Skip to content
Snippets Groups Projects
Commit 68b641a3 authored by David Radu's avatar David Radu
Browse files

Updated set-ups Pan

parent e2b8bd48
No related branches found
No related tags found
No related merge requests found
Hide whitespace changes
Inline Side-by-side
config_model.yml
+ 7
− 7
View file @ 68b641a3
......@@ -25,7 +25,7 @@ legacy_layer: False
distance_layer: False
# Start time and end time of the analysis.
time_slice: ['2009-01-01T00:00', '2018-31-31T23:00']
time_slice: ['2009-01-01T00:00', '2018-12-31T23:00']
# List of regions to be considered in the optimization.
#regions: ['DE', 'FR', 'ES', 'UK', 'IT', 'SE', 'PL', 'NL', 'PT', 'AT', 'EL', 'IE', 'DK', 'FI',
# 'RO', 'BE', 'BG', 'LT', 'CZ', 'EE', 'LV', 'HR', 'NO']
......@@ -51,22 +51,22 @@ name_prefix: 'off_'
# Solution method: BB or HEU or RAND.
solution_method:
BB:
set: True
set: False
c: 8
solver: 'gurobi'
mipgap: 0.02
timelimit: 1800
threads: 0
HEU:
set: False
c: [8, 12, 24]
set: True
c: [1, 106, 212, 318, 424, 530]
neighborhood: 1
no_iterations: 150
no_iterations: 200
no_epochs: 500
initial_temp: 200.
no_runs: 5
no_runs: 100
algorithm: 'SALS' #'GLS'
which_sol: 'max' #'rand'
which_sol: 'rand' #'rand'
seed: 1
RAND:
set: False
......
src/jl/main_heuristics.jl
+ 1
− 1
View file @ 68b641a3
......@@ -41,6 +41,6 @@ function main_call(index_dict, deployment_dict, D, c, N, I, E, T_init, R, run)
throw(ArgumentError)
end
return x_sol, LB_sol
return x_sol, LB_sol, obj_sol
end
src/jl/optimisation_models.jl
+ 2
− 2
View file @ 68b641a3
......@@ -7,7 +7,7 @@ function solve_MILP(D::Array{Float64, 2}, c::Float64, n::Float64, solver::String
L = size(D)[2]
if solver == "Gurobi"
MILP_model = Model(optimizer_with_attributes(Gurobi.Optimizer, "TimeLimit" => 1800., "MIPGap" => 0.05))
MILP_model = Model(optimizer_with_attributes(Gurobi.Optimizer, "TimeLimit" => 7200., "MIPGap" => 0.05))
else
println("Please use Cbc or Gurobi")
throw(ArgumentError)
......@@ -50,7 +50,7 @@ function solve_MILP_partitioning(D::Array{Float64, 2}, c::Float64, n::Array{Int6
# Selects solver
if solver == "Gurobi"
MILP_model = Model(optimizer_with_attributes(Gurobi.Optimizer, "TimeLimit" => 1800., "MIPGap" => 0.01))
MILP_model = Model(optimizer_with_attributes(Gurobi.Optimizer, "TimeLimit" => 7200., "MIPGap" => 0.01))
else
println("Please use Cbc or Gurobi")
throw(ArgumentError)
......
src/main.py
+ 58
− 44
View file @ 68b641a3
......@@ -5,7 +5,7 @@ from random import sample
from pyomo.opt import SolverFactory
from numpy import zeros, argmax
import argparse
import time
from src.helpers import read_inputs, init_folder, custom_log, remove_garbage, generate_jl_output
from src.models import preprocess_input_data, build_model
......@@ -15,8 +15,8 @@ def parse_args():
parser = argparse.ArgumentParser(description='Command line arguments.')
parser.add_argument('-c', '--global_thresh', type=float, help='Global threshold')
parser.add_argument('-tl', '--time_limit', type=float, help='Solver time limit')
parser.add_argument('-c', '--global_thresh', type=int, help='Global threshold')
parser.add_argument('-tl', '--time_limit', type=int, help='Solver time limit')
parser.add_argument('-th', '--threads', type=int, help='Number of threads')
parsed_args = vars(parser.parse_args())
......@@ -33,8 +33,8 @@ if parameters['solution_method']['BB']['set']:
args = parse_args()
parameters['solution_method']['BB']['timelimit'] = args['time_limit']
parameters['solution_method']['BB']['threads'] = args['thread']
parameters['solution_method']['BB']['c'] = args['thread']
parameters['solution_method']['BB']['threads'] = args['threads']
parameters['solution_method']['BB']['c'] = args['global_thresh']
custom_log(' BB chosen to solve the IP.')
......@@ -60,7 +60,7 @@ if parameters['solution_method']['BB']['set']:
instance, indices = build_model(parameters, input_dict, output_folder, write_lp=False)
custom_log(' Sending model to solver.')
results = opt.solve(instance, tee=True, keepfiles=False, report_timing=False,
results = opt.solve(instance, tee=False, keepfiles=False, report_timing=False,
logfile=join(output_folder, 'solver_log.log'))
objective = instance.objective()
......@@ -152,44 +152,58 @@ elif parameters['solution_method']['HEU']['set']:
for c in parameters['solution_method']['HEU']['c']:
print('Running heuristic for c value of', c)
jl_selected, jl_objective = fn(jl_dict['index_dict'],
jl_dict['deployment_dict'],
jl_dict['criticality_matrix'],
c,
parameters['solution_method']['HEU']['neighborhood'],
parameters['solution_method']['HEU']['no_iterations'],
parameters['solution_method']['HEU']['no_epochs'],
parameters['solution_method']['HEU']['initial_temp'],
parameters['solution_method']['HEU']['no_runs'],
parameters['solution_method']['HEU']['algorithm'])
if parameters['solution_method']['HEU']['which_sol'] == 'max':
jl_objective_seed = max(jl_objective)
jl_selected_seed = jl_selected[argmax(jl_objective), :]
output_folder = init_folder(parameters, input_dict, suffix='_c' + str(c))
with open(join(output_folder, 'config_model.yaml'), 'w') as outfile:
yaml.dump(parameters, outfile, default_flow_style=False, sort_keys=False)
jl_locations = retrieve_location_dict_jl(jl_selected_seed, parameters, input_dict, indices)
retrieve_site_data(parameters, input_dict, output_folder, jl_locations, jl_objective_seed)
else: #'rand'
seed = parameters['solution_method']['HEU']['seed']
for i in range(jl_selected.shape[0]):
output_folder = init_folder(parameters, input_dict, suffix='_c' + str(c) + '_seed' + str(seed))
seed += 1
with open(join(output_folder, 'config_model.yaml'), 'w') as outfile:
yaml.dump(parameters, outfile, default_flow_style=False, sort_keys=False)
jl_selected_seed = jl_selected[i, :]
jl_objective_seed = jl_objective[i]
jl_locations = retrieve_location_dict_jl(jl_selected_seed, parameters, input_dict, indices)
retrieve_site_data(c, parameters, input_dict, output_folder, jl_locations, jl_objective_seed)
start = time.time()
jl_selected, jl_objective, jl_traj = fn(jl_dict['index_dict'],
jl_dict['deployment_dict'],
jl_dict['criticality_matrix'],
c,
parameters['solution_method']['HEU']['neighborhood'],
parameters['solution_method']['HEU']['no_iterations'],
parameters['solution_method']['HEU']['no_epochs'],
parameters['solution_method']['HEU']['initial_temp'],
parameters['solution_method']['HEU']['no_runs'],
parameters['solution_method']['HEU']['algorithm'])
end = time.time()
noruns = parameters['solution_method']['HEU']['no_runs']
dt = (end-start)/noruns
print(f'Average time per run: {dt}')
output_folder = init_folder(parameters, input_dict, suffix='_c' + str(c) + '_MIRSA')
with open(join(output_folder, 'config_model.yaml'), 'w') as outfile:
yaml.dump(parameters, outfile, default_flow_style=False, sort_keys=False)
pickle.dump(jl_selected, open(join(output_folder, 'solution_matrix.p'), 'wb'), protocol=4)
pickle.dump(jl_objective, open(join(output_folder, 'objective_vector.p'), 'wb'), protocol=4)
pickle.dump(jl_traj, open(join(output_folder, 'trajectory_matrix.p'), 'wb'), protocol=4)
if c == parameters['solution_method']['HEU']['c'][0]:
pickle.dump(input_dict['criticality_data'], open(join(output_folder, 'criticality_matrix.p'), 'wb'), protocol=4)
#if parameters['solution_method']['HEU']['which_sol'] == 'max':
# jl_objective_seed = max(jl_objective)
# jl_selected_seed = jl_selected[argmax(jl_objective), :]
# output_folder = init_folder(parameters, input_dict, suffix='_c' + str(c))
# with open(join(output_folder, 'config_model.yaml'), 'w') as outfile:
# yaml.dump(parameters, outfile, default_flow_style=False, sort_keys=False)
# jl_locations = retrieve_location_dict_jl(jl_selected_seed, parameters, input_dict, indices)
# retrieve_site_data(parameters, input_dict, output_folder, jl_locations, jl_objective_seed)
#else: #'rand'
# seed = parameters['solution_method']['HEU']['seed']
# for i in range(jl_selected.shape[0]):
# output_folder = init_folder(parameters, input_dict, suffix='_c' + str(c) + '_seed' + str(seed) + '_MIRSA')
# seed += 1
# with open(join(output_folder, 'config_model.yaml'), 'w') as outfile:
# yaml.dump(parameters, outfile, default_flow_style=False, sort_keys=False)
# jl_selected_seed = jl_selected[i, :]
# jl_objective_seed = jl_objective[i]
# jl_locations = retrieve_location_dict_jl(jl_selected_seed, parameters, input_dict, indices)
# retrieve_site_data(c, parameters, input_dict, output_folder, jl_locations, jl_objective_seed)
else:
raise ValueError(' This solution method is not available. Retry.')
......
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment