def solve(localsolver, solvername, instance,
logfilename='logfile_loadobjective.log',
get_suffixes=True, solver_options=None, outputfile=None):
# Wall time - clock starts.
starttime_modelsolve = time.time()
if localsolver:
solver = SolverFactory(solvername)
# Configure with solver options
# file_print_levels (Output Level-of-Detail):
# 4 for just # of iterations, and final objective, infeas,etc. values
# 6 for summary information about all iterations, but not variable values
# 8 for variable values at all iterations
# 10 for all iterations
if solver_options:
for k, v in solver_options.items():
solver.options[k] = v
solver.options['OF_mumps_mem_percent'] = '5' # "OF_" prefix signals to Pyomo to create a temporary options file
solver.options['OF_output_file'] = outputfile
if get_suffixes:
instance.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT)
instance.ipopt_zL_out = pyo.Suffix(direction=pyo.Suffix.IMPORT)
instance.ipopt_zU_out = pyo.Suffix(direction=pyo.Suffix.IMPORT)
setattr(instance, 'lambda', pyo.Suffix(direction=pyo.Suffix.IMPORT)) # use setattr because 'lambda' is reserved keyword
try:
results = solver.solve(instance, tee=True, symbolic_solver_labels=True,
keepfiles=False, logfile=logfilename)
except pyutilib.common._exceptions.ApplicationError:
traceback.print_exc()
return None
else:
opt = SolverFactory("cbc")
solver_manager = SolverManagerFactory('neos')
instance.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT)
instance.rc = pyo.Suffix(direction=pyo.Suffix.IMPORT)
instance.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT_EXPORT)
# self.instance.slack = pyo.Suffix(direction=pyo.Suffix.IMPORT)
opt.options["display_width"] = 170
opt.options["display"] = '_varname, _var.rc, _var.lb, _var, _var.ub, _var.slack'
results = solver_manager.solve(instance, opt=opt, solver=solvername, logfile=logfilename)
results.write()
# Wall time - clock stops.
_endtime_modelsolve = time.time()
timefor_modelsolve = _endtime_modelsolve - starttime_modelsolve
logger.info('*solving done* <- it took %f seconds>' % timefor_modelsolve)
feasible = check_whether_feasible(instance, results)
return instance, results, feasible
return (
sum(
modelo.tiempo_produccion[planta, producto] *
modelo.produccion[producto]
for producto in modelo.productos
) <= modelo.tiempo_disponible[planta]
)
modelo.disponibilidad_tiempo = Constraint(
modelo.plantas, rule=funcion_disponibilidad_tiempo
)
def funcion_ganancias(modelo):
return sum(
modelo.ganancia_unitaria[producto] * modelo.produccion[producto]
for producto in modelo.productos
)
modelo.ganancias = Objective(rule=funcion_ganancias, sense=maximize)
instancia = modelo.create_instance("wyndor.dat")
opt = SolverFactory("cbc")
solver_manager = SolverManagerFactory('neos')
resultados = solver_manager.solve(instancia, opt=opt) # resolver en la nube
# resultados = opt.solve(instancia) # resolver localmente
instancia.display()
from pyomo.environ import *
from pyomo.opt import SolverFactory, SolverManagerFactory
from DiseaseEstimation import model
# create the instance
instance = model.create('DiseaseEstimation.dat')
# define the solver and its options
solver = 'ipopt'
opt = SolverFactory(solver)
if opt is None:
raise ValueError, "Problem constructing solver `" + str(solver)
opt.set_options('max_iter=2')
# create the solver manager
solver_manager = SolverManagerFactory('serial')
# solve
results = solver_manager.solve(instance, opt=opt, tee=True, timelimit=None)
instance.load(results)
# display results
display(instance)
def solve_model(model): # Custom Solve Method
import datetime
trial_pool = {
'sample1':{'solution_file':None,'aspiration':None},
'sample2':{'solution_file':None,'aspiration':None},
'sample3':{'solution_file':None,'aspiration':None}
}
#model1 = copy.deepcopy(model)
#model2 = copy.deepcopy(model)
#model3 = copy.deepcopy(model)
print ("success! \n loading solver......")
mip_gap = 0.005
solver_timeout = 300
number_of_trials = 2
solver_sh = 'cplex' #initialization Setting
engage_neos = True #initialization Setting
j = 1
timeout_arguments = {'cplex':'timelimit','cbc':'sec'}
gap_arguments = {'cplex':'mipgap','cbc':'ratio'} # Cplex Local Executable Take : "mip_tolerance_mipgap", mipgap is for neos version
# NEOS Server Library Dependency
# pyro4
# suds
# openopt
while j < number_of_trials + 1:
from pyomo.opt import SolverFactory, SolverManagerFactory
#model.pprint()
opt = SolverFactory(solver_sh, solver_io = 'lp')
print ('\ninterfacing solver shell :',solver_sh)
if engage_neos:
solver_manager = SolverManagerFactory('neos')
opt.options[timeout_arguments[solver_sh]]= solver_timeout
opt.options[gap_arguments[solver_sh]] = mip_gap
#opt.symbolic_solver_labels=True
#opt.options['slog'] = 1
#opt.enable = 'parallel'
print ('\tsolver options >> \n\n\tTolerance Limits:\n\tmip_gap = %s \n\ttimeout = %s'%(str(mip_gap),str(solver_timeout)))
print ("\nProcessing Trial Number :",j)
print ("\nJob Triggered at :",str(datetime.datetime.now()))
print ('\ngenerating production plan...... !! please wait !! \n to interrupt press ctrl+C\n')
try:
if engage_neos:
results = solver_manager.solve(model,opt = opt, tee= True)
else:
opt.options['threads'] = 3
results = opt.solve(model) #,tee=True) # Method Load Solutions is not available in pyomo versions less than 4.x
except:
j = j+1
mip_gap = (j-1)*mip_gap
solver_sh = 'cplex'
engage_neos = True
continue
#results.write(filename='results'+str(datetime.date.today())+'.json',format = 'json')
#print (results)
if str(results['Solver'][0]['Termination condition']) in ['infeasible','maxTimeLimit','maxIterations','intermediateNonInteger']:
j = j+1
mip_gap = (j-1)*mip_gap
solver_sh = 'cplex'
engage_neos = True
if j == number_of_trials + 1:
#print (results['Problem'])
#print (results['Solver'])
raise AssertionError("Solver Failed with Termination Status : %s"%(str(results['Solver'][0]['Termination condition'])))
exit(0)
print ('Terminated by:',str(results['Solver'][0]['Termination condition']))
print ("\n\nRetrying...\n\n")
else:
print ("solution captured")
model.solutions.store_to(results)
#post_process_results()
break
print (results['Problem'])
print (results['Solver'])
print ("\nSolution Retrived at:",str(datetime.datetime.now()))
return [model,results]
def solve_model(model, p_summary=False, p_log=False): # Custom Solve Method
import datetime
# Choose the best solution from the trial pool
# trial_pool = {
# 'sample1':{'solution_file':None,'aspiration':None},
# 'sample2':{'solution_file':None,'aspiration':None},
# 'sample3':{'solution_file':None,'aspiration':None}
# }
#model1 = copy.deepcopy(model)
#model2 = copy.deepcopy(model)
#model3 = copy.deepcopy(model)
import configparser
config = configparser.ConfigParser()
config.read('../start_config.ini')
#initialization Setting
mip_gap = float(config['solver']['mip_gap'])
solver_timeout = int(config['solver']['solver_timeout'])
solver_sh = config['solver']['solver_sh']
number_of_trials = int(config['solver']['number_of_trials'])
engage_neos = bool(int(
config['solver']['engage_neos'])) #initialization Setting
threads = int(config['solver']['threads'])
if solver_sh not in set(["cbc", "cplex"]):
raise AssertionError("Invalid Solver!, Error Code : 200B")
logger.info("success! \n loading solver......")
j = 1
timeout_arguments = {'cplex': 'timelimit', 'cbc': 'sec'}
gap_arguments = {
'cplex': 'mipgap',
'cbc': 'ratio'
} # Cplex Local Executable will take : "mip_tolerance_mipgap", mipgap is for neos version
# NEOS Server Library Dependency
# pyro4
# suds
# openopt
while j < number_of_trials + 1:
from pyomo.opt import SolverFactory, SolverManagerFactory
#model.pprint()
opt = SolverFactory(solver_sh, solver_io='lp')
# print ('\ninterfacing solver shell :',solver_sh)
logger.debug('interfacing solver shell : %s' % (solver_sh))
if engage_neos:
solver_manager = SolverManagerFactory('neos')
opt.options[timeout_arguments[solver_sh]] = solver_timeout
opt.options[gap_arguments[solver_sh]] = mip_gap
#opt.symbolic_solver_labels=True
#
#opt.enable = 'parallel'
print(
'\tsolver options >> \n\n\tTolerance Limits:\n\tmip_gap = %s \n\ttimeout = %s'
% (str(mip_gap), str(solver_timeout)))
# print ("\nProcessing Trial Number :",j)
# print ("\nJob Triggered at :",str(datetime.datetime.now()))
print(
'\ngenerating solution ...... !! please wait !! \n to interrupt press ctrl+C\n'
)
# logger.debug('\tsolver options >> \n\n\tTolerance Limits:\n\tmip_gap = %s \n\ttimeout = %s'%(str(mip_gap),str(solver_timeout)))
logger.debug('Processing Trial Number :%d' % (j))
logger.debug("Job Triggered")
try:
if engage_neos:
p_log = False
results = solver_manager.solve(model, opt=opt, tee=True)
else:
opt.options['threads'] = threads
if p_log:
opt.options['slog'] = 1
results = opt.solve(
model
) # Method Load Solutions is not available in pyomo versions less than 4.x
except:
j = j + 1
mip_gap = (j - 1) * mip_gap
solver_sh = 'cplex'
engage_neos = True
continue
#results.write(filename='results'+str(datetime.date.today())+'.json',format = 'json')
#print (results)
if str(results['Solver'][0]['Termination condition']) in [
'infeasible', 'maxTimeLimit', 'maxIterations',
'intermediateNonInteger', 'unbounded'
]:
j = j + 1
mip_gap = (j - 1) * mip_gap
solver_sh = 'cplex'
engage_neos = True
if j == number_of_trials + 1:
#print (results['Problem'])
#print (results['Solver'])
raise AssertionError(
"Solver Failed with Termination Status : %s \nError Code : 200C"
% (str(results['Solver'][0]['Termination condition'])))
exit(0)
# print ('Terminated by:',str(results['Solver'][0]['Termination condition']))
# print ("\n\nRetrying...\n\n")
logger.info('Terminated by:',
str(results['Solver'][0]['Termination condition']))
logger.info("\n\nRetrying...\n\n")
else:
# print ("SUCCESS: Solution Captured!")
logger.info("Solution Captured!")
model.solutions.store_to(results)
#post_process_results()
break
if p_summary:
print(results['Problem'])
# print (results['Solver'])
# print ("\nSolution Retrived at:",str(datetime.datetime.now()))
# logger.info("Solution Retrived")
return [model, results]
from pyomo.core import *
from pyomo.opt import SolverFactory, SolverManagerFactory
from DiseaseEstimation import model
# create the instance
instance = model.create('DiseaseEstimation.dat')
# define the solver and its options
solver = 'ipopt'
opt = SolverFactory( solver )
if opt is None:
raise ValueError, "Problem constructing solver `"+str(solver)
opt.set_options('max_iter=2')
# create the solver manager
solver_manager = SolverManagerFactory( 'serial' )
# solve
results = solver_manager.solve(instance, opt=opt, tee=True, timelimit=None)
instance.load(results)
# display results
display(instance)
modelo.ganancias = Objective(rule=funcion_ganancias, sense=maximize)
def funcion_capacidad_doug(modelo):
return modelo.produccion["madera"] <= 6
modelo.capacidad_doug = Constraint(rule=funcion_capacidad_doug)
def funcion_capacidad_linda(modelo):
return modelo.produccion["aluminio"] <= 4
modelo.capacidad_linda = Constraint(rule=funcion_capacidad_linda)
def funcion_capacidad_bob(modelo):
return (
modelo.produccion["madera"]*6 +
modelo.produccion["aluminio"]*8 <= 48
)
modelo.capacidad_bob = Constraint(rule=funcion_capacidad_bob)
opt = SolverFactory("cbc")
solver_manager = SolverManagerFactory('neos')
resultados = solver_manager.solve(modelo, opt=opt) # resolver en la nube
# resultados = opt.solve(modelo) # resolver localmente
modelo.display()
