Carbon deterministic decision-making for remanufacturing systems under uncertainty

The emergent shift of the world manufacturing industry towards a circular economy has made remanufacturing become an important engineering technology that will ensure resources circulation and carbon emission reduction. The core operational challenges of remanufacturing systems stem from the deep uncertainty and heterogeneity of the quality of end-of-life parts. To address this, we propose a Carbon Deterministic Decision-Making method for remanufacturing systems under uncertainty. Its core innovation is constructing a Carbon-Internally-Taguchi net benefit function, which internalizes quality loss as implicit carbon cost via an economic carbon intensity, thereby enabling the automated and synergistic optimization of quality, cost, and carbon emissions under a unified metric. Based on this, we establish a deterministic mapping from real-time quality measurements to optimal process decisions and design an efficient solution algorithm. A case study on remanufacturing shows that CDDM reduces process carbon input by 38.0% and improves product reliability by 15-25% compared to traditional empirical decision-making. Theoretically, this study develops a strict optimization model to handle uncertainty in remanufacturing systems. Practically, it offers a mathematical-based and engineering feasible way to implement carbon neutrality goals into production decision-making in real-time.