The transition toward circular maintenance systems requires decision-support tools capable of evaluating reconditioned spare parts across multiple and often conflicting technical, economic, and sustainability criteria. Although reconditioned components can reduce cost, resource consumption, and environmental burden, their industrial adoption remains limited due to uncertainty in quality, reliability, and long-term value, as well as the lack of transparent selection frameworks. This study proposes a scenario- and stability-aware decision framework for reconditioned spare parts selection in circular maintenance systems. The framework integrates six evaluation criteria (cost, reliability, environmental impact, circular economy contribution, quality, and future remanufacturing value) within a weighted composite objective optimized using a genetic algorithm. To support controlled validation and reproducibility, the framework is assessed through a simulated industrial case study based on an industry-inspired dataset of reconditioned spare parts. In addition to optimization, the study formalizes a scenario- and stability-oriented evaluation protocol to represent different stakeholder priorities and to assess outcome consistency under parameter variation. The study reports trade-off-oriented results through visualization and statistical analyses, including scatter plots, correlation analysis, and performance distributions. These elements provide a methodological basis for expanded comparative analysis in future studies. The findings are intended to support transparent and practical decision-making in circular maintenance planning while providing a reproducible basis for future integration with industrial datasets and digital maintenance systems.
Mansouri et al. (Fri,) studied this question.