Research Article Open Access

A Design Framework for End-of-Life Vehicles Recovery: Optimization of Disassembly Sequence Using Genetic Algorithms

T. F. Go, D. A. Wahab, M.N. Ab. Rahman and R. Ramli

Abstract

Problem statement: It is expected that over the next few years type approval legislation and public awareness will force vehicle manufacturers to identify recovery methods during the design process in order to achieve reuse and recycling targets. Current vehicle design in Malaysia does not sufficiently aid the economic recovery of parts and materials to reach these targets. Approach: This study aimed to provide a framework for automotive components to be designed for ease of recovery. Disassemblability concept evolved from the life cycle engineering concept in which design for disassembly is one of the strategies in reducing the impact of the product to the environment. Results: The proposed methodology that consisted of three distinct elements namely implementing principles and guidelines of design for disassembly into the design, generating optimum disassembly using genetic algorithm approach and evaluating disassemblability of end-of-life products will be discussed. Conclusion/Recommendations: There is a need for effective disassembly in order to enhance the recovery of end-of-life product.The proposed methodology was implemented as a computer-based disassemblability evaluation tool that will enhance disassemblability of the product starting from the design stage.

American Journal of Environmental Sciences
Volume 6 No. 4, 2010, 350-356

DOI: https://doi.org/10.3844/ajessp.2010.350.356

Submitted On: 9 August 2010 Published On: 31 August 2010

How to Cite: Go, T. F., Wahab, D. A., Rahman, M. A. & Ramli, R. (2010). A Design Framework for End-of-Life Vehicles Recovery: Optimization of Disassembly Sequence Using Genetic Algorithms. American Journal of Environmental Sciences, 6(4), 350-356. https://doi.org/10.3844/ajessp.2010.350.356

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Keywords

  • Recovery
  • design for disassembly
  • optimal disassembly sequence
  • genetic algorithm