Optimization of Three-Dimensional Disassembly Line with Green and Economic Objectives Using I-ACO

Optimization of Three-Dimensional Disassembly Line with Green and Economic Objectives Using I-ACO

Yanggang Teng* Jinjie Chen Ting Chen Guilin Ge

University of Shanghai for Science and Technology, No. 516, Jungong Road, Yangpu, Shanghai

Changzhou University, No.21, Gehu Middle Road, Wujin, Changzhou

Corresponding Author Email: 
hawk_teng@163.com
Page: 
91-101
|
DOI: 
https://doi.org/10.18280/ama_c.730304
Received: 
25 July 2018
|
Accepted: 
28 August 2018
|
Published: 
30 September 2018
| Citation

OPEN ACCESS

Abstract: 

Although the problem of disassembly line balance has been studied in depth, there are fewer starting points from green, economy and position as the basis for path planning. This paper considers the disassembly path from the three perspective. For green perspective, even if the product doesn’t have the economic value, the toxic solid and liquid must be excluded; For economic, the re-utilize value of the disassembled object is fewer, the complete disassembly will abandon; Furthermore, the three-dimensional information is difficult to obtain. In this paper, the reducer drawn by Solidworks to obtain the position and mass, and then the improved R-AOG (Rectangle-AND/OR Graph) used to represent the directional relationship of large-scale component disassembly. And use R-AOG de-sign Allowedk, Lastly, compare I-ACO with other ACO to prove the efficiency and quality, and ap-plies it to the reducer disassembly planning to obtain the optimal disassembly path, these experimental results help company to better plan the disassembly line, reduce the maintenance threshold of the reducer, and also beneficial to build automated disassembly lines.

Keywords: 

R-AOG, three-dimension disassembly line, dynamic table update, green and economic disassembly, I-ACO

1. Introduction
2. Research Problem and Method
3. Improved I-ACO for DLBP
4. Experimental and Disscussions
5. Conclusions and Future Work
Acknowledgements

This work was financially supported by Chinese University Students Innovation Fund Project (201411585002Y).

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