APPLYING SWARM INTELLIGENCE TO DESIGN THE RECONFIGURABLE FLOW LINES

Abstract Reconfigurable Manufacturing System (RMS) justifies the need of hour by combining the high throughput of dedicated manufacturing system with the flexibility of flexible manufacturing systems. At the heart of RMS lies the Reconfigurable Machine Tools which are capable of performing multiple operations in its existing configurations and can further be reconfigured into more configurations which makes the configuration selection an arduous task. In the present research work the design of single part reconfigurable flow line has been attempted considering multiple objectives i.e. cost and machine utilization. A methodology is proposed for multiple objective optimization of RMS configuration based on machine utilization and cost by applying Multiple Objective Particle Swarm Optimization (MOPSO). A case study has been taken to illustrate the developed approach of flow line optimization applying MOPSO. (Received in February 2012, accepted in August 2012. This paper was with the authors 1 month for 1 revision.)

[1]  Paolo Pedrazzoli,et al.  Flexibility and reconfigurability: An analytical approach and some examples , 2001 .

[2]  Jianping Dou,et al.  Graph theory-based approach to optimize single-product flow-line configurations of RMS , 2009 .

[3]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[4]  Li Tang,,et al.  Concurrent Line-Balancing, Equipment Selection and Throughput Analysis for Multi-Part Optimal Line Design , 2004 .

[5]  P. K. Jain,et al.  A model and optimisation approach for reconfigurable manufacturing system configuration design , 2012 .

[6]  Yoram Koren,et al.  Convertibility Measures for Manufacturing Systems , 2003 .

[7]  Tomislav Šarić,et al.  Adaptive fuzzy particle swarm optimization for flow-shop scheduling problem , 2012 .

[8]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[9]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[10]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[11]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[12]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[13]  F. Musharavati.  RECONFIGURABLE MANUFACTURING SYSTEMS , 2010 .

[14]  Hoda A. ElMaraghy,et al.  Modelling and optimization of multiple-aspect RMS configurations , 2006 .

[15]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[16]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[17]  A. G. U Lso,et al.  Reconfigurable manufacturing systems: Key to future manufacturing , 2000 .

[18]  Hoda A. ElMaraghy,et al.  Optimal configuration selection for Reconfigurable Manufacturing Systems , 2007 .

[19]  Lyes Benyoucef,et al.  Developing a reconfigurability index using multi-attribute utility theory , 2011 .

[20]  P. K. Jain,et al.  Cell formation in the presence of reconfigurable machines , 2007 .

[21]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[22]  Xiaodong Li,et al.  A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization , 2003, GECCO.

[23]  J. Rowe,et al.  Particle SwarmOptimization andFitness Sharing tosolve Multi-Objective Optimization Problems , 2005 .

[24]  Madhu Jain,et al.  Optimal configuration selection for reconfigurable manufacturing system using NSGA II and TOPSIS , 2012 .