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38
src/reports/plant_emissions.jl
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38
src/reports/plant_emissions.jl
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# RELOG: Reverse Logistics Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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using DataFrames
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using CSV
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function plant_emissions_report(solution)::DataFrame
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df = DataFrame()
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df."plant type" = String[]
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df."location name" = String[]
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df."year" = Int[]
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df."emission type" = String[]
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df."emission amount (tonne)" = Float64[]
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T = length(solution["Energy"]["Plants (GJ)"])
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for (plant_name, plant_dict) in solution["Plants"]
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for (location_name, location_dict) in plant_dict
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for (emission_name, emission_amount) in location_dict["Emissions (tonne)"]
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for year = 1:T
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push!(
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df,
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[
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plant_name,
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location_name,
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year,
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emission_name,
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round(emission_amount[year], digits = 2),
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],
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)
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end
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end
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end
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end
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return df
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end
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write_plant_emissions_report(solution, filename) =
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CSV.write(filename, plant_emissions_report(solution))
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66
src/reports/plant_outputs.jl
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66
src/reports/plant_outputs.jl
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# RELOG: Reverse Logistics Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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using DataFrames
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using CSV
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function plant_outputs_report(solution)::DataFrame
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df = DataFrame()
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df."plant type" = String[]
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df."location name" = String[]
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df."year" = Int[]
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df."product name" = String[]
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df."amount produced (tonne)" = Float64[]
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df."amount sent (tonne)" = Float64[]
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df."amount disposed (tonne)" = Float64[]
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df."disposal cost (\$)" = Float64[]
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T = length(solution["Energy"]["Plants (GJ)"])
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for (plant_name, plant_dict) in solution["Plants"]
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for (location_name, location_dict) in plant_dict
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for (product_name, amount_produced) in location_dict["Total output"]
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send_dict = location_dict["Output"]["Send"]
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disposal_dict = location_dict["Output"]["Dispose"]
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sent = zeros(T)
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if product_name in keys(send_dict)
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for (dst_plant_name, dst_plant_dict) in send_dict[product_name]
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for (dst_location_name, dst_location_dict) in dst_plant_dict
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sent += dst_location_dict["Amount (tonne)"]
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end
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end
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end
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sent = round.(sent, digits = 2)
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disposal_amount = zeros(T)
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disposal_cost = zeros(T)
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if product_name in keys(disposal_dict)
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disposal_amount += disposal_dict[product_name]["Amount (tonne)"]
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disposal_cost += disposal_dict[product_name]["Cost (\$)"]
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end
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disposal_amount = round.(disposal_amount, digits = 2)
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disposal_cost = round.(disposal_cost, digits = 2)
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for year = 1:T
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push!(
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df,
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[
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plant_name,
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location_name,
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year,
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product_name,
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round(amount_produced[year], digits = 2),
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sent[year],
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disposal_amount[year],
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disposal_cost[year],
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],
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)
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end
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end
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end
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end
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return df
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end
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write_plant_outputs_report(solution, filename) =
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CSV.write(filename, plant_outputs_report(solution))
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79
src/reports/plants.jl
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79
src/reports/plants.jl
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# RELOG: Reverse Logistics Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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using DataFrames
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using CSV
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function plants_report(solution)::DataFrame
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df = DataFrame()
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df."plant type" = String[]
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df."location name" = String[]
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df."year" = Int[]
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df."latitude (deg)" = Float64[]
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df."longitude (deg)" = Float64[]
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df."capacity (tonne)" = Float64[]
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df."amount processed (tonne)" = Float64[]
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df."amount received (tonne)" = Float64[]
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df."amount in storage (tonne)" = Float64[]
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df."utilization factor (%)" = Float64[]
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df."energy (GJ)" = Float64[]
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df."opening cost (\$)" = Float64[]
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df."expansion cost (\$)" = Float64[]
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df."fixed operating cost (\$)" = Float64[]
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df."variable operating cost (\$)" = Float64[]
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df."storage cost (\$)" = Float64[]
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df."total cost (\$)" = Float64[]
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T = length(solution["Energy"]["Plants (GJ)"])
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for (plant_name, plant_dict) in solution["Plants"]
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for (location_name, location_dict) in plant_dict
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for year = 1:T
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capacity = round(location_dict["Capacity (tonne)"][year], digits = 2)
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received = round(location_dict["Total input (tonne)"][year], digits = 2)
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processed = round(location_dict["Process (tonne)"][year], digits = 2)
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in_storage = round(location_dict["Storage (tonne)"][year], digits = 2)
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utilization_factor = round(processed / capacity * 100.0, digits = 2)
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energy = round(location_dict["Energy (GJ)"][year], digits = 2)
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latitude = round(location_dict["Latitude (deg)"], digits = 6)
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longitude = round(location_dict["Longitude (deg)"], digits = 6)
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opening_cost = round(location_dict["Opening cost (\$)"][year], digits = 2)
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expansion_cost =
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round(location_dict["Expansion cost (\$)"][year], digits = 2)
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fixed_cost =
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round(location_dict["Fixed operating cost (\$)"][year], digits = 2)
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var_cost =
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round(location_dict["Variable operating cost (\$)"][year], digits = 2)
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storage_cost = round(location_dict["Storage cost (\$)"][year], digits = 2)
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total_cost = round(
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opening_cost + expansion_cost + fixed_cost + var_cost + storage_cost,
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digits = 2,
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)
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push!(
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df,
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[
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plant_name,
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location_name,
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year,
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latitude,
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longitude,
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capacity,
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processed,
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received,
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in_storage,
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utilization_factor,
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energy,
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opening_cost,
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expansion_cost,
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fixed_cost,
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var_cost,
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storage_cost,
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total_cost,
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],
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)
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end
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end
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end
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return df
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end
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write_plants_report(solution, filename) = CSV.write(filename, plants_report(solution))
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75
src/reports/tr.jl
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75
src/reports/tr.jl
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# RELOG: Reverse Logistics Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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using DataFrames
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using CSV
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function transportation_report(solution)::DataFrame
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df = DataFrame()
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df."source type" = String[]
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df."source location name" = String[]
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df."source latitude (deg)" = Float64[]
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df."source longitude (deg)" = Float64[]
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df."destination type" = String[]
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df."destination location name" = String[]
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df."destination latitude (deg)" = Float64[]
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df."destination longitude (deg)" = Float64[]
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df."product" = String[]
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df."year" = Int[]
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df."distance (km)" = Float64[]
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df."amount (tonne)" = Float64[]
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df."amount-distance (tonne-km)" = Float64[]
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df."transportation cost (\$)" = Float64[]
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df."transportation energy (GJ)" = Float64[]
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T = length(solution["Energy"]["Plants (GJ)"])
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for (dst_plant_name, dst_plant_dict) in solution["Plants"]
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for (dst_location_name, dst_location_dict) in dst_plant_dict
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for (src_plant_name, src_plant_dict) in dst_location_dict["Input"]
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for (src_location_name, src_location_dict) in src_plant_dict
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for year = 1:T
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push!(
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df,
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[
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src_plant_name,
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src_location_name,
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round(src_location_dict["Latitude (deg)"], digits = 6),
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round(src_location_dict["Longitude (deg)"], digits = 6),
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dst_plant_name,
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dst_location_name,
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round(dst_location_dict["Latitude (deg)"], digits = 6),
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round(dst_location_dict["Longitude (deg)"], digits = 6),
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dst_location_dict["Input product"],
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year,
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round(src_location_dict["Distance (km)"], digits = 2),
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round(
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src_location_dict["Amount (tonne)"][year],
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digits = 2,
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),
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round(
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src_location_dict["Amount (tonne)"][year] *
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src_location_dict["Distance (km)"],
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digits = 2,
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),
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round(
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src_location_dict["Transportation cost (\$)"][year],
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digits = 2,
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),
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round(
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src_location_dict["Transportation energy (J)"][year] /
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1e9,
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digits = 2,
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),
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],
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)
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end
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end
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end
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end
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end
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return df
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end
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write_transportation_report(solution, filename) =
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CSV.write(filename, transportation_report(solution))
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71
src/reports/tr_emissions.jl
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71
src/reports/tr_emissions.jl
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@@ -0,0 +1,71 @@
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# RELOG: Reverse Logistics Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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using DataFrames
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using CSV
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function transportation_emissions_report(solution)::DataFrame
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df = DataFrame()
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df."source type" = String[]
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df."source location name" = String[]
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df."source latitude (deg)" = Float64[]
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df."source longitude (deg)" = Float64[]
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df."destination type" = String[]
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df."destination location name" = String[]
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df."destination latitude (deg)" = Float64[]
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df."destination longitude (deg)" = Float64[]
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df."product" = String[]
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df."year" = Int[]
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df."distance (km)" = Float64[]
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df."shipped amount (tonne)" = Float64[]
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df."shipped amount-distance (tonne-km)" = Float64[]
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df."emission type" = String[]
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df."emission amount (tonne)" = Float64[]
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T = length(solution["Energy"]["Plants (GJ)"])
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for (dst_plant_name, dst_plant_dict) in solution["Plants"]
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for (dst_location_name, dst_location_dict) in dst_plant_dict
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for (src_plant_name, src_plant_dict) in dst_location_dict["Input"]
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for (src_location_name, src_location_dict) in src_plant_dict
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for (emission_name, emission_amount) in
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src_location_dict["Emissions (tonne)"]
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for year = 1:T
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push!(
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df,
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[
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src_plant_name,
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src_location_name,
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round(src_location_dict["Latitude (deg)"], digits = 6),
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round(src_location_dict["Longitude (deg)"], digits = 6),
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dst_plant_name,
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dst_location_name,
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round(dst_location_dict["Latitude (deg)"], digits = 6),
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round(dst_location_dict["Longitude (deg)"], digits = 6),
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dst_location_dict["Input product"],
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year,
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round(src_location_dict["Distance (km)"], digits = 2),
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round(
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src_location_dict["Amount (tonne)"][year],
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digits = 2,
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),
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round(
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src_location_dict["Amount (tonne)"][year] *
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src_location_dict["Distance (km)"],
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digits = 2,
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),
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emission_name,
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round(emission_amount[year], digits = 2),
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],
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)
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end
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end
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end
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end
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end
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end
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return df
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end
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write_transportation_emissions_report(solution, filename) =
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CSV.write(filename, transportation_emissions_report(solution))
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13
src/reports/write.jl
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13
src/reports/write.jl
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@@ -0,0 +1,13 @@
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# RELOG: Reverse Logistics Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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using DataFrames
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using CSV
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function write(solution::AbstractDict, filename::AbstractString)
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@info "Writing solution: $filename"
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open(filename, "w") do file
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JSON.print(file, solution, 2)
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end
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end
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