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Intelligent selective disassembly using the ant colony algorithm

Published online by Cambridge University Press:  01 November 2003

J.F. WANG ,
J.H. LIU ,
S.Q. LI  and
Y.F. ZHONG
J.F. WANG
Affiliation:
School of Mechanical Science and Engineering, Huazhong University of Science & Technology, Wuhan, People's Republic of China
J.H. LIU
Affiliation:
School of Mechanical Engineering and Automation, Beihang University, Beijing, People's Republic of China
S.Q. LI
Affiliation:
School of Mechanical Science and Engineering, Huazhong University of Science & Technology, Wuhan, People's Republic of China
Y.F. ZHONG
Affiliation:
School of Mechanical Science and Engineering, Huazhong University of Science & Technology, Wuhan, People's Republic of China
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Abstract

Selective disassembly is an important issue in industrial and mechanical engineering for environmentally conscious manufacturing. This paper presents an intelligent selective disassembly approach based on ant colony algorithms, which take inspiration from the behavior of real ant colonies and are used to solve combinatorial optimization problems. For diverse assemblies, the algorithm generates different amounts of ants cooperating to find disassembly sequences for selected components, minimizing the reorientation of assemblies and removal of components. A candidate list that is composed of feasible disassembly operations, which are derived from a disassembly matrix of products, guides sequence construction in the implicit solution space and ensures the geometric feasibility of sequences. Preliminary implementation results show the effectiveness of the proposed method.

Keywords

Ant Colony OptimizationDisassembly MatrixDisassembly Sequence PlanningSelective Disassembly
Type
Research Article
Information
AI EDAM , Volume 17 , Issue 4 , November 2003 , pp. 325 - 333
Copyright
2003 Cambridge University Press

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References

REFERENCES

Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. Oxford: Oxford University Press.
Boothroyd, G., Dewhurst, P., & Knight, W. (1994). Product Design for Manufacture and Assembly. New York: Marcel Dekker.
De Fazio, T.L. & Whitney, D.E. (1987). Simplified generation of all mechanical assembly sequences. IEEE Journal of Robotics and Automation, RA-3(6), 640–658.CrossRefGoogle Scholar
Dini, G. & Santochi, M. (1992). Automated sequencing and subassembly detection in assembly planning. Annals of the CIRP, 41(1), 1–4.CrossRefGoogle Scholar
Dorigo, M. (1992). Optimization, learning and natural algorithms. PhD Thesis. Politecnico di Milano, Italy.
Dorigo, M. & Gambardella, L.M. (1997). Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1), 53–66.CrossRefGoogle Scholar
Dorigo, M., Maniezzo, V., & Colorni, A. (1996). The ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics Part B, 26(1), 29–41.CrossRefGoogle Scholar
Dutta, D. & Woo, T.C. (1995). Algorithm for multiple disassembly and parallel assemblies. ASME Journal of Engineering for Industry, 117(1), 102–109.CrossRefGoogle Scholar
Failli, F. & Dini, G. (2000). Ant colony systems in assembly planning: a new approach to sequence detection and optimization. Proc. Second CIRP Int. Seminar on Intelligent Computation Manufacturing Engineering, pp. 227–232.
Garcia, M.A., Larre, A., Lopez, B., & Oller, A. (2000). Reducing the complexity of geometric selective disassembly. Proc. IEEE/RSJ Int. Conf. Intelligent Robots Systems, pp. 1474–1479.CrossRef
Gungor, A. & Gupta, S.M. (1999). Issues in environmentally conscious manufacturing and product recovery: a survey. Computers & Industrial Engineering, 36(4), 811–853.Google Scholar
Homem De Mello, L.S. & Sanderson, A.C. (1991). A correct and complete algorithm for the generation of mechanical assembly sequences. IEEE Transactions on Robotics and Automation, 7(2), 228–240.CrossRefGoogle Scholar
Huang, H.H.T., Wang, M.H., & Johnson, M.R. (2000). Disassembly sequence generation using a neural network approach. Journal of Manufacturing Systems, 19(2), 73–82.CrossRefGoogle Scholar
Huang, Y.M. & Huang, C.T. (2002). Disassembly matrix for disassembly processes of products. International Journal of Production Research, 40(2), 255–273.CrossRefGoogle Scholar
Jones, R.E., Wilson, R.H., & Calton, T.L. (1997). Constraint based interactive assembly planning. IEEE Proc. Int. Conf. Robotics Automation, pp. 913–920.CrossRef
Kuo, T.C., Zhang, H.C., & Huang, H.S. (2000). Disassembly analysis for electromechanical products: a graph-based heuristic approach. International Journal of Production Research, 38(5), 993–1007.CrossRefGoogle Scholar
Lambert, A.J.D. (2002). Determining optimum disassembly sequences in electronic equipment. Computers & Industrial Engineering, 43(3), 553–575.CrossRefGoogle Scholar
Li, J.R., Khoo, L.P., & Tor, S.B. (2002). A novel representation scheme for disassembly sequence planning. International Journal of Advanced Manufacturing Technology, 20(8), 621–630.CrossRefGoogle Scholar
McMullen, P.R. (2001). An ant colony optimization approach to addressing a JIT sequencing problem with multiple objectives. Artificial Intelligence in Engineering, 15(3), 309–307.CrossRefGoogle Scholar
Seo, K.K., Park, J.H., & Jang, D.S. (2001). Optimal disassembly sequence using genetic algorithms considering economic and environmental aspects. International Journal of Advanced Manufacturing Technology, 18(5), 371–380.CrossRefGoogle Scholar
Srinivasan, H., Figueroa, R., & Gadh, R. (1999). Selective disassembly for virtual prototyping as applied to de-manufacturing. Robotics and Computer Integrated Manufacturing, 15(3), 231–245.CrossRefGoogle Scholar
Srinivasan, H. & Gadh, R. (1998). A geometric algorithm for single selective disassembly using the wave propagation abstraction. Computer Aided Design, 30(8), 603–613.CrossRefGoogle Scholar
Srinivasan, H. & Gadh, R. (2000). Efficient disassembly of multiple components from an assembly using wave propagation. ASME Journal of Mechanical Design, 122(2), 179–184.CrossRefGoogle Scholar
Srinivasan, H. & Gadh, R. (2002). A non-interfering selective disassembly sequence for components with geometric constraints. IIE Transactions, 34(4), 349–361.CrossRefGoogle Scholar
Srinivasan, H., Shyamsundar, N., & Gadh, R. (1997). A framework for virtual disassembly analysis. Journal of Intelligent Manufacturing, 8(4), 277–295.CrossRefGoogle Scholar
T'kindt, V., Monmarche, N., Tercinet, F., & & Laught, D. (2002). An ant colony optimization algorithm to solve a 2-machine bicriteria flowshop scheduling problem. European Journal of Operational Research, 142(2), 250–257.CrossRefGoogle Scholar
Watkins, R.D., Kleban, S.D., & Luger, G.F. (1995). Expert system support for environmentally conscious manufacturing. International Journal of Environmentally Conscious Design & Manufacturing, 4(2), 53–66.Google Scholar
Woo, T.C. & Dutta, D. (1991). Automatic disassembly and total ordering in three dimensions. ASME Journal of Engineering for Industry, 113(1), 207–213.CrossRefGoogle Scholar
Zeid, I., Gupta, S.M., & Bardasz, T. (1997). A case-based reasoning approach to planning for disassembly. Journal of Intelligent Manufacturing, 8(2), 97–106.CrossRefGoogle Scholar
Zha, X.F. & Lim, S.Y.E. (2000). Assembly/disassembly task planning and simulation using expert Petri Nets. International Journal of Production Research, 38(15), 3639–3676.CrossRefGoogle Scholar
Zussman, E. & Zhou, M.C. (1999). A methodology for modeling and adaptive planning of disassembly processes. IEEE Transactions of Robotics and Automation, 15(1), 190–194.CrossRefGoogle Scholar