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IJAT Vol.12 No.6 pp. 822-832
doi: 10.20965/ijat.2018.p0822
(2018)

Paper:

Disassembly Reuse Part Selection for Recovery Rate and Cost with Lifetime Analysis

Shota Hasegawa*, Yuki Kinoshita*, Tetsuo Yamada*,†, Masato Inoue**, and Stefan Bracke***

*Department of Informatics, The University of Electro-Communications
1-5-1 Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan

Corresponding author

**Department of Mechanical Engineering Informatics, Meiji University, Kawasaki, Japan

***Chair of Reliability Engineering and Risk Analytics, University of Wuppertal, Wuppertal, Germany

Received:
April 28, 2018
Accepted:
August 31, 2018
Published:
November 5, 2018
Keywords:
life expectancy, recyclability evaluation method, straight line depreciation method, ϵ constraint method, integer programming
Abstract

The depletion of natural resources is a critical environmental issue, and the recovery, including reuse and recycling, of end-of-life assembled products is the key to reducing the use of natural resources. However, in order to reuse or recycle an assembled product, it is essential to consider the life expectancy or material type and the weight of the parts in the product. In addition, because the assembled products comprise various parts, manual disassembly is required, which entails high costs. To recover assembled products in an environmentally friendly and economical manner, part selection for disassembly is required. A part selection method is proposed with three selection types: reuse, recycling, and disposal. First, data-set preparation is addressed. Second, the method for selecting the disassembly parts using integer programming and the ϵ constraint method is explained. Finally, numerical experiments are conducted using the proposed part selection method with a computer as a case study. Lifetime changes of the parts/product are then analyzed.

Cite this article as:
S. Hasegawa, Y. Kinoshita, T. Yamada, M. Inoue, and S. Bracke, “Disassembly Reuse Part Selection for Recovery Rate and Cost with Lifetime Analysis,” Int. J. Automation Technol., Vol.12, No.6, pp. 822-832, 2018.
Data files:
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Last updated on Dec. 11, 2018