Prioritization of biodiesel development policies under hybrid uncertainties: A possibilistic stochastic multi-attribute decision-making approach

Decision-making in the field of energy policy is very difficult due to the existence of various uncertainties in this area. Classical multi-criteria decision-making (MCDM) methods are effective only in stable conditions and ignore future uncertainties. Therefore, in this study, a novel concept called possibilistic stochastic multi-attribute decision-making (PSMADM) is presented. In this approach, the decision matrix is varied in each scenario. Since the values of weights and scores in each scenario are imprecise, the possibilistic programming is used to confront this kind of uncertainty. The approach is used to rank Iran's biodiesel development policies. In addition, a new scenario planning method is presented in this research for designing biodiesel scenarios. Fuzzy SWARA and fuzzy EDAS methods are used to solve the different MCDM models. The results are validated by comparing with VIKOR model and evaluating by Spearman's rank correlation test. The Spearman's rank values for the scenarios are 0.9394, 0.8909, and 0.9636, which show a strong positive correlation between the results. Lastly, an approach is suggested for aggregating the results of scenarios to final ranking the alternatives. The results indicate that the best alternative for the development of biodiesel is supporting the private sector for more participation in this field .

Automated summary

A novel concept called possibilistic stochastic multi-attribute decision-making (PSMADM) is proposed in this study to address uncertainties in the field of energy policy. The approach is used to rank Iran's biodiesel development policies, with the results indicating that supporting the private sector for more participation in biodiesel development is the best alternative. The study also introduces a new scenario planning method for designing biodiesel scenarios, and utilizes fuzzy SWARA and fuzzy EDAS methods to solve different MCDM models.