diff --git a/config_model.yml b/config_model.yml
index e9b99a3c5f7851c4cec5f9047cd5403941d062ff..8c5dbe22f4525d569ca72e8d3f8018ea30104695 100644
--- a/config_model.yml
+++ b/config_model.yml
@@ -26,33 +26,34 @@ siting_params:
BB:
# Branch & Bound
set: False
- mir: True
+ mir: False
solver: 'gurobi'
mipgap: 0.01
- timelimit: 3600
- threads: 1
+ timelimit: 43200
+ threads: 36
MIRSA:
# Simulated Annealing with Local Search
set: True
neighborhood: 1
- no_iterations: 1000
+ no_iterations: 2000
no_epochs: 500
initial_temp: 200.
- no_runs: 1
- algorithm: 'RSSA' # 'GLS' (Greedy Local Search), 'RSSA', 'SALS'
+ no_runs: 50
+ algorithm: 'SALS' # 'GLS' (Greedy Local Search), 'RSSA', 'SALS', 'STGHSA'
GRED_DET:
set: False
- no_runs: 5
+ no_runs: 10
+ p: 0
algorithm: 'TGH'
GRED_STO:
set: False
- p: 55 # 10% of 10150 locations
- no_runs: 100
+ p: 27 # 10% of 10150 locations
+ no_runs: 200
algorithm: 'STGH'
RAND:
# Random Search
set: False
- no_iterations: 50
- no_epochs: 500
- no_runs: 1
+ no_iterations: 1000
+ no_epochs: 1000
+ no_runs: 200
algorithm: 'RS'
diff --git a/src/main.py b/src/main.py
index f4ed0745d2f90e2e72ba166518aa7acd94646f83..e223b0780aac4fda719ca4140114e79dc3397c97 100644
--- a/src/main.py
+++ b/src/main.py
@@ -16,11 +16,12 @@ def parse_args():
parser = argparse.ArgumentParser(description='Command line arguments.')
- parser.add_argument('-c', '--threshold', type=int)
- parser.add_argument('-run_BB', '--run_BB_algorithm', type=bool, default=False)
- parser.add_argument('-run_MIRSA', '--run_MIRSA_algorithm', type=bool, default=False)
- parser.add_argument('-run_GRED_DET', '--run_GRED_DET_algorithm', type=bool, default=False)
- parser.add_argument('-run_GRED_STO', '--run_GRED_STO_algorithm', type=bool, default=False)
+ parser.add_argument('--c', type=int)
+ parser.add_argument('--run_BB', type=bool, default=False)
+ parser.add_argument('--run_MIRSA', type=bool, default=False)
+ parser.add_argument('--run_GRED_DET', type=bool, default=False)
+ parser.add_argument('--run_GRED_STO', type=bool, default=False)
+ parser.add_argument('--run_RAND', type=bool, default=False)
parsed_args = vars(parser.parse_args())
@@ -86,10 +87,11 @@ if __name__ == '__main__':
siting_parameters['solution_method']['MIRSA']['set'] = args['run_MIRSA']
siting_parameters['solution_method']['GRED_DET']['set'] = args['run_GRED_DET']
siting_parameters['solution_method']['GRED_STO']['set'] = args['run_GRED_STO']
+ siting_parameters['solution_method']['RAND']['set'] = args['run_RAND']
c = args['c']
- if not single_true([args['run_BB'], args['run_MIRSA'], args['run_GRED_DET'], args['GRED_STO']]):
+ if not single_true([args['run_BB'], args['run_MIRSA'], args['run_GRED_DET'], args['run_GRED_STO'], args['run_RAND']]):
raise ValueError(' More than one run selected in the argparser.')
if siting_parameters['solution_method']['BB']['set']:
@@ -97,7 +99,7 @@ if __name__ == '__main__':
custom_log(' BB chosen to solve the IP.')
params = siting_parameters['solution_method']['BB']
- output_folder = init_folder(model_parameters, c, f"_BB_MIR_str{params['mir']}")
+ output_folder = init_folder(model_parameters, c, f"_BB_MIR_{params['mir']}")
with open(join(output_folder, 'config_model.yaml'), 'w') as outfile:
yaml.dump(model_parameters, outfile, default_flow_style=False, sort_keys=False)
with open(join(output_folder, 'config_techs.yaml'), 'w') as outfile:
@@ -154,43 +156,27 @@ if __name__ == '__main__':
pickle.dump(jl_objective, open(join(output_folder, 'objective_vector.p'), 'wb'))
pickle.dump(jl_traj, open(join(output_folder, 'trajectory_matrix.p'), 'wb'))
- # elif siting_parameters['solution_method']['RAND']['set']:
- #
- # custom_log(' Locations to be chosen via random search.')
- # params = siting_parameters['solution_method']['RAND']
- #
- # if not isinstance(params['c'], list):
- # raise ValueError(' Values of c have to provided as list for the RAND set-up.')
- # if len(model_parameters['technologies']) > 1:
- # raise ValueError(' This method is currently implemented for one single technology only.')
- #
- # jl_dict = generate_jl_input(deployment_dict, site_coordinates)
- #
- # import julia
- # j = julia.Julia(compiled_modules=False)
- # from julia import Main
- # Main.include("jl/SitingHeuristics.jl")
- #
- # for c in params['c']:
- # print('Running heuristic for c value of', c)
- #
- # jl_selected, jl_objective = Main.main_RAND(jl_dict['deployment_dict'], criticality_data,
- # c, params['no_iterations'], params['no_runs'],
- # params['algorithm'])
- #
- # output_folder = init_folder(model_parameters, c, suffix='_RS')
- #
- # pickle.dump(jl_selected, open(join(output_folder, 'solution_matrix.p'), 'wb'))
- # pickle.dump(jl_objective, open(join(output_folder, 'objective_vector.p'), 'wb'))
- #
- # med_run = argsort(jl_objective)[params['no_runs']//2]
- # jl_selected_seed = jl_selected[med_run, :]
- # jl_objective_seed = jl_objective[med_run]
- #
- # jl_locations = retrieve_location_dict(jl_selected_seed, model_parameters, site_positions)
- # retrieve_site_data(model_parameters, deployment_dict, site_coordinates, capacity_factors_data,
- # criticality_data, site_positions, c, jl_locations, jl_objective_seed,
- # output_folder, benchmarks=False)
+ elif siting_parameters['solution_method']['RAND']['set']:
+
+ custom_log(' Locations to be chosen via random search.')
+ params = siting_parameters['solution_method']['RAND']
+
+ jl_dict = generate_jl_input(deployment_dict, site_coordinates)
+
+ import julia
+ j = julia.Julia(compiled_modules=False)
+ from julia import Main
+ Main.include("jl/SitingHeuristics.jl")
+
+
+ jl_selected, jl_objective = Main.main_RAND(jl_dict['deployment_dict'], criticality_data,
+ c, params['no_iterations'], params['no_runs'],
+ params['algorithm'])
+
+ output_folder = init_folder(model_parameters, c, suffix='_RS')
+
+ pickle.dump(jl_selected, open(join(output_folder, 'solution_matrix.p'), 'wb'))
+ pickle.dump(jl_objective, open(join(output_folder, 'objective_vector.p'), 'wb'))
elif siting_parameters['solution_method']['GRED_DET']['set']:
@@ -220,9 +206,6 @@ if __name__ == '__main__':
params = siting_parameters['solution_method']['GRED_STO']
custom_log(f" GRED_{params['algorithm']} chosen to solve the IP. Opening a Julia instance.")
- if not isinstance(params['c'], list):
- raise ValueError(' Values of c have to elements of a list for the heuristic set-up.')
-
jl_dict = generate_jl_input(deployment_dict, site_coordinates)
import julia