This paper introduces three distinct models, stochastic, robust, and min-max regret models, for designing the biofuel supply chain network under conditions of biomass price uncertainty and disruptions that reduce the quantity of biofuel. The primary objective of this study, through the development of these models, is to offer a flexible framework that can be effectively adapted to various problem scenarios. These models are proposed to meet the specific needs of decision-makers while addressing different risks inherent in the problem. In contrast to previous related studies, this paper introduces a more realistic problem by considering the dependency of biomass prices on biomass availability, which is influenced by disruptions. An efficient algorithm incorporating the benders decomposition algorithm enhanced with acceleration technique is developed to alleviate the computational burden and reduce problem-solving time. Comprehensive experiments are conducted utilizing Iran’s real case study to examine the concurrent influence of uncertainty and disruption on the biofuel supply chain network and assess the performance of the introduced models and algorithms. The results obtained from solving various test problems demonstrate that each model generates a unique structure for the biofuel supply chain network, with each structure being optimized to meet the specific conditions and requirements of the decision-makers. Furthermore, the findings underscore the effectiveness and computational efficiency of the proposed algorithm in addressing the problem at hand.

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