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RELOG
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Fix expansion cost breakdown

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Alinson S. Xavier 5 years ago
parent c10eb506a6
commit 237275fc2b
5 changed files with 83 additions and 119 deletions
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6
CHANGELOG.md
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@ -1,4 +1,8 @@
# Version 0.3 (June 25, 2020)
# Vertion 0.3.1 (July 17, 2020)
- Fix expansion cost breakdown
# Version 0.3.0 (June 25, 2020)
- Track emissions and energy (transportation and plants)
- Minor changes to input file format:

2
Project.toml
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@ -1,7 +1,7 @@
name = "RELOG"
uuid = "a2afcdf7-cf04-4913-85f9-c0d81ddf2008"
authors = ["Alinson S Xavier <axavier@anl.gov>"]
version = "0.3.0"
version = "0.3.1"
[deps]
Cbc = "9961bab8-2fa3-5c5a-9d89-47fab24efd76"

116
instances/solutions/s1.json
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@ -15,16 +15,16 @@
216672.818
],
"Transportation ($)": [
714499.2748313101,
714499.2748313101
714499.27483131,
714499.27483131
],
"Disposal ($)": [
-20.0,
-20.0
],
"Total ($)": [
935782.0928313101,
935396.8688313101
939896.86883131,
931282.09283131
],
"Fixed operating ($)": [
130.0,
@ -35,8 +35,8 @@
0.0
],
"Expansion ($)": [
0.0,
4114.776
4114.776,
0.0
]
},
"Plants": {
@ -119,8 +119,8 @@
]
},
"Expansion cost ($)": [
0.0,
750.0
750.0,
0.0
],
"Longitude (deg)": 0.0,
"Energy (GJ)": [
@ -207,16 +207,16 @@
"C3": {
"Distance (km)": 8889.75,
"Amount (tonne)": [
212.97,
212.97
212.97000000000003,
212.97000000000003
],
"Transportation energy (J)": [
227190.00689999998,
208257.506325
227190.00690000004,
208257.50632500005
],
"Transportation cost ($)": [
28398.7508625,
28398.7508625
28398.750862500005,
28398.750862500005
],
"Longitude (deg)": 76.0,
"Variable operating cost ($)": [
@ -226,74 +226,74 @@
"Latitude (deg)": 84.0,
"Emissions (tonne)": {
"CH4": [
0.63891,
0.42594
0.6389100000000001,
0.42594000000000004
],
"CO2": [
11.07444,
10.6485
11.074440000000001,
10.648500000000002
]
}
},
"C7": {
"Distance (km)": 8526.39,
"Amount (tonne)": [
246.61999999999998,
246.61999999999998
246.62,
246.62
],
"Transportation energy (J)": [
252333.39621599994,
231305.61319799995
252333.396216,
231305.613198
],
"Transportation cost ($)": [
31541.674526999996,
31541.674526999996
31541.674527,
31541.674527
],
"Longitude (deg)": 83.0,
"Variable operating cost ($)": [
7398.599999999999,
7398.599999999999
7398.6,
7398.6
],
"Latitude (deg)": 30.0,
"Emissions (tonne)": {
"CH4": [
0.73986,
0.49323999999999996
0.7398600000000001,
0.49324
],
"CO2": [
12.824239999999998,
12.331
12.82424,
12.331000000000001
]
}
},
"C5": {
"Distance (km)": 9148.52,
"Amount (tonne)": [
510.33,
510.33
510.3299999999999,
510.3299999999999
],
"Transportation energy (J)": [
560251.705392,
513564.06327600003
560251.7053919999,
513564.0632759999
],
"Transportation cost ($)": [
70031.463174,
70031.463174
70031.46317399999,
70031.46317399999
],
"Longitude (deg)": 92.0,
"Variable operating cost ($)": [
15309.9,
15309.9
15309.899999999998,
15309.899999999998
],
"Latitude (deg)": 32.0,
"Emissions (tonne)": {
"CH4": [
1.53099,
1.5309899999999999,
1.02066
],
"CO2": [
26.537159999999997,
25.5165
25.516499999999997
]
}
},
@ -333,7 +333,7 @@
},
"L2": {
"Opening cost ($)": [
1000.0,
999.9999999999999,
0.0
],
"Emissions (tonne)": {
@ -347,8 +347,8 @@
]
},
"Expansion cost ($)": [
0.0,
3364.776
3364.7759999999994,
0.0
],
"Longitude (deg)": 0.5,
"Energy (GJ)": [
@ -357,8 +357,8 @@
],
"Total output": {
"P2": [
747.7280000000001,
747.7280000000001
747.728,
747.728
],
"P3": [
1869.32,
@ -366,8 +366,8 @@
]
},
"Capacity (tonne)": [
3738.64,
3738.64
3738.6399999999994,
3738.6399999999994
],
"Latitude (deg)": 0.5,
"Output": {
@ -377,8 +377,8 @@
"L4": {
"Distance (km)": 43.35,
"Amount (tonne)": [
747.7280000000001,
747.7280000000001
747.728,
747.728
],
"Longitude (deg)": 0.2,
"Latitude (deg)": 0.75
@ -635,7 +635,7 @@
"F2": {
"L4": {
"Opening cost ($)": [
3000.0,
2999.9999999999995,
0.0
],
"Emissions (tonne)": {},
@ -695,8 +695,8 @@
"Dispose": {}
},
"Total input (tonne)": [
946.7280000000001,
946.7280000000001
946.728,
946.728
],
"Fixed operating cost ($)": [
50.0,
@ -729,21 +729,21 @@
"L2": {
"Distance (km)": 43.35,
"Amount (tonne)": [
747.7280000000001,
747.7280000000001
747.728,
747.728
],
"Transportation energy (J)": [
0.0,
0.0
],
"Transportation cost ($)": [
648.2801760000001,
648.2801760000001
648.280176,
648.280176
],
"Longitude (deg)": 0.5,
"Variable operating cost ($)": [
37386.4,
37386.4
37386.399999999994,
37386.399999999994
],
"Latitude (deg)": 0.5,
"Emissions (tonne)": {}

64
instances/solutions/s1.log
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@ -1,53 +1,11 @@
Gurobi Optimizer version 9.0.0 build v9.0.0rc2 (linux64)
Optimize a model with 124 rows, 141 columns and 400 nonzeros
Model fingerprint: 0x46af87f0
Variable types: 117 continuous, 24 integer (24 binary)
Coefficient statistics:
Matrix range [5e-02, 2e+04]
Objective range [9e-01, 3e+03]
Bounds range [1e+00, 1e+08]
RHS range [3e+01, 2e+04]
Presolve removed 98 rows and 107 columns
Presolve time: 0.00s
Presolved: 26 rows, 34 columns, 74 nonzeros
Variable types: 28 continuous, 6 integer (6 binary)
Root relaxation: objective 1.870636e+06, 10 iterations, 0.00 seconds
Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
0 0 1870636.06 0 2 - 1870636.06 - - 0s
H 0 0 1871178.9617 1870636.06 0.03% - 0s
Explored 1 nodes (10 simplex iterations) in 0.00 seconds
Thread count was 8 (of 80 available processors)
Solution count 1: 1.87118e+06
Optimal solution found (tolerance 1.00e-02)
Best objective 1.871178961663e+06, best bound 1.870636062997e+06, gap 0.0290%
Gurobi Optimizer version 9.0.0 build v9.0.0rc2 (linux64)
Optimize a model with 124 rows, 141 columns and 400 nonzeros
Model fingerprint: 0x133a97e7
Coefficient statistics:
Matrix range [5e-02, 2e+04]
Objective range [9e-01, 3e+03]
Bounds range [1e+00, 1e+08]
RHS range [3e+01, 2e+04]
Presolve removed 115 rows and 110 columns
Presolve time: 0.00s
Presolved: 9 rows, 31 columns, 94 nonzeros
Iteration Objective Primal Inf. Dual Inf. Time
0 1.0296026e+06 1.950922e+03 0.000000e+00 0s
7 1.8711790e+06 0.000000e+00 0.000000e+00 0s
Solved in 7 iterations and 0.00 seconds
Optimal objective 1.871178962e+06
Reading s1.json...
Building graph...
Building optimization model...
Optimizing MILP...
Re-optimizing with integer variables fixed...
Extracting solution...
[ Info: Reading s1.json...
[ Info: Building graph...
[ Info: 2 time periods
[ Info: 6 process nodes
[ Info: 8 shipping nodes (plant)
[ Info: 10 shipping nodes (collection)
[ Info: 38 arcs
[ Info: Building optimization model...
[ Info: Optimizing MILP...
[ Info: Re-optimizing with integer variables fixed...
[ Info: Extracting solution...

10
src/model.jl
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@ -304,11 +304,13 @@ function get_solution(model::ManufacturingModel)
JuMP.value(vars.expansion[process_node, t]) *
slope_fix_oper_cost(plant, t)
for t in 1:T],
"Expansion cost (\$)" => [JuMP.value(vars.expansion[process_node, t]) *
(if t < T
slope_open(plant, t) - slope_open(plant, t + 1)
"Expansion cost (\$)" => [(if t == 1
slope_open(plant, t) * JuMP.value(vars.expansion[process_node, t])
else
slope_open(plant, t)
slope_open(plant, t) * (
JuMP.value(vars.expansion[process_node, t]) -
JuMP.value(vars.expansion[process_node, t - 1])
)
end)
for t in 1:T],
)

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