Optimization of end-of-use decision-making

Abstract Remanufacturing and recycling are pivotal strategies for closing material loops, yet remanufacturers rarely evaluate in-house dismantling as a viable alternative to selling components at market price, largely due to the absence of standardized recycling processes and adequate decision-support tools. This paper develops a quantitative optimization model for end-of-use (EOU) decision-making that determines, at the component level, whether remanufacturing or recycling maximizes economic and environmental profitability. For the recycling branch, we derive a closed-form expression for the optimal dismantling time by modeling material purity as a saturating function of processing effort, balanced against linearly increasing labor and environmental costs. The model is further extended to account for core quality heterogeneity through a state-of-health-dependent purity gain factor and a remanufacturing failure probability, both parametrized using low-effort data such as bill-of-materials weight fractions, handling complexity scoring, and market-based values. Applied to an industrial case study of an OEM remanufacturer of electric power steering systems, the model shows that remanufacturing dominates recycling for every component under current market conditions, a result robust to core quality variation, raw material price fluctuations, and favorable labor cost assumptions. However, the proposed model provides remanufacturers with a transferable, data-efficient tool to identify when, and under which market conditions, component-level recycling becomes a economically and environmentally viable decision to complement remanufacturing enterprises.