def create_output(self):
dict_of_variables = {
'production_variables': self.production_variables,
'inventory_variables': self.inventory_variables
}
output_df = write_outputs(dict_of_variables, attr='x')
write_to_csv(output_df)
# ================== Optimization ==================
if model_params['write_lp']:
logger.info('Writing the lp file!')
model.export_as_lp('./{}.lp'.format(model.name))
logger.info('Optimization starts!')
# If CPLEX is installed locally, we can use that to solve the problem.
# Otherwise, we can use DOcplexcloud. For docloud solve, we need valid 'url' and 'key'.
# Note, that if 'url' and 'key' parameters are present,
# the solve will be started on DOcplexcloud even if CPLEX is available.
# I've provided more info on this in optimization_model_docplex.py
# For now, a simple way to handle local or docloud solve
if model_params['cplex_cloud']:
model.solve(url=model_params['url'], key=model_params['api_key'])
else:
model.solve()
if model.solve_details.status == 'optimal':
logger.info('The solution is optimal and the objective value '
'is ${:,.2f}!'.format(model.objective_value))
# ================== Output ==================
dict_of_variables = {'production_variables': production_variables,
'inventory_variables': inventory_variables}
output_df = process_data.write_outputs(dict_of_variables, attr='solution_value')
helper.write_to_csv(output_df)
logger.info('Outputs are written to csv!')
total_production_cost = grb.quicksum(row['production_cost'] * production_variables[index]
for index, row in input_df_dict['input_data'].iterrows())
objective = total_holding_cost + total_production_cost
model.setObjective(objective, grb.GRB.MINIMIZE)
logger.info('Model creation time in sec: {:.4f}'.format(time() - start))
# ================== Optimization ==================
if model_params['write_lp']:
logger.info('Writing the lp file!')
model.write(model.ModelName + '.lp')
if not model_params['write_log']:
model.setParam('OutputFlag', 0)
logger.info('Optimization starts!')
model.optimize()
if model.Status == grb.GRB.OPTIMAL:
logger.info('The solution is optimal and the objective value '
'is ${:,.2f}!'.format(model.objVal))
# ================== Output ==================
dict_of_variables = {'production_variables': production_variables,
'inventory_variables': inventory_variables}
output_df = process_data.write_outputs(dict_of_variables, attr='x')
helper.write_to_csv(output_df)
logger.info('Outputs are written to csv!')