Circularity triage: A multi-stage adaptive model for end-of-life products

Determining appropriate recovery pathways for end-of-life (EoL) products is challenging due to limited information on their highly variable physical conditions and residual values. Existing recovery systems are highly dependent on static procedures, which hinder adaptive pathway selection and limit profitability. To address this limitation, this study proposes a multi-stage triage model inspired by medical triage protocols. This model formulates EoL recovery as a sequential decision process in three hierarchical stages: preliminary, product-level, and component-level triage. To drive this adaptive decision-making, the model uses a digital twin (DT) during the preliminary and product-level stages for structured state mapping and dynamic information updating. At the component-level stage, a knowledge graph (KG) is used to represent structural relationships and precedence constraints, supporting structurally feasible disassembly planning. The model incorporates progressively acquired evidence across all stages using fuzzy Bayesian updating. To evaluate the profitability of continued disassembly, the model embeds a partially observable Markov decision process (POMDP) with Bellman recursion, enabling sequential decision-making and optimal stopping under uncertainty. A gearbox case study involving four condition–uncertainty scenarios demonstrates that this dynamic, evidence-driven model adaptively updates recovery pathways and improves the resulting net recovery benefit.