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Mathematical Problems in Engineering
Volume 2013, Article ID 173736, 8 pages
Research Article

Efficiency Optimization for Disassembly Tools via Using NN-GA Approach

1Transportation College, Northeast Forestry University, Harbin 150040, China
2Transportation College, Jilin University, Changchun 130020, China
3Business College, Shandong University of Technology, Zibo 250012, China

Received 16 September 2013; Accepted 29 October 2013

Academic Editor: Shuping He

Copyright © 2013 Guangdong Tian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Disassembly issues have been widely attracted in today’s sustainable development context. One of them is the selection of disassembly tools and their efficiency comparison. To deal with such issue, taking the bolt as a removal object, this work designs their removal experiments for different removal tools considering some factors influencing its removal process. Moreover, based on the obtained experimental data, the removal efficiency for different removal tools is optimized by a hybrid algorithm integrating neural networks (NN) and genetic algorithm (GA). Their efficiency comparison is discussed. Some numerical examples are given to illustrate the proposed idea and the effectiveness of the proposed methods.