Renewable energy-based sustainable oilseed selection problem: a new integrated group decision model under dynamic uncertainty

Energy demands worldwide have been rising for a while and will continue in recent years. Most of today's energy conditions are met by non-renewable energy sources, which contaminate the conditions and consume pretty rapidly. Renewable energy alternatives, i.e., biomass, can be regarded as inexpensive, reliable, secure, and sustainable energy for ever-increasing people. To choose the best renewable energy alternative to meet the rising energy needs, various elements, such as economic, social, and environmental, must be considered by decision-makers. Thus, this paper examines a new weighting method to compute the criteria weights and experts weights with a new integrated dynamic interval-valued hesitant fuzzy set (DIVHFS). The introduced decision-maker weighting method is based on the direct and indirect decision matrixes. Afterward, the criteria weights are computed using a new maximizing deviation method and the proposed entropy approach under DIVHFS conditions. Afterward, a new soft computing ranking method is proposed based on the positive and negative ideal solution values under DIVHFS to rank the main alternatives that are related to oilseeds products. A sensitivity analysis is discussed on experts' weights and criteria weights. In this respect, the amount of experts' weights changes to measure its impacts on the criteria weights. Furthermore, the dependency of the criteria weights on final ranking results is obtained by changing the weights among each other. A comparative analysis is introduced to compare the proposed model with two existing ranking methods in the current literature. The results indicate that jatropha is the optimum oilseed to select in the presented case study.