Abstract
Cell formation (CF) is a key step in group technology (GT). This combinatorial optimization problem is NP-complete. So, meta-heuristic algorithms have been extensively adopted to efficiently solve the CF problem. Particle swarm optimization (PSO) is a modern evolutionary computation technique based on a population mechanism. Since Kennedy and Eberhart invented the PSO, the challenge has been to employ the algorithm to different problem areas other than those that the inventors originally focused on. This paper investigates the first applications of this emerging novel optimization algorithm into the CF problem, and a newly developed PSO-based optimization algorithm for it is elaborated. Forming manufacturing cells lead to process each part family within a machine group with reduction intracellular travel of parts and setup time. A maximum number of machines in a cell and the maximum number of cells are imposed. Some published results in various problem sizes have been used as benchmarks to assess the proposed algorithm. Overall, the advantages of the proposed PSO are that it is rapidly converging towards an optimum, there are fewer parameters to adjust, it is simple to compute, it is easy to implement, it is free from the complex computation, and it is very efficient to use in CF with a wide variety of machine/part matrices.
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References
Ballakur A, Steudel HJ (1987) A within cell utilization based heuristic for designing cellular manufacturing systems. Int J Prod Res 25:639–655
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proc. of IEEE Int. Conf. on Neural Networks, Perth, Australia: 1942–1948
Yang MSh, Yang JH (2007) Machine–part cell formation in group technology using a modified ART1 method. European Journal of Operational Research, Production, Manufacturing and Logistics, In press
Wemmerlov U, Hyer NL (1989) Cellular manufacturing in the US industry: a survey of users. Int J Prod Res 27(9):1511–1530
Wu T, Chang Ch, Chung Sh (2008) A simulated annealing algorithm for manufacturing cell formation problems. Int J Expert Syst Appl 34(3):1609–1617
Andre´s C, Lozano S (2006) A particle swarm optimization algorithm for part–machine grouping. Robot Comput-Integr Manuf 22:468–474
Mahdavi I, Mahadevan B (2008) CLASS: an algorithm for cellular manufacturing system and layout design using sequence data. Robot Comput-Integr Manuf 24(3):488–497
Yang X, Yuan J, Yuan J, Mao H (2007) A modified particle swarm optimizer with dynamic adaptation. Appl Math Comput 189:1205–1213
Allahverdi A, Al-Anzi FS (2006) A PSO and a Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application. Comput Oper Res 33:1056–1080
Eberhart RC, Shi Y (1998) Comparison between genetic algorithms and particle swarm optimization. In: Porto VW, Saravanan N, Waagen D, Eiben AE (eds) Evolutionary programming VII, Lecture Notes in Computer Science 1447. Springer, Berlin, pp 611–6
Biswas S, Mahapatra SS (2007) Machine loading in flexible manufacturing system: a swarm optimization approach. Eighth Int. Conference on Opers. & Quant. Management: 621–628
Parsopoulos KE, Vrahatis MN (2002) Particle swarm optimization method for constrained optimization problems. In P. Sincak, J. Vascak, V. Kvasnicka, and J. Pospichal, editors, Intelligent Technologies—Theory and Application: New Trends in Intelligent Technologies. Frontiers in Artificial Intelligence and Applications 76, IOS Press: 214–220
Hu X, Eberhart RC (2002) Solving constrained nonlinear optimization problems with particle swarm optimization. In: Proc. of the Sixth World Multi conference on Systemic, Cybernetics and Informatics 2002 (SCI 2002), Orlando, USA: 203–206
Laskari EC, Parsopoulos KE, Vrahatis MN (2002) Particle swarm optimization for integer programming. In: Proc. of the IEEE Congress on Evolutionary Computation (CEC 2002), Honolulu, Hawaii, USA: 1582–1587
Yoshida H, Kawata K, Fukuyama Y, Nakanishi Y (2000) A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Security Assessment. IEEE Transactions on Power Systems: 1232–1239
Guo YW, Li WD, Mileham AR, Owen GW (2009) Applications of particle swarm optimization in integrated process planning and scheduling. Robot Comput-Integr Manuf 25(2):280–288
Solon C (2002) Ants can solve constraint satisfaction problems. IEEE Trans Evol Comput 6(4):347–357
Yang Q, Sun J, Zhang J, Wang Ch (2006) A Hybrid Discrete Particle Swarm Algorithm for Hard Binary CSPs. Springer-Verlag Berlin Heidelberg; ICNC Part II; LNCS 4222: 184–193
Kennedy J, Eberhart RC (1995b) A new optimizer using particle swarm theory. in: Proc. of the Sixth Int. Symp. On Micro Machine and Human Science (MHS'95), Nagoya, Japan: 39–43
De Castro LN (2002) Immune, Swarm, and Evolutionary Algorithms Part I: Basic Models. Proc. of the ICONIP Conference (International Conference on Neural Information Processing), Workshop on Artificial Immune Systems; Singapura: 1464–1468
Zhang H, Li H, Tam CM (2006) Particle swarm optimization for resource-constrained project scheduling. Int J Proj Manag 24:83–92
Sevkli M, Guner AR (2006) A New Approach to Solve Un capacitated Facility Location Problems by Particle Swarm Optimization. Proceedings of 5th International Symposium on Intelligent Manufacturing Systems 29–31:237–246
Boulif M, Atif K (2007) A new fuzzy genetic algorithm for the dynamic bi-objective cell formation problem considering passive and active strategies. International Journal of Approximate Reasoning In press
Chandrashekharan MP, Rajagopalan R (1986) An ideal seed nonhierarchical clustering algorithm for cellular manufacturing. Int J Prod Res 24(2):451–464
Kumar CS, Chandrasekharan MP (1990) Grouping efficacy: a quantitative criterion for goodness of block diagonal forms of binary matrices in group technology. Int J Prod Res 28:233–243
Dagli C, Huggahalli R (1995) Machine–part family formation with the adaptive resonance theory paradigm. Int J Prod Res 33:893–913
Mahdavi I, Kaushal OP, Chandra M (2001) Graph-neural network approach in cellular manufacturing on the basis of a binary system. Int J Prod Res 39(13):2913–2922
Chen SJ, Cheng CS (1995) A neural network-based cell formation algorithm in cellular manufacturing. Int J Prod Res 33(2):293–318
Venagupal V, Narendran TT (1992) Cell formation in manufacturing systems through simulated annealing: an experimental evaluation. Eur J Oper Res 63:409–422
Solimanpur M, Vrat P, Shanker R (2004) A heuristic to minimize makespan of cell scheduling problem. Int J Prod Econ 88(3):231–241
Giri R, Srinivas J, Mouli KVVC (2007) An optimal design approach for a cellular manufacturing system. Proc. IMechE 221 Part B: J. Engineering Manufacture: 1101–1106
Harhalakis G, Nagi R, Proth JM (1990) An efficient heuristic in manufacturing cell formation for group technology applications. Int J Prod Res 28(1):185–198
Srinvansan G, Narendran TT, Mahadevan B (1990) An assignment model for the part-families problem in group technology. Int J Prod Res 28(1):145–152
Kennedy J, Eberhart R, Shi Y (2002) Swarm Intelligence. Morgan Kaufmann
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Anvari, M., Mehrabad, M.S. & Barzinpour, F. Machine–part cell formation using a hybrid particle swarm optimization. Int J Adv Manuf Technol 47, 745–754 (2010). https://doi.org/10.1007/s00170-009-2202-9
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DOI: https://doi.org/10.1007/s00170-009-2202-9