Abstract
Group technology is a concept that emerged in the manufacturing field almost seventy years ago. Since then, group technology has been widely applied by means of a cellular manufacturing philosophy application called the cell formation problem. In this paper, we focus on adapting the discrete gravitational search algorithm to the cell formation problem. The mathematical model and the discrete gravitational search algorithm stages are detailed thereafter. To evaluate the algorithm’s performance, thirty-five tests were carried out on widely used benchmarks. The results obtained were satisfactory to confirm successful adaptation of the gravitational search algorithm. Indeed, the algorithm reached thirty best values of benchmarks obtained by previous algorithms. The algorithm also outperformed the best-known solution of one benchmark.
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References
Akay, B., Karaboga, D.: Artificial bee colony algorithm for large-scale problems and engineering design optimization. J. Intell. Manuf. 23(4), 1001–1014 (2010)
Burbidge, J.L.: The introduction of group technology. John Wiley & Sons, Incorporated, London (1975)
Chakraborty, P., et al.: On convergence of the multi-objective particle swarm optimizers. Inf. Sci. 181(8), 1411–1425 (2011)
Dagli, C., Huggahalli, R.: Machine-part family formation with the adaptive resonance theory paradigm. Int. J. Prod. Res. 33(4), 893–913 (1995)
Dimopoulos, C., Zalzala, A.M.S.: Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons. IEEE Trans. Evol. Comput. 4(2), 93–113 (2000)
Doraghinejad, M., et al.: Channel assignment in multi-radio wireless mesh networks using an improved gravitational search algorithm. J. Netw. Comput. Appl. 38, 163–171 (2014)
Dowlatshahi, M.B., et al.: A discrete gravitational search algorithm for solving combinatorial optimization problems. Inf. Sci. 258, 94–107 (2014)
Elbenani, B., et al.: Genetic algorithm and large neighbourhood search to solve the cell formation problem. Expert Syst. Appl. 39(3), 2408–2414 (2012)
Eusuff, M.M., Lansey, K.E.: Optimization of water distribution network design using the shuffled frog leaping algorithm. J. Water Resour. Plan. Manage. 129(3), 10–25 (2003)
Farmer, J.D., et al.: The immune system, adaptation, and machine learning. Phys. D 2(1–3), 187–204 (1986)
Goldengorin, B., et al.: The problem of cell formation: ideas and their applications. In: Cell Formation in Industrial Engineering. pp. 1–23. Springer, New York (2013)
Gonçalves, J.F., Resende, M.G.C.: An evolutionary algorithm for manufacturing cell formation. Comput. Ind. Eng. 47(2–3), 247–273 (2004)
Gravel, M., Nsakanda, A.L.: Efficient solutions to the cell-formation problem with multiple routings via a double-loop genetic algorithm. Eur. J. Oper. Res. 109(2), 286–298 (1998)
Holland, J.H.: Outline for a logical theory of adaptive systems. J. ACM 9(3), 297–314 (1962)
Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, MI, USA (1975)
James, T.L., et al.: A hybrid grouping genetic algorithm for the cell formation problem. Comput. Oper. Res. 34(7), 2059–2079 (2007)
Lei, D., Wu, Z.: Tabu search for multiple-criteria manufacturing cell design. Int. J. Adv. Manuf. Technol. 28(9–10), 950–956 (2006)
Li, X., et al.: An ant colony optimization metaheuristic for machine-part cell formation problems. Comput. Oper. Res. 37(12), 2071–2081 (2010)
Luo, J., Tang, L.: A hybrid approach of ordinal optimization and iterated local search for manufacturing cell formation. Int. J. Adv. Manuf. Technol. 40(3–4), 362–372 (2008)
Mahdavi, I., et al.: Genetic algorithm approach for solving a cell formation problem in cellular manufacturing. Expert Syst. Appl. 36(3), 6598–6604 (2009)
Mukattash, A.M., et al.: Heuristic approaches for part assignment in cell formation. Comput. Ind. Eng. 42(2–4), 329–341 (2002)
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)
Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. 22(3), 52–67 (2002)
Rashedi, E., et al.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)
Rezazadeh, H., et al.: Solving a dynamic virtual cell formation problem by linear programming embedded particle swarm optimization algorithm. Appl. Soft Comput. 11(3), 3160–3169 (2011)
Shi, W., et al.: QSAR analysis of tyrosine kinase inhibitor using modified ant colony optimization and multiple linear regression. Eur. J. Med. Chem. 42(1), 81–86 (2007)
Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)
Soleymanpour, M., et al.: A transiently chaotic neural network approach to the design of cellular manufacturing. Int. J. Prod. Res. 40(10), 2225–2244 (2002)
Souilah, A.: Simulated annealing for manufacturing systems layout design. Eur. J. Oper. Res. 82(3), 592–614 (1995)
Tian, H., et al.: Multi-objective optimization of short-term hydrothermal scheduling using non-dominated sorting gravitational search algorithm with chaotic mutation. Energy Convers. Manage. 81, 504–519 (2014)
Tunnukij, T., Hicks, C.: An enhanced grouping genetic algorithm for solving the cell formation problem. Int. J. Prod. Res. 47(7), 1989–2007 (2009)
Venkumar, P., Haq, A.N.: Complete and fractional cell formation using Kohonen self-organizing map networks in a cellular manufacturing system. Int. J. Prod. Res. 44(20), 4257–4271 (2006)
Yang, X.-S.: Harmony search as a metaheuristic algorithm. In: Geem, Z.W. (ed.) Music-Inspired Harmony Search Algorithm, pp. 1–14. Springer, Berlin (2009)
Yang, X.-S., Deb, S.: Cuckoo search via Lévy flights. In: World Congress on Nature & Biologically Inspired Computing, NaBIC 2009, Coimbatore, India. pp. 210–214, 9–11 Dec 2009
Zolfaghari, S.: An objective-guided ortho-synapse Hopfield network approach to machine grouping problems
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Zettam, M., Elbenani, B. (2016). Gravitational Search Algorithm Applied to the Cell Formation Problem. In: Yang, XS. (eds) Nature-Inspired Computation in Engineering. Studies in Computational Intelligence, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-319-30235-5_12
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DOI: https://doi.org/10.1007/978-3-319-30235-5_12
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