Systematic Review of Data Modeling Methods for System-Level Disassembly Planning and Control: Literature Data

Disassembly is crucial in recovering complex end-of-life products in a circular economy, e.g., before remanufacturing or recycling. Systematic disassembly planning and control involves challenging decision making due to the inherent complexity and uncertainty of disassembly, such as unknown product conditions. Research addresses this challenge with data-driven approaches based on data models, such as mathematical programming, machine learning, simulation, and digital twins. However, existing approaches and reviews do not provide a comprehensive view of data models across the different fields of disassembly planning and control but rather focus on specific fields. This narrow scope limits the compatibility and applicability of the approaches, which must build upon each other to incrementally refine a disassembly plan in practice. This article addresses the lack of a comprehensive view by providing a systematic review of data modeling methods for disassembly line balancing, disassembly sequence planning, disassembly scheduling, disassembly process planning, and reactive disassembly planning based on 376 publications. A structured catalog is presented, which includes 83 data modeling methods based on, e.g., graphs, matrices and Petri nets. The current application of the identified methods is analyzed, providing insights into the most commonly used methods by research fields. Finally, the methods are compared in terms of the types of information they cover, considering product, process, resource, and order information. The results provide a shared foundation for data model design across all research fields involved in system-level disassembly planning and control. They allow researchers to identify, select, and combine suitable data modeling methods for their specific use case while considering the big picture of the research fields.