Supporting codes for "Incentivizing Sustainable Aviation Fuel: Supply Chain and Policy Insights from Brazil."
The scripts in this repository build an optimization model via Pyomo (v6.6.1) and solve using Gurobi (v10.0.3).
The content of this repository is detailed below:
create_sc_model_full: contains a function to create and initialize the optimization model
create_maps: contains a function to create interactive maps of the optimal supply chain designs
run_blend_and_opt_sensitivity: contains a script to run a sensitivty analysis varying the decision-making paradigm and SAF blend requirement solving instances of create_sc_model_full and collect results data
run_create_maps: contains a script to run create_maps for different case studies
run_integer_cuts: contains a script to run an integer cut analysis on the optimal supply chain design and collect results data
run_mill_specific_incentives: contains a script to run instances of create_sc_model_full where mill-specific incentives are a variable to be optimized and collect results data
run_unconstrained_SAF_prem_sensitivity: contains a script to run instances of create_sc_model_full with no required SAF production at various SAF premium prices and collect results data
IntegerCutAnalysis: make plots to visualize the integer cut analysis results (maps)
SensitivityAnalysis: make plots to visualize the incentive sensitivty study to production incentives (line plot), SAF premium prices (line plot), and mill-specific incentives (bar chart and line plot)
SupplyChainMaps: make plots to visualize the optimal supply chain infrastructure locations for each case study (maps)
SupplyChainSummary: make plots to visualize the supply chain flows in the optimal design (line plot) and emissions sensitivity to ATJ technology (contour plot)
Case1: results files from run_blend_and_opt_sensitivity for Case 1
Case2: results files from run_blend_and_opt_sensitivity for Case 2
Case3: results files from run_blend_and_opt_sensitivity for Case 3
Case4: results files from run_blend_and_opt_sensitivity for Case 4
integer_cuts_case1: results files from run_integer_cuts for Case 1
integer_cuts_case3: results files from run_integer_cuts for Case 3
mill_specific_incentives: results files from run_mill_specific_incentives
unconstrained_SAF: results files from run_unconstrained_SAF_prem_sensitivity
Results_Figures: all figures produced for the manuscript
README: this file
335MillsLatitudesLongitudes: excel file containing latitude and longitude data for all sugarcane mills in the supply chain
AirportsLatitudeLongitude: excel file containing latitude and longitude data for all airports in the supply chain
base_case_data_with_demands: excel file containing input data to the supply chain model including distances between infrastructure, capacity, demand, price, conversion, and cost data
integer_cut_organized_data: excel file containing organized results data from integer_cuts_case1 and integer_cuts_case3 for easy plotting
OilRefineriesLatLong: excel file containing latitude and longitude data for all refineries in the supply chain
gadm41_BRA_1: database file from the GADM database containing geographic data from Brazil to create map figures in python
gadm41_BRA_1: shape file from the GADM database containing geographic data from Brazil to create map figures in python
gadm41_BRA_1: shape index file from the GADM database containing geographic data from Brazil to create map figures in python