This jupiter nootbook contains all the necessary routines to generate the data required in the optimization model HS_SQC
Set definitions |
| $N$ Set of nodes in supply chain network $G(N,A)$ |
| $A$ Set of arcs in $G(N,A)$ |
| $P$ Set of counties (providers) |
| $D$ Set of potential locations for depots |
| $B$ Set of potential locations for biorefineries |
| $T$ Set of arcs that connects counties to potential depot locations |
| $R$ Set of arcs that connects potential depot locations to potential biorefineries locations |
Problem parameters |
| $c_{ij}^{T}$ unit cost charged per metric ton shipped along $(i,j) \in T$ |
| $c_{ij}^{R}$ unit cost charged per metric ton shipped along $(i,j) \in R$ |
| $\psi_{ij}$ fixed cost loading/unloading a unit train $(i,j) \in R$ |
| $v_{ij}$ represents the maximum capacity of a unit train along arc in metric tons$(i,j) \in R$ |
| $\xi_{i}$ fixed investment cost to install a depot at node $i \in D$ |
| $u_{i}$ represents the pre-processing capacity of depot facility $i \in D$ |
| $\varrho_{ik}$ the fixed investment cost to install a biorefinary with technology $k \in K$ at node $i \in B$ |
| $s_{i}(o)$ represents the supply of biomass at pre-processing county $i \in P$ under scenario $o \in \Omega$ |
| $d_{i}$ total demand of bio-ethanol (liters) |
| $\alpha_{i}$ represents the penalty cost for demand shortage |
| $g_{jk}$ conversion factor for biomass supplied to biorefinery $j \in B$ applying technology $k \in K$ |
| $q_{jk}$ production capacity of biorefinery $j \in B$ with technology $k \in K$ |
| $p(o)$ probability of scenario $o \in \Omega$ |
| $c'_{i}(t_{k},o)$ quality loss due to moisture content under scenario $o \in \Omega$ for a given $t_{k}$ |
| $c_{i}(\delta_{k},o)$ quality loss due to ash content under scenario $o \in \Omega$ for a given $\delta_{k}$ |
The first parameter to compute is the number of counties (providers) $P$, in the state of Texas. The information has been taken from the website: https://www.census.gov/geo/maps-data/. A list of all the counties in the United States was downloaded and filtered out to keep only the counties in Texas
In [26]:
using DataFrames
counties_ls = readtable("2016_Gaz_counties_national.csv");
nrows = nrow(counties_ls)
nrows
sum = 0
for i in 1:nrows
if counties_ls[i, 1] == "TX"
sum = sum +1
end
end
P = sum
P
Out[26]:
The number of potential locations for depots $D$ and their coordinates were computed utilizing information provided by Oak Ridge National Lab (ORNL): http://www-cta.ornl.gov/transnet/RailRoads.html. A shapefile was used to get the location infomation with the R programming environment. Further pre-process of this data was realized with the calculation of the distances
The number of potential locations for biorefineries $B$ and their coordinates were obtained from: https://maps.nrel.gov/biofuels-atlas/ from a downloadable file. A csv file was filtered out to set the number of potential locations in Texas
In [2]:
using DataFrames
bioenergy_sites = readtable("bioenergy_sites_epa_jul2015.csv");
nrows = nrow(bioenergy_sites)
nrows
sum = 0
for i in 1:nrows
if bioenergy_sites[i, 3] == "TX"
sum = sum +1
end
end
B = sum
B
Out[2]:
The distances for truck transportation were computed using the Open Source Routing Machine (OSRM) which is an algorithm that allows the estimation of the distance between two points, utilizing the current road infrastructure of Texas. test
In [ ]: