Designing resilient remanufacturing strategies: A probabilistic framework for uncertainty-aware decision making

Abstract

Remanufacturing is a vital strategy for advancing sustainability and resource efficiency across diverse industries. However, developing effective remanufacturing plans remains challenging due to inherent uncertainties, such as varying conditions of returned products and fluctuating market demands. Previous studies have a limitation in reflecting these uncertainties, as they rely on deterministic inputs in optimization models. This study proposes a new framework-referred to as Probabilistic Green Profit Design (PrGPD) model-that explicitly incorporates uncertainties into remanufacturing optimization. The model assigns probability density functions to key parameters based on expert knowledge and empirical data, enabling stochastic analysis through synthetic data generation. Furthermore, the framework identifies critical factors affecting remanufacturing outcomes by analyzing constraint activities under varying conditions. The proposed approach was validated through a case study involving a recreational boat engine. The results showed that improvements in the quality of disassembled parts directly enhance both profitability and carbon reduction, justifying the targeted investments in Design for Remanufacturing (DfR), early-return programs, and quality inspection systems. Additionally, the analysis revealed that profit margins remain stable across different market conditions, indicating that green profit opportunities are robust even under demand fluctuations. Overall, this research develops a practical and adaptable decision-support tool for remanufacturing businesses, providing more resilient strategies than traditional deterministic approaches.