def GLOA_with_disjunctive_bounds(pyomo_model):
job_result = _JobResult()
pyomo_results = SolverFactory('gdpopt').solve(
pyomo_model,
tee=True,
strategy='GLOA',
mip_solver='gams',
mip_solver_args=dict(solver='cplex',
add_options=get_base_gams_options_list()),
nlp_solver='gams',
nlp_solver_args=dict(solver='baron',
add_options=get_base_gams_options_list()),
minlp_solver='gams',
minlp_solver_args=dict(solver='baron',
add_options=get_base_gams_options_list()),
iterlim=300,
calc_disjunctive_bounds=True,
time_limit=options.time_limit)
job_result.solver_run_time = pyomo_results.solver.timing.total
job_result.pyomo_solver_status = pyomo_results.solver.status
job_result.iterations = pyomo_results.solver.iterations
job_result.termination_condition = pyomo_results.solver.termination_condition
job_result.LB = pyomo_results.problem.lower_bound
job_result.UB = pyomo_results.problem.upper_bound
return job_result
def DICOPT(pyomo_model):
job_result = _JobResult()
try:
pyomo_results = SolverFactory('gams').solve(
pyomo_model,
tee=True,
# keepfiles=True,
solver='dicopt',
add_options=get_base_gams_options_list() +
[f'option reslim={options.time_limit};'])
except ValueError as e:
# Handle GAMS interface complaining about using DICOPT for MIPs
if 'GAMS writer passed solver (dicopt) unsuitable for model type (mip)' in str(
e):
pyomo_results = SolverFactory('gams').solve(
pyomo_model,
tee=True,
solver='cplex',
add_options=get_base_gams_options_list() +
[f'option reslim={options.time_limit};'])
else:
raise
job_result.solver_run_time = pyomo_results.solver.user_time
job_result.pyomo_solver_status = pyomo_results.solver.status
job_result.termination_condition = pyomo_results.solver.termination_condition
job_result.LB = pyomo_results.problem.lower_bound
job_result.UB = pyomo_results.problem.upper_bound
return job_result
def LOA(pyomo_model):
job_result = _JobResult()
pyomo_results = SolverFactory('gdpopt').solve(
pyomo_model,
tee=True,
mip_solver='gams',
mip_solver_args=dict(solver='cplex',
add_options=get_base_gams_options_list()),
nlp_solver='gams',
nlp_solver_args=dict(solver='ipopth',
add_options=get_base_gams_options_list()),
minlp_solver='gams',
minlp_solver_args=dict(solver='dicopt',
add_options=get_base_gams_options_list()),
iterlim=300,
time_limit=options.time_limit)
job_result.solver_run_time = pyomo_results.solver.timing.total
job_result.pyomo_solver_status = pyomo_results.solver.status
job_result.iterations = pyomo_results.solver.iterations
job_result.termination_condition = pyomo_results.solver.termination_condition
job_result.LB = pyomo_results.problem.lower_bound
job_result.UB = pyomo_results.problem.upper_bound
return job_result
def SCIP(pyomo_model):
job_result = _JobResult()
pyomo_results = SolverFactory('gams').solve(
pyomo_model,
tee=True,
# keepfiles=True,
solver='scip',
add_options=get_base_gams_options_list() +
[f'option reslim={options.time_limit};'])
job_result.solver_run_time = pyomo_results.solver.user_time
job_result.pyomo_solver_status = pyomo_results.solver.status
job_result.termination_condition = pyomo_results.solver.termination_condition
job_result.LB = pyomo_results.problem.lower_bound
job_result.UB = pyomo_results.problem.upper_bound
return job_result