Intelligent selective disassembly planning based on disassemblability characteristics of product components

Soran Parsa, Mozafar Saadat

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
260 Downloads (Pure)

Abstract

Many studies have used different optimisation methods to find a near-optimal solution by optimising the disassembly operations sequence. These studies have used disassembly operation time as the main optimisation parameter, and other parameters such as direction change or tool change are converted to time scale. In order to determine accurate operation time, a product needs to be completely disassembled, noting that the same EOL products can be in a different condition and result in different operation time. In this work, new optimisation parameters based on the disassemblability and components demand are defined. These include Disassembly Handling Index (DHI), Disassembly Operation Index (DOI) and Disassembly Demand Index (DDI). In order to consider the operation time and other costs, Disassembly Cost Index (DCI) is further defined. Genetic algorithm optimisation method was employed to optimise the process sequence. Here, the most demanded components with the easiest disassembly operations are disassembled first without requiring to disassemble the unwanted components and avoid complicated operations. Two case studies were analysed to determine the effectiveness and compatibility of this method. The result shows 13% and 10% improvement in overall disassembly time for the case studies.
Original languageEnglish
Pages (from-to)1769-1783
Number of pages15
JournalThe International Journal of Advanced Manufacturing Technology
Volume104
Issue number5-8
Early online date18 Jun 2019
DOIs
Publication statusPublished - 1 Oct 2019

Keywords

  • Remanufacturing
  • Disassembly sequence planning
  • Multi-objective planning
  • Intelligent optimisation
  • Genetic algorithm
  • Disassemblability

Fingerprint

Dive into the research topics of 'Intelligent selective disassembly planning based on disassemblability characteristics of product components'. Together they form a unique fingerprint.

Cite this