IATA emphasizes transitioning from fossil fuels to Sustainable Aviation Fuel (SAF) to mitigate Greenhouse Gas (GHG) emissions from the aviation sector, and many airports aim to meet 10% of their fuel demand with SAF by 2030. This study develops a new framework to design and optimize a Carinata-based SAF supply chain network that integrates all sustainability pillars. The framework has two phases. Phase I uses fuzzy Delphi method and trapezoidal fuzzy Analytic Hierarchy Process (AHP) to evaluate Carinata farms. Then, Phase II formulates a new multi-objective Mixed-Integer Linear Programming (MILP) model to design the network. The 4 objective functions include minimization of the total cost and GHG emissions, and maximization of job creation and supply from high-weight farms. The weighted-sum and ε-constraint methods are used to solve the multi-objective model. This paper presents an application in Canada, resulting in a 66% reduction in GHG emissions, with a 1. 10 /L unit production cost and 0. 90 kg CO₂e L -1. The results show that incorporating social aspects into the framework provides significant benefits for rural employment and economic development. • Two-phase framework to design and optimize a Carinata-based SAF supply chain network. • New decision-making approach to evaluate farms, considering sustainability. • New multi-objective programming model for sustainable aviation fuel supply chain. • New objectives integrating sustainability across the triple bottom line dimensions. • Application across Canada to deploy facilities and supply airports in the network.
Alharairi et al. (Mon,) studied this question.