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

Nowadays, preferred compromise response of renewable energies' demands regarding the candidate sustainable feedstocks is a crucial issue for market change management. Thus, selecting the most suitable sustainable feedstock is a key factor for optimum renewable products allocation problem. To address the issue, this study proposes a hybrid adaptive framework based on consensus evaluation approach, weighting and ranking procedure, and preferred demand assignment under dynamic hesitant fuzzy sets. In this respect, the consensus evaluation approach is tailored regarding the direct and indirect feedback mechanisms to enhance the quality evaluation of candidate sustainable feedstocks under assessment criteria. Thereby, the weight of each criterion is determined based on the developed dynamic hesitant fuzzy entropy method and the candidate sustainable feedstocks are ranked with respect to developed dynamic hesitant fuzzy positive and negative ideal solutions. Then, a revised multi-choice goal programming model is extended regarding the dynamic hesitant fuzzy closeness indexes to attend to preferred compromise response of demand centers by optimum renewable products allocation. Meanwhile, the presented hybrid adaptive framework is implemented to a real case study to represent the applicability and efficiency of the proposed approach. Furthermore, a comparative analysis is provided by defining eight comparison indexes to compare the obtained results with two recent studies in relevant literature for representing the validation and verification of the proposed approach. The comparative analysis shows that the proposed approach versus the two other approaches has merits such as modeling of uncertainty, experts' weights, adaptive structure, unanimous agreement-based approach, and last aggregation framework. Finally, a sensitivity analysis is represented to show the sensitiveness and robustness of the obtained results from changing the criteria weights, goals values, and consensus elimination. Thereby, the sensitivity analysis indicates that the obtained ranking results are sensitive to sustainability criteria unlike the technical criterion.