A benders-local branching algorithm for second-generation biodiesel supply chain network design under epistemic uncertainty

•Developing a possibilistic MILP model to design a second-generation biodiesel supply chain network under epistemic uncertainty.•Employing a credibility-based possibilistic programming approach to convert the original possibilistic programming model into a crisp counterpart.•Proposing an accelerated benders decomposition algorithm using efficient acceleration mechanisms to deal with the computational complexity of solving the proposed model.•Verification and validation of the proposed approach through investigating a real case study in Iran.