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A Discrete Bacterial Chemotaxis Approach to the Design of Cellular Manufacturing Layouts

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Computational Science and Its Applications – ICCSA 2018 (ICCSA 2018)

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.

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Notes

  1. 1.

    In the following link, are available the five benchmark problems in GAMS format: https://sites.google.com/view/dbcoa-cml/problems.

  2. 2.

    https://neos-server.org/neos/.

References

  1. Tompkins, J.A.: Facilities Planning. Wiley, Hoboken (2010)

    Google Scholar 

  2. 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)

    Google Scholar 

  3. Mejía-Moncayo, C., Lara-Sepúlveda, D.F., Córdoba-Nieto, E.: Technological kinship circles. Ingeniería e Investigación 30(1), 163–167 (2010)

    Google Scholar 

  4. 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

    Book  Google Scholar 

  5. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. 22(3), 52–67 (2002)

    Article  MathSciNet  Google Scholar 

  6. 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)

    Article  Google Scholar 

  7. 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)

    Article  Google Scholar 

  8. 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)

    Article  Google Scholar 

  9. 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)

    Article  Google Scholar 

  10. 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)

    Article  Google Scholar 

  11. 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)

    Article  MathSciNet  Google Scholar 

  12. 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)

    Article  Google Scholar 

  13. Onwubolu, G., Mutingi, M.: A genetic algorithm approach to cellular manufacturing systems. Comput. Ind. Eng. 39(1–2), 125–144 (2001)

    Article  Google Scholar 

  14. 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)

    Article  Google Scholar 

  15. 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)

    Article  Google Scholar 

  16. 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)

    Article  Google Scholar 

  17. 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

    Chapter  Google Scholar 

  18. Saeedi, S.: Heuristic approaches for cell formation in cellular manufacturing. J. Softw. Eng. Appl. 03(07), 674–682 (2010)

    Article  Google Scholar 

  19. Hamann, T., Vernadat, F.: The intra-cell layout problem in automated manufacturing systems. Technical report (1992)

    Google Scholar 

  20. 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)

    Article  Google Scholar 

  21. 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)

    Article  Google Scholar 

  22. 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)

    Article  Google Scholar 

  23. 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

    Chapter  MATH  Google Scholar 

  24. Nouri, H.: Development of a comprehensive model and BFO algorithm for a dynamic cellular manufacturing system. Appl. Math. Model. 40(2), 1514–1531 (2016)

    Article  MathSciNet  Google Scholar 

  25. Atasagun, Y., Kara, Y.: Bacterial foraging optimization algorithm for assembly line balancing. J. Neural Comput. Appl. 25(1), 237–250 (2015)

    Article  Google Scholar 

  26. 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)

    Article  Google Scholar 

  27. Vitanov, V., Tjahjono, B., Marghalany, I.: Heuristic rules-based logic cell formation algorithm. Int. J. Prod. Res. 46(2), 321–344 (2008)

    Article  Google Scholar 

  28. Seifoddini, H., Djassemi, M.: A new grouping measure for evaluation of machine-component matrices. Int. J. Prod. Res. 34(5), 1179–1193 (1996)

    Article  Google Scholar 

  29. 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

    Article  Google Scholar 

  30. Burbidge, J.L.: The Introduction of Group Technology. Wiley, Hoboken (1975)

    Google Scholar 

  31. Chandrasekharan, M.P., Rajagopalan, R.: MODROC: an extension of rank order clustering for group technology. Int. J. Prod. Res. 24(5), 1221–1233 (1986)

    Article  Google Scholar 

  32. Czyzyk, J., Mesnier, M., More, J.: The NEOS server. IEEE Comput. Sci. Eng. 5(3), 68–75 (1998)

    Article  Google Scholar 

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Correspondence to Camilo Mejía-Moncayo .

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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

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  • DOI: https://doi.org/10.1007/978-3-319-95162-1_29

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