A Decision Tree-Based Method for Evaluating the Remanufacturability of Used Parts

Assessing the remanufacturability of used parts is a crucial basis for determining their value and optimal utilization methods. Due to the uncertain quality of used parts and the varying processing capacity of enterprises, coupled with the continuous expansion of the scale of the remanufacturing industry, the traditional weighted-analysis model, which considers all indicators at the same level, is inefficient for decision-making. In order to evaluate the remanufacturability of used parts more efficiently, a decision tree-based method is proposed, which hierarchically processes the evaluation criteria to enhance decision-making efficiency and adaptability. First, using a data platform, the remaining value of used parts reflected in the failure degree is analyzed and predicted, with the aid of artificial neural networks and the Weibull model, providing an initial remanufacturability assessment. Then, remanufacturability is assessed sequentially from the technical, economic, and environmental feasibility aspects, based on the enterprise’s processing capabilities. Finally, the effectiveness of the proposed method is validated through a case study on the remanufacturing of used blades.