A Smart Multi-Objective Optimization Framework for Sustainable Supplier Selection in Renewable Energy Equipment Supply Chain

The renewable energy sector faces critical challenges in establishing reliable supply chains for essential equipment components. This research develops an integrated decision support framework for supplier evaluation and order distribution in solar panel component procurement. Unlike existing approaches, this study simultaneously optimizes three fundamental business dimensions: economic efficiency, product reliability, and delivery performance, while incorporating regulatory compliance verification through government-certified testing facilities. The proposed multi-period multi-objective renewable energy supply chain management model addresses the practical needs of emerging renewable energy enterprises that lack comprehensive testing infrastructure. A hybrid solution methodology combining interactive fuzzy programming with weighted goal programming is employed to handle inherent uncertainties in supplier performance data. The framework integrates expert committee judgments weighted by individual experience levels to prioritize conflicting objectives. Computational experiments demonstrate achievement of 48.32% satisfaction for cost minimization, 77.89% for quality maximization, and 93.67% for time reduction objectives. Sensitivity analyses confirm the model’s robustness under varying decision-maker preferences and uncertainty levels. This research contributes a comprehensive decision framework for renewable energy supply chain management, offering direct applicability to industries requiring regulatory oversight in supplier qualification processes.