{
"cells": [
{
"cell_type": "markdown",
"id": "171e1a75-257f-4ef5-a98c-667b9460663d",
"metadata": {},
"source": [
"# 3.6.3 Taylor et al 2023"
]
},
{
"cell_type": "markdown",
"id": "c2c6a4f3-7a9f-417d-b8c7-c9215da5f142",
"metadata": {},
"source": [
"This is used in the paper **Flexible cable routing framework for wind farm collection system optimization**."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "bdf5ac5d-ce06-4425-94be-2793edcbf33e",
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"from importlib.resources import files"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ff8b4268-f6cd-4f4a-82d1-200f208fe9c2",
"metadata": {},
"outputs": [],
"source": [
"from optiwindnet.interarraylib import G_from_S\n",
"from optiwindnet.svg import svgplot\n",
"from optiwindnet.mesh import make_planar_embedding\n",
"from optiwindnet.baselines.hgs import hgs_cvrp\n",
"from optiwindnet.importer import L_from_yaml\n",
"from optiwindnet.pathfinding import PathFinder\n",
"from optiwindnet.MILP import solver_factory, ModelOptions\n",
"from optiwindnet.heuristics import constructor\n",
"from optiwindnet.interarraylib import as_normalized"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "35bb73d8-26c0-4132-bfae-d9438f94e6fe",
"metadata": {},
"outputs": [],
"source": [
"solver = solver_factory('gurobi')"
]
},
{
"cell_type": "markdown",
"id": "e2c8bb22-2a1f-4fb1-bafd-bbc3528057f4",
"metadata": {},
"source": [
"## Reference solution"
]
},
{
"cell_type": "markdown",
"id": "e8ec0a99-6d06-4d36-9da8-ed28a8831646",
"metadata": {},
"source": [
"Taylor, P., Yue, H., Campos-Gaona, D., Anaya-Lara, O., & Jia, C. (2023). Wind farm array cable layout optimisation for complex offshore sites—A decomposition based heuristic approach. IET Renewable Power Generation, 17(2), 243–259. https://doi.org/10.1049/rpg2.12593"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2b6f398d-6333-4090-a329-cd5a0d7a2b47",
"metadata": {},
"outputs": [],
"source": [
"G_ref = pickle.load(open('data/taylor_2023_paper_routeset.pkl', 'rb'))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "37fbe442-9722-4d7f-918f-c97a7e9ed5e1",
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
""
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svgplot(G_ref)"
]
},
{
"cell_type": "markdown",
"id": "8308f53d-4b45-4c3d-9b71-26ff4b4ef519",
"metadata": {},
"source": [
"## Start here"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ed9067bb-250e-4a1a-981b-f586bc7f5985",
"metadata": {},
"outputs": [],
"source": [
"L = L_from_yaml(files('optiwindnet.data') / 'Taylor-2023.yaml')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b6fab52e-aa7a-4b9b-bedd-9735db54e982",
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
""
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svgplot(L)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d2ae9456-39d5-41c9-9677-d3a280301142",
"metadata": {},
"outputs": [],
"source": [
"P, A = make_planar_embedding(L)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "c65ec31b-4789-4440-b341-df62f28e9f19",
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
""
],
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Sʹ = constructor(A, 12)\n",
"Gʹ = G_from_S(Sʹ, A)\n",
"Hʹ = PathFinder(Gʹ, planar=P, A=A).create_detours()\n",
"svgplot(Hʹ)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "22837d22-08fb-402c-b1f9-5d07fd2b7766",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.16, 0.15)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Sʹ = hgs_cvrp(as_normalized(A), capacity=12, time_limit=0.6)\n",
"Sʹ.graph['solution_time']"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "858e13dc-814d-4a79-8d13-f46300659c49",
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
""
],
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Gʹ = G_from_S(Sʹ, A)\n",
"Hʹ = PathFinder(Gʹ, planar=P, A=A).create_detours()\n",
"svgplot(Hʹ)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "9216760f-8edc-4d79-bab0-4baab6a5ce41",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.007668344152415463"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1 - Hʹ.size(weight='length')/G_ref.size(weight='length')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "1d961d96-a63c-43c4-a283-78e40b6e8d66",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Set parameter WLSAccessID\n",
"Set parameter WLSSecret\n",
"Set parameter LicenseID to value 937681\n",
"Set parameter MIPFocus to value 1\n",
"Academic license 937681 - for non-commercial use only - registered to ma___@dtu.dk\n"
]
}
],
"source": [
"solver.set_problem(\n",
" P, A,\n",
" capacity=Sʹ.graph['capacity'],\n",
" model_options=ModelOptions(\n",
" topology=\"branched\",\n",
" feeder_route=\"segmented\",\n",
" feeder_limit=\"unlimited\",\n",
" ),\n",
" warmstart=Sʹ,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "d4deede9-2051-4318-b240-f0e790a38daf",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Set parameter RINS to value 100\n",
"Set parameter CutPasses to value 4\n",
"Set parameter TimeLimit to value 8\n",
"Set parameter MIPGap to value 0.001\n",
"Gurobi Optimizer version 13.0.2 build v13.0.2rc1 (linux64 - \"Debian GNU/Linux forky/sid\")\n",
"\n",
"CPU model: 11th Gen Intel(R) Core(TM) i7-11850H @ 2.50GHz, instruction set [SSE2|AVX|AVX2|AVX512]\n",
"Thread count: 8 physical cores, 16 logical processors, using up to 16 threads\n",
"\n",
"Non-default parameters:\n",
"TimeLimit 8\n",
"MIPGap 0.001\n",
"MIPFocus 1\n",
"RINS 100\n",
"CutPasses 4\n",
"\n",
"Academic license 937681 - for non-commercial use only - registered to ma___@dtu.dk\n",
"Optimize a model with 4316 rows, 2892 columns and 16300 nonzeros (Min)\n",
"Model fingerprint: 0xc70e3106\n",
"Model has 1446 linear objective coefficients\n",
"Variable types: 0 continuous, 2892 integer (1446 binary)\n",
"Coefficient statistics:\n",
" Matrix range [1e+00, 1e+01]\n",
" Objective range [4e+02, 9e+03]\n",
" Bounds range [1e+00, 1e+01]\n",
" RHS range [1e+00, 1e+02]\n",
"\n",
"Loaded user MIP start with objective 103512\n",
"\n",
"Presolve removed 529 rows and 0 columns\n",
"Presolve time: 0.02s\n",
"Presolved: 3787 rows, 2892 columns, 13798 nonzeros\n",
"Variable types: 0 continuous, 2892 integer (1446 binary)\n",
"\n",
"Root relaxation: objective 9.983546e+04, 2782 iterations, 0.04 seconds (0.07 work units)\n",
"\n",
" Nodes | Current Node | Objective Bounds | Work\n",
" Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time\n",
"\n",
" 0 0 99835.4625 0 133 103512.235 99835.4625 3.55% - 0s\n",
"H 0 0 103200.55910 99835.4625 3.26% - 0s\n",
"H 0 0 103166.06622 99835.4625 3.23% - 0s\n",
"H 0 0 103013.05991 99835.4625 3.08% - 0s\n",
"H 0 0 102978.56703 99835.4625 3.05% - 0s\n",
"H 0 0 102978.48906 99835.4625 3.05% - 0s\n",
"H 0 0 102973.41212 99835.4625 3.05% - 0s\n",
"H 0 0 102971.33465 99835.4625 3.05% - 0s\n",
"H 0 0 102698.63828 99835.4625 2.79% - 0s\n",
"H 0 0 102687.22923 99835.4625 2.78% - 0s\n",
"H 0 0 102685.15176 99835.4625 2.78% - 0s\n",
"H 0 0 102677.99735 99835.4625 2.77% - 0s\n",
"H 0 0 102266.22193 99835.4625 2.38% - 0s\n",
"H 0 0 102022.70468 99835.4625 2.14% - 0s\n",
" 0 0 100368.185 0 236 102022.705 100368.185 1.62% - 0s\n",
"H 0 0 101728.83004 100368.185 1.34% - 0s\n",
"H 0 0 100953.95751 100368.185 0.58% - 0s\n",
"H 0 0 100953.46423 100368.185 0.58% - 0s\n",
"H 0 0 100939.92989 100368.185 0.57% - 0s\n",
"H 0 0 100932.27453 100368.185 0.56% - 0s\n",
"H 0 0 100916.86258 100368.185 0.54% - 0s\n",
" 0 0 100389.423 0 229 100916.863 100389.423 0.52% - 0s\n",
" 0 0 100390.106 0 230 100916.863 100390.106 0.52% - 0s\n",
" 0 0 100434.413 0 252 100916.863 100434.413 0.48% - 0s\n",
" 0 0 100434.413 0 139 100916.863 100434.413 0.48% - 0s\n",
" 0 0 100483.339 0 229 100916.863 100483.339 0.43% - 0s\n",
" 0 0 100484.023 0 204 100916.863 100484.023 0.43% - 0s\n",
" 0 0 100484.030 0 216 100916.863 100484.030 0.43% - 0s\n",
" 0 0 100490.604 0 244 100916.863 100490.604 0.42% - 0s\n",
" 0 0 100491.550 0 274 100916.863 100491.550 0.42% - 0s\n",
" 0 0 100491.675 0 288 100916.863 100491.675 0.42% - 0s\n",
" 0 0 100491.723 0 286 100916.863 100491.723 0.42% - 0s\n",
"H 0 0 100874.89356 100491.723 0.38% - 0s\n",
" 0 0 100511.086 0 258 100874.894 100511.086 0.36% - 0s\n",
" 0 0 100512.393 0 243 100874.894 100512.393 0.36% - 0s\n",
" 0 0 100512.764 0 243 100874.894 100512.764 0.36% - 0s\n",
" 0 0 100512.880 0 299 100874.894 100512.880 0.36% - 0s\n",
" 0 0 100512.893 0 252 100874.894 100512.893 0.36% - 0s\n",
"H 0 0 100863.60197 100512.893 0.35% - 0s\n",
" 0 0 100517.112 0 297 100863.602 100517.112 0.34% - 0s\n",
" 0 0 100517.112 0 181 100863.602 100517.112 0.34% - 0s\n",
" 0 0 100517.112 0 225 100863.602 100517.112 0.34% - 0s\n",
" 0 0 100518.560 0 294 100863.602 100518.560 0.34% - 0s\n",
" 0 0 100518.913 0 295 100863.602 100518.913 0.34% - 0s\n",
" 0 0 100518.916 0 295 100863.602 100518.916 0.34% - 0s\n",
" 0 0 100521.181 0 314 100863.602 100521.181 0.34% - 0s\n",
" 0 0 100521.540 0 318 100863.602 100521.540 0.34% - 1s\n",
" 0 0 100521.675 0 297 100863.602 100521.675 0.34% - 1s\n",
" 0 0 100521.675 0 297 100863.602 100521.675 0.34% - 1s\n",
" 0 0 100526.194 0 291 100863.602 100526.194 0.33% - 1s\n",
" 0 0 100527.535 0 306 100863.602 100527.535 0.33% - 1s\n",
" 0 0 100528.106 0 319 100863.602 100528.106 0.33% - 1s\n",
" 0 0 100528.412 0 314 100863.602 100528.412 0.33% - 1s\n",
" 0 0 100528.548 0 319 100863.602 100528.548 0.33% - 1s\n",
" 0 0 100528.593 0 321 100863.602 100528.593 0.33% - 1s\n",
" 0 0 100531.773 0 303 100863.602 100531.773 0.33% - 1s\n",
" 0 0 100531.773 0 303 100863.602 100531.773 0.33% - 1s\n",
" 0 0 100531.773 0 303 100863.602 100531.773 0.33% - 1s\n",
" 0 2 100531.873 0 303 100863.602 100531.873 0.33% - 1s\n",
"H 12 16 100855.16790 100536.793 0.32% 132 1s\n",
"H 221 200 100852.07827 100536.793 0.31% 62.4 1s\n",
"H 304 236 100796.21047 100539.731 0.25% 56.5 1s\n",
"H 375 263 100794.97109 100539.731 0.25% 55.1 2s\n",
" 2051 1193 100681.706 22 326 100794.971 100561.078 0.23% 42.9 5s\n",
"\n",
"Cutting planes:\n",
" Gomory: 24\n",
" Lift-and-project: 1\n",
" Implied bound: 23\n",
" MIR: 197\n",
" StrongCG: 6\n",
" Flow cover: 177\n",
" Flow path: 19\n",
" GUB cover: 2\n",
" Inf proof: 17\n",
" Zero half: 3\n",
" Network: 17\n",
" RLT: 2\n",
" Relax-and-lift: 4\n",
"\n",
"Explored 6628 nodes (332428 simplex iterations) in 8.01 seconds (7.43 work units)\n",
"Thread count was 16 (of 16 available processors)\n",
"\n",
"Solution count 10: 100795 100796 100852 ... 100953\n",
"\n",
"Time limit reached\n",
"Best objective 1.007949710936e+05, best bound 1.005981129097e+05, gap 0.1953%\n"
]
},
{
"data": {
"text/plain": [
"SolutionInfo(runtime=8.014618873596191, bound=100598.1129097235, objective=100794.97109360031, relgap=0.0019530556112170583, termination='maxTimeLimit')"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"solver.solve(\n",
" mip_gap=0.001,\n",
" time_limit=8,\n",
" verbose=True,\n",
" options=dict(\n",
" mipfocus=1,\n",
" RINS=100,\n",
" CutPasses=4,\n",
" # VarBranch=1,\n",
" )\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "9100aab2-2b32-415b-aa17-89031eea3d92",
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
""
],
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"S, G = solver.get_solution()\n",
"\n",
"svgplot(G)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "08f5b4da-6779-459e-acf3-10eb45a5d307",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.03371769751959075"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1 - G.size(weight='length')/G_ref.size(weight='length')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "582d5e10-904c-4619-992d-a7c5f5bae5c8",
"metadata": {},
"outputs": [],
"source": [
"pickle.dump(G, open('Taylor_comparison_κ_12_branched_our.pkl', 'wb'))"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 5
}