A new multi-objective optimization model for Carinata-based sustainable aviation fuel supply chain network design

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.