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optimization_model_pulp.py
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import logging
import pulp
from helper import write_to_csv
from parameters import model_params
from process_data import write_outputs
__author__ = 'Ehsan Khodabandeh'
__version__ = '1.1'
# ====================================
logger = logging.getLogger(__name__ + ': ')
# Pulp addConstraint function doesn't return the constraint object.
# So, to have a consistent object, we return it ourselves.
# Rather than a function, you can also define this as a method on OptimizationModel class
def add_constr(model, constraint):
model.addConstraint(constraint)
return constraint
class OptimizationModel(object):
def __init__(self, input_data, input_params):
self.input_data = input_data
self.input_params = input_params
self.model = pulp.LpProblem(name='prod_planning', sense=pulp.LpMinimize)
self._create_decision_variables()
self._create_main_constraints()
self._set_objective_function()
# ================== Decision variables ==================
def _create_decision_variables(self):
self.production_variables = pulp.LpVariable.dicts(name='X', indexs=self.input_data.index,
lowBound=0, cat=pulp.LpContinuous)
self.inventory_variables = pulp.LpVariable.dicts(name='I', indexs=self.input_data.index,
lowBound=0, cat=pulp.LpContinuous)
# Alternative way of creating the variables:
# self.production_variables = {
# index: pulp.LpVariable(name='X_' + str(row['period']),
# lowBound=0, cat=pulp.LpContinuous)
# for index, row in self.input_data.iterrows()}
#
# self.inventory_variables = {
# index: pulp.LpVariable(name='I_' + str(row['period']),
# lowBound=0, cat=pulp.LpContinuous)
# for index, row in self.input_data.iterrows()}
# ================== Constraints ==================
def _create_main_constraints(self):
# Depending on what you need, you may want to consider creating any of
# the expressions (constraints or objective terms) as an attribute of
# the OptimizationModel class (e.g. self.inv_balance_constraints).
# That way if, for example, at the end of the optimization you need to check
# the slack variables of certain constraints, you know they already exists in your model
# ================== Inventory balance constraints ==================
self.inv_balance_constraints = {
period: add_constr(self.model, pulp.LpConstraint(
e=self.inventory_variables[period - 1] + self.production_variables[period]
- self.inventory_variables[period],
sense=pulp.LpConstraintEQ,
name='inv_balance' + str(period),
rhs=value.demand))
for period, value in self.input_data.iloc[1:].iterrows()}
# inv balance for first period
self.first_period_inv_balance_constraints = add_constr(self.model, pulp.LpConstraint(
e=self.production_variables[0] - self.inventory_variables[0],
sense=pulp.LpConstraintEQ,
name='inv_balance0',
rhs=self.input_data.iloc[0].demand - self.input_params['initial_inventory']))
# ================== Production capacity constraints ==================
self.production_capacity_constraints = {
index: add_constr(self.model, pulp.LpConstraint(
e=value,
sense=pulp.LpConstraintLE,
name='prod_cap_month_' + str(index),
rhs=self.input_data.iloc[index].production_capacity))
for index, value in self.production_variables.items()}
# ================== Costs and objective function ==================
def _set_objective_function(self):
# Similar to constraints, saving the costs expressions as attributes
# can give you the chance to retrieve their values at the end of the optimization
self.total_holding_cost = self.input_params['holding_cost'] * pulp.lpSum(self.inventory_variables)
self.total_production_cost = pulp.lpSum(row['production_cost'] * self.production_variables[index]
for index, row in self.input_data.iterrows())
objective = self.total_holding_cost + self.total_production_cost
self.model.setObjective(objective)
# ================== Optimization ==================
def optimize(self):
"""
Default solver is 'cbc' unless solver is set to something else.
You may need to provide a path for any of the solvers using 'path' argument.
"""
_solver = None
s_name = model_params['solver']
w_log = model_params['write_log']
disp_log = model_params['display_log']
mip_gap = model_params['mip_gap']
tl = model_params['time_limit']
if model_params['write_lp']:
logger.info('Writing the lp file!')
self.model.writeLP(self.model.name + '.lp')
if not s_name or s_name == 'cbc':
_solver = pulp.PULP_CBC_CMD(keepFiles=w_log, msg=disp_log, gapRel=mip_gap, timeLimit=tl)
elif s_name == 'gurobi':
# One can use GUROBI_CMD like CPLEX_CMD and pass mip_gap and time_limit as options
_solver = pulp.GUROBI(msg=w_log, gapRel=mip_gap, timeLimit=tl)
elif s_name == 'cplex':
_solver = pulp.CPLEX_CMD(keepFiles=w_log, msg=disp_log, gapRel=mip_gap, timelimit=tl)
elif s_name == 'glpk':
# Read more about glpk options: https://en.wikibooks.org/wiki/GLPK/Using_GLPSOL
options = []
if mip_gap:
set_mip_gap = f'--mipgap {mip_gap}'
options.append(set_mip_gap)
_solver = pulp.GLPK_CMD(keepFiles=w_log, msg=disp_log, options=options, timeLimit=tl)
elif s_name == 'xpress':
_solver = pulp.XPRESS(keepFiles=w_log, msg=disp_log, gapRel=mip_gap, timeLimit=tl)
logger.info('Optimization starts!')
self.model.solve(solver=_solver)
if self.model.status == pulp.LpStatusOptimal:
logger.info(f'The solution is optimal and the objective value '
f'is ${self.model.objective.value():,.2f}')
# ================== Output ==================
def create_output(self):
dict_of_variables = {'production_variables': self.production_variables,
'inventory_variables': self.inventory_variables}
output_df = write_outputs(dict_of_variables)
write_to_csv(output_df)