A robust possibilistic multi-objective optimization framework for cloud-based remanufacturing

Cloud-based remanufacturing systems face major challenges due to the uncertain quality of returned components, the geographic dispersion of suppliers, and the simultaneous need to optimize conflicting objectives, such as time, cost, and energy. These uncertainties significantly affect scheduling efficiency and decision reliability in modern closed-loop manufacturing environments. To address these challenges, this study develops a robust possibilistic multi-objective optimization framework that integrates fuzzy logic, possibility theory, and robust programming within a unified decision model. Unlike previous approaches that either treat uncertainty probabilistically or ignore robustness in fuzzy formulations, the proposed method ensures both feasibility and optimality robustness under epistemic uncertainty. Uncertain parameters, processing time, operational cost, and energy consumption are represented by trapezoidal fuzzy numbers and systematically transformed into deterministic equivalents through a necessity-based approach. Four control parameters (α, γ, θ, δ) are introduced to flexibly adjust confidence levels and conservativeness, allowing decision-makers to balance reliability and performance. Comprehensive numerical experiments using GAMS software demonstrate the model’s superior performance compared with traditional deterministic and fuzzy-only optimization methods. The robust possibilistic model achieves a ۱۴.۳% reduction in total cost, an ۱۱.۶% reduction in energy consumption, and a ۹.۸% decrease in overall completion time, while maintaining high stability across different uncertainty levels. These findings confirm the model’s capability to generate reliable, low-risk decisions in highly uncertain production environments. The proposed framework represents a novel integration of fuzzy and robust optimization concepts and serves as an intelligent decision-support tool for sustainable, low-carbon, and Industry ۴.۰-driven remanufacturing systems.

کلیدواژه ها:

Multi-Objective Optimization ، Possibilistic Robust Programming ، Cloud Remanufacturing ، Uncertainty