TY - JOUR
T1 - Intelligent selective disassembly planning based on disassemblability characteristics of product components
AU - Parsa, Soran
AU - Saadat, Mozafar
N1 - Funding Information:
The authors would like to thank EPSRC for its support of this research, which was carried out as part of AUTOREMAN project (grant number EP/N018524/1), and Reco Turbo Ltd. for supplying the case study product.
Publisher Copyright:
© 2019, The Author(s).
PY - 2019/10/1
Y1 - 2019/10/1
N2 - 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.
AB - 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.
KW - Disassemblability
KW - Disassembly sequence planning
KW - Genetic algorithm
KW - Intelligent optimisation
KW - Multi-objective planning
KW - Remanufacturing
UR - http://www.scopus.com/inward/record.url?scp=85067824724&partnerID=8YFLogxK
U2 - 10.1007/s00170-019-03857-1
DO - 10.1007/s00170-019-03857-1
M3 - Article
AN - SCOPUS:85067824724
VL - 104
SP - 1769
EP - 1783
JO - International Journal of Advanced Manufacturing Technology
JF - International Journal of Advanced Manufacturing Technology
SN - 0268-3768
IS - 5-8
ER -