–6604 (2009)
Deljoo, V., Mirzapour Al-e-hashem, S.M.J., Deljoo, F., Aryanezhad, M.B.: Using genetic algorithm to solve dynamic cell formation problem. Appl. Math. Model. 34, 1078–1092 (2010)
Saeedi, S.: Heuristic approaches for cell formation in cellular manufacturing. J. Softw. Eng. Appl. 3, 674–682 (2010)
Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. 22, 52–67 (2002)
Niu, B., Fan, Y., Tan, L., Rao, J., Li, L.: A review of bacterial foraging optimization part I: background and development. In: Huang, D.-S., McGinnity, M., Heutte, L., Zhang, X.-P. (eds.) ICIC 2010. CCIS, vol. 93, pp. 535–543. Springer, Heidelberg (2010). doi:10.1007/978-3-642-14831-6_70
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). doi:10.1007/978-3-642-14831-6_71
Gen, M., Lin, L., Zhang, H.: Evolutionary techniques for optimization problems in integrated manufacturing system: state-of-the-art-survey. Comput. Ind. Eng. 56, 779–808 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Mejia-Moncayo, C., Rojas, A.E., Dorado, R. (2017). Manufacturing Cell Formation with a Novel Discrete Bacterial Chemotaxis Optimization Algorithm. In: Figueroa-García, J., López-Santana, E., Villa-Ramírez, J., Ferro-Escobar, R. (eds) Applied Computer Sciences in Engineering. WEA 2017. Communications in Computer and Information Science, vol 742. Springer, Cham. https://doi.org/10.1007/978-3-319-66963-2_51
Download citation
DOI: https://doi.org/10.1007/978-3-319-66963-2_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66962-5
Online ISBN: 978-3-319-66963-2
eBook Packages: Computer ScienceComputer Science (R0)