Sustainable feedstocks selection and renewable products allocation: A new hybrid adaptive utility-based consensus...

Area of Science:

• Renewable Energy Systems
• Decision Support Systems
• Fuzzy Set Theory

Background:

• Optimizing renewable energy production requires careful selection of sustainable feedstocks.
• Market change management necessitates efficient allocation of renewable products based on feedstock availability.
• Existing methods often struggle to adequately model the uncertainty inherent in feedstock evaluation and demand forecasting.

Purpose of the Study:

• To propose a hybrid adaptive framework for selecting sustainable feedstocks for renewable energy.
• To develop a method for optimum renewable product allocation considering demand preferences.
• To address uncertainty in decision-making using dynamic hesitant fuzzy sets.

Main Methods:

• A consensus evaluation approach with feedback mechanisms for feedstock quality assessment.
• A dynamic hesitant fuzzy entropy method for criterion weighting.
• Ranking feedstocks using dynamic hesitant fuzzy positive and negative ideal solutions.
• A revised multi-choice goal programming model with dynamic hesitant fuzzy closeness indexes for demand assignment.

Main Results:

• The proposed framework effectively models uncertainty and incorporates expert weights.
• Comparative analysis demonstrates advantages over existing methods in adaptability and consensus building.
• Sensitivity analysis confirms the robustness of ranking results, highlighting the importance of sustainability criteria.

Conclusions:

• The hybrid adaptive framework provides a robust approach for sustainable feedstock selection and renewable product allocation.
• The methodology enhances decision-making by effectively managing uncertainty and expert opinions.
• The study offers a valuable tool for market change management in the renewable energy sector.