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Systematic review of data modelling methods for system-level disassembly planning and control.

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Title: Systematic review of data modelling methods for system-level disassembly planning and control.
Authors: Streibel, Lasse1 (AUTHOR) lasse.streibel@iwb.tum.de, Brunnenkant, Finn-Augustin1 (AUTHOR), Reinbold, Nora1 (AUTHOR), Lindholm, Niklas1 (AUTHOR), Jordan, Patrick1 (AUTHOR), Bluvstein, German1 (AUTHOR), Zaeh, Michael F.1 (AUTHOR), Reuter, Christina1 (AUTHOR)
Source: International Journal of Sustainable Engineering. Dec2025, Vol. 18 Issue 1, p1-41. 41p.
Subject Terms: Data modeling, Circular economy, Digital twin, Machine learning, Computer simulation, Mathematical programming
Abstract: Disassembly is crucial in recovering end-of-life products in a circular economy, e.g. before remanufacturing or recycling. Research addresses the challenges of complexity and uncertainty in disassembly planning and control with data-driven approaches based on data models, such as mathematical programming, machine learning, simulation, and digital twins. However, existing approaches consider data models only within their specific field of disassembly planning and control. Their narrow focus limits the compatibility and applicability of the approaches, which must build upon each other to incrementally refine a disassembly plan in practice. This review provides a comprehensive overview of data modelling methods for disassembly line balancing, disassembly sequence planning, disassembly scheduling, disassembly process planning, and reactive disassembly planning based on 376 publications. A structured catalogue is presented, including 83 data modelling methods based on, e.g. graphs, matrices, and Petri nets. Their application is analysed, showing how methods are used in different research fields. Finally, the scope of the methods is compared, considering product, process, resource, and order modelling. The results support the informed selection and combination of appropriate data modelling methods for data model design across all research fields involved in system-level disassembly planning and control. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Sustainable Engineering is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: GreenFILE
An: 190207104
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  Data: Systematic review of data modelling methods for system-level disassembly planning and control.
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  Data: <searchLink fieldCode="AR" term="%22Streibel%2C+Lasse%22">Streibel, Lasse</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lasse.streibel@iwb.tum.de</i><br /><searchLink fieldCode="AR" term="%22Brunnenkant%2C+Finn-Augustin%22">Brunnenkant, Finn-Augustin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Reinbold%2C+Nora%22">Reinbold, Nora</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lindholm%2C+Niklas%22">Lindholm, Niklas</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jordan%2C+Patrick%22">Jordan, Patrick</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bluvstein%2C+German%22">Bluvstein, German</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zaeh%2C+Michael+F%2E%22">Zaeh, Michael F.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Reuter%2C+Christina%22">Reuter, Christina</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Sustainable+Engineering%22">International Journal of Sustainable Engineering</searchLink>. Dec2025, Vol. 18 Issue 1, p1-41. 41p.
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  Data: <searchLink fieldCode="DE" term="%22Data+modeling%22">Data modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Circular+economy%22">Circular economy</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+twin%22">Digital twin</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+programming%22">Mathematical programming</searchLink>
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  Data: Disassembly is crucial in recovering end-of-life products in a circular economy, e.g. before remanufacturing or recycling. Research addresses the challenges of complexity and uncertainty in disassembly planning and control with data-driven approaches based on data models, such as mathematical programming, machine learning, simulation, and digital twins. However, existing approaches consider data models only within their specific field of disassembly planning and control. Their narrow focus limits the compatibility and applicability of the approaches, which must build upon each other to incrementally refine a disassembly plan in practice. This review provides a comprehensive overview of data modelling methods for disassembly line balancing, disassembly sequence planning, disassembly scheduling, disassembly process planning, and reactive disassembly planning based on 376 publications. A structured catalogue is presented, including 83 data modelling methods based on, e.g. graphs, matrices, and Petri nets. Their application is analysed, showing how methods are used in different research fields. Finally, the scope of the methods is compared, considering product, process, resource, and order modelling. The results support the informed selection and combination of appropriate data modelling methods for data model design across all research fields involved in system-level disassembly planning and control. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Sustainable Engineering is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Dec2025
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