Product Disassembly Planning Using Design For Disassembly and Genetic Algorithm
This paper introduces the use of an Artificial-Intelligence (AI) based technique, Genetic Algorithm (GA), to solve single model product disassembly sequence problems. The generation of disassembly sequence is modeled using Design for Assembly (DfA) working principles. In this paper, the performances...
| 第一著者: | |
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| フォーマット: | 学位論文 |
| 言語: | 英語 英語 |
| 出版事項: |
2006
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| 主題: | |
| オンライン･アクセス: | https://etd.uum.edu.my/23/1/lau_lee_lyn.pdf https://etd.uum.edu.my/23/2/lau_lee_lyn.pdf |
| 要約: | This paper introduces the use of an Artificial-Intelligence (AI) based technique, Genetic Algorithm (GA), to solve single model product disassembly sequence problems. The generation of disassembly sequence is modeled using Design for Assembly (DfA) working principles. In this paper, the performances of Design for
Disassembly (DfD) and GA in selecting optimum disassembly sequence were tested. The problem is involves minimizing the total disassembly time by proper feeder allocation and component sequencing. The objective is to find out the optimum disassembly sequence with minimum disassembly time. The study started by manual disassembly using DfD which involves manual handling and manual insertion
guideline in estimating time to search for optimum sequence. Finally, GA technique is applied to search for the optimum sequence. The results were compared between DfD
and GA to show the efficiency of the proposed GA approach. |
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