Energy-aware three-stage remanufacturing scheduling considering asymmetric uncertainty: Fuzzy modeling and solution method

Remanufacturing scheduling is a critical path to sustainability. However, existing research on the three-stage remanufacturing system scheduling problem (3T-RSSP) is predominantly concentrated in the deterministic domain; the few studies involving uncertainty mainly employ stochastic programming or symmetric fuzzy sets, neglecting the asymmetric uncertainty inherent in remanufacturing. This paper investigates the bi-objective energy-aware 3T-RSSP under asymmetric uncertainty, integrating disassembly, reprocessing, and reassembly. First, a fuzzy mathematical model is formulated to minimize makespan and energy consumption; by quantifying aggregate asymmetric uncertainty via a Right-Skewed Perturbation Model based on Triangular Fuzzy Numbers, it captures operational delay characteristics to accurately map real-world scenarios. Second, an Uncertainty-Aware Scheduling Strategy (UASS) is proposed to preserve the stability of the scheduling scheme. By treating tasks with similar expected processing times as indistinguishable ones and using the Coefficient of Variation as a tie-breaking criterion, UASS implements a risk postponement mechanism to shift high-volatility operations to later stages. Third, an Improved Hybrid Guided Whale Migration Algorithm (HGWMA) is proposed, incorporating AGALF and PAVM strategies to balance exploration and exploitation. Finally, comprehensive simulations across varying scales evaluate HGWMA against other algorithms. Compared with NSGA-II, HGWMA achieves a 12.8% average Hypervolume improvement and significantly reduces Inverted Generational Distance, especially in large-scale instances. Results demonstrate the feasibility and superiority of the proposed framework in addressing the energy-aware scheduling problem under asymmetric uncertainty.