Machine–part cell formation using a hybrid particle swarm optimization

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.

[1]  Tai-Hsi Wu,et al.  A simulated annealing algorithm for manufacturing cell formation problems , 2008, Expert Syst. Appl..

[2]  Yoshikazu Fukuyama,et al.  A particle swarm optimization for reactive power and voltage control considering voltage security assessment , 2000 .

[3]  Chuen-Sheng Cheng,et al.  A neural network-based cell formation algorithm in cellular manufacturing , 1995 .

[4]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.

[5]  T. Narendran,et al.  An assignment model for the part-families problem in group technology , 1990 .

[6]  Russell C. Eberhart,et al.  Solving Constrained Nonlinear Optimization Problems with Particle Swarm Optimization , 2002 .

[7]  R. Huggahalli,et al.  Machine-part family formation with the adaptive resonance theory paradigm , 1995 .

[8]  Rakesh Nagi,et al.  An efficient heuristic in manufacturing cell formation for group technology applications , 1990 .

[9]  Siba Sankar Mahapatra,et al.  Machine Loading in Flexible Manufacturing System:A Swarm Optimization Approach , 2007 .

[10]  M. Chandrasekharan,et al.  Grouping efficacy: a quantitative criterion for goodness of block diagonal forms of binary matrices in group technology , 1990 .

[11]  Harold J. Steudel,et al.  A within-cell utilization based heuristic for designing cellular manufacturing systems , 1987 .

[12]  Christine Solnon,et al.  Ants can solve constraint satisfaction problems , 2002, IEEE Trans. Evol. Comput..

[13]  Miin-Shen Yang,et al.  Machine-part cell formation in group technology using a modified ART1 method , 2008, Eur. J. Oper. Res..

[14]  J Srinivas,et al.  An optimal design approach for a cellular manufacturing system , 2007 .

[15]  Menouar Boulif,et al.  A new fuzzy genetic algorithm for the dynamic bi-objective cell formation problem considering passive and active strategies , 2008, Int. J. Approx. Reason..

[16]  A. Mileham,et al.  Applications of particle swarm optimisationin integrated process planning and scheduling , 2009 .

[17]  Jiangye Yuan,et al.  A modified particle swarm optimizer with dynamic adaptation , 2007, Appl. Math. Comput..

[18]  Iraj Mahdavi,et al.  Graph-neural network approach in cellular manufacturing on the basis of a binary system , 2001 .

[19]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[20]  T. T. Narendran,et al.  Cell formation in manufacturing systems through simulated annealing: An experimental evaluation , 1992 .

[21]  Sebastián Lozano,et al.  A particle swarm optimization algorithm for part–machine grouping , 2006 .

[22]  Michael N. Vrahatis,et al.  Particle swarm optimization for integer programming , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[23]  Iraj Mahdavi,et al.  CLASS: An algorithm for cellular manufacturing system and layout design using sequence data , 2008 .

[24]  Hong Zhang,et al.  Particle swarm optimization for resource-constrained project scheduling , 2006 .

[25]  Maghsud Solimanpur,et al.  A heuristic to minimize makespan of cell scheduling problem , 2004 .

[26]  L. N. de Castro.  Immune, swarm, and evolutionary algorithms. Part I: basic models , 2002 .

[27]  Fawaz S. Al-Anzi,et al.  A PSO and a Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application , 2006, Comput. Oper. Res..

[28]  M. Chandrasekharan,et al.  An ideal seed non-hierarchical clustering algorithm for cellular manufacturing , 1986 .

[29]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[30]  Nancy Lea Hyer,et al.  Cellular manufacturing in the U.S. industry: a survey of users , 1989 .