Solving the assembly configuration problem for modular products using an immune algorithm approach

Designing for modularity enhances the agility of a manufacturing system. It allows manufacturing systems to be built under high product customization requirements and at the same time keeping product development time low. Modular products imply in themselves a complex multiproduct assembly line design challenge. The design of such assembly systems comprises some of the most challenging computations in engineering and mathematics. More often than not, the optimal solution to the problem could not be found. This paper describes an approach based on the principles of natural immune systems for solving the design of assembly system for modular products. The approach, which was based on immune algorithm, was illustrated using an assembly combinatorial problem and the results obtained were compared with those derived by an established heuristic algorithm, the genetic algorithm. The results show that the immune algorithm outperforms the genetic algorithm in terms of convergence trends, distribution of near-optimal solutions and quality of solutions.

[1]  Andrew Kusiak,et al.  Design of assembly systems for modular products , 1997, IEEE Trans. Robotics Autom..

[2]  D. Dasgupta.  Artificial Immune Systems and Their Applications , 1998, Springer Berlin Heidelberg.

[3]  Xuehong Du,et al.  Architecture of product family for mass customization , 2000, Proceedings of the 2000 IEEE International Conference on Management of Innovation and Technology. ICMIT 2000. 'Management in the 21st Century' (Cat. No.00EX457).

[4]  Leandro Nunes de Castro,et al.  The Clonal Selection Algorithm with Engineering Applications 1 , 2000 .

[5]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[6]  Dipankar Dasgupta,et al.  An Overview of Artificial Immune Systems and Their Applications , 1993 .

[7]  Peter Gould.  What is agility? [agile manufacturing] , 1997 .

[8]  Kazuyuki Mori,et al.  Adaptive scheduling system inspired by immune system , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[9]  R. Nagel,et al.  Agile competitors and virtual organisations , 1995 .

[10]  J. Doyne Farmer,et al.  A Rosetta stone for connectionism , 1990 .

[11]  Koji Yamada,et al.  Immune algorithm for n-TSP , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[12]  L. P. Khoo,et al.  A Tabu-Enhanced Genetic Algorithm Approach to Agile Manufacturing , 2002 .

[13]  Philippe Collard,et al.  Two models of immunization for time dependent optimization , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.