Rule-based recursive selective disassembly sequence planning for green design

Authors:
Shana S. Smith;Wei-Hsiang Chen
Affiliations:
Department of Mechanical Engineering, National Taiwan University, Taiwan, ROC;Department of Mechanical Engineering, National Taiwan University, Taiwan, ROC
Venue:
Advanced Engineering Informatics
Year:
2011

Citing 3
Cited 1

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Intelligent selective disassembly using the ant colony algorithm

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Research on evaluation methodologies of product life cycle engineering design (LCED) and development of its tools

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Disassembly sequence structure graphs: An optimal approach for multiple-target selective disassembly sequence planning

Advanced Engineering Informatics

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Abstract

Disassembly sequence planning not only reduces product lifecycle cost, but also greatly influences environmental impact. Many prior green design research studies have focused on complete disassembly of an end-of-life product to recover valuable components. However, complete disassembly is often not practical or cost effective if only a few components will be recovered and recycled from a given product. Selective disassembly sequence planning focuses on disassembling only one or more selected components from a product for reuse, recycling, remanufacturing, and maintenance. This paper presents a rule-based recursive method for finding a near-optimal heuristic selective disassembly sequence for green design. Most prior methods either enumerate all solutions or use stochastic random methods to generate solutions. Enumerative or stochastic methods often require tremendous computational resources while, at the same time, they often fail to find realistic or optimal solutions. On the contrary, the proposed method establishes certain heuristic disassembly rules to eliminate uncommon or unrealistic solutions. In addition, rather than considering geometric constraints for each pair of components, the developed method only considers geometric relationships between a part and its neighboring parts. As a result, the developed method can effectively find a near-optimal heuristic solution while greatly reducing computational time and space.