Note
Click here to download the full example code
Superheroes Factory#
This example demostrates how to use the low-sugar in combination with mlflow to solve a simple supply chain problem.
# sphinx_gallery_thumbnail_path = '_static/superheroes.png'
Problem Description#
Populate description here later.
Let’s start by doing some useful imports.
from itertools import product
from typing import Dict
import gurobipy as gp
Tracking an Optimization Experiment#
Add description here.
def build_indices(data: Dict) -> Dict:
customers = list(data["demand"].keys())
accessories = list(data["initial_inventory"].keys())
products = list(data["recipe"].keys())
max_day = max(demand_details["date"] for demand_details in data["demand"].values())
days = range(1, max_day + 1)
customer_dates = {
(customer, day)
for customer in customers
for day in range(
data["demand"][customer]["date"],
min(
data["demand"][customer]["date"] + data["max_delay"],
max_day + 1,
),
)
}
indices = {
"customers": customers,
"customer_dates": customer_dates,
"max_day": max_day,
"days": days,
"accessories": accessories,
"products": products,
}
return indices
def build_variables(model: gp.Model, indices: Dict):
dispatch = model.addVars(indices["customer_dates"], vtype="B", name="dispatch")
inventory = model.addVars(
product(indices["accessories"], indices["days"]),
vtype="C",
name="inventory",
)
from_inventory = model.addVars(
product(
indices["customers"],
indices["products"],
indices["accessories"],
indices["days"],
),
vtype="I",
name="from_inventory",
)
from_factory = model.addVars(
product(
indices["customers"],
indices["products"],
indices["accessories"],
indices["days"],
),
vtype="I",
name="from_factory",
)
to_inventory = model.addVars(
product(indices["accessories"], indices["days"]),
vtype="C",
name="to_inventory",
)
extra_production = model.addVars(
product(
indices["customers"],
indices["products"],
indices["accessories"],
indices["days"],
),
vtype="C",
name="extra_production",
)
variables = {
"dispatch": dispatch,
"inventory": inventory,
"from_inventory": from_inventory,
"from_factory": from_factory,
"to_inventory": to_inventory,
"extra_production": extra_production,
}
return variables
def build_constraints(model: gp.Model, indices: Dict, variables: Dict) -> gp.Model:
to_inventory = variables["to_inventory"]
from_factory = variables["from_factory"]
from_inventory = variables["from_inventory"]
inventory = variables["inventory"]
dispatch = variables["dispatch"]
extra_production = variables["extra_production"]
production = data["production"]
initial_inventory = data["initial_inventory"]
inventory_capacity = data["inventory_capacity"]
demand = data["demand"]
recipe = data["recipe"]
for accessory, day in product(indices["accessories"], indices["days"]):
if str(day) in production[accessory]:
model.addConstr(
to_inventory[accessory, day]
+ from_factory.sum("*", "*", accessory, day)
== production[accessory][str(day)],
name=f"production_allocation_{accessory}_{day}",
)
else:
model.addConstr(
to_inventory[accessory, day]
+ from_factory.sum("*", "*", accessory, day)
== 0,
name=f"production_allocation_{accessory}_{day}",
)
for accessory, day in product(indices["accessories"], indices["days"]):
if day == 1:
model.addConstr(
inventory[accessory, day]
== initial_inventory[accessory]
+ to_inventory[accessory, day]
- from_inventory.sum("*", "*", accessory, day),
name=f"keeping_track_of_inventories_{accessory}_{day}",
)
else:
model.addConstr(
inventory[accessory, day]
== inventory[accessory, day - 1]
+ to_inventory[accessory, day]
- from_inventory.sum("*", "*", accessory, day),
name=f"keeping_track_of_inventories_{accessory}_{day}",
)
for accessory, day in product(indices["accessories"], indices["days"]):
model.addConstr(
inventory[accessory, day] <= inventory_capacity[accessory],
name=f"inventory_capacity_{accessory}_{day}",
)
for customer in indices["customers"]:
model.addConstr(
dispatch.sum(customer, "*") == 1, name=f"customer_served_{customer}"
)
for prod, accessory in product(indices["products"], indices["accessories"]):
for customer, day in indices["customer_dates"]:
model.addConstr(
from_inventory[customer, prod, accessory, day]
+ from_factory[customer, prod, accessory, day]
+ extra_production[customer, prod, accessory, day]
== dispatch[customer, day]
* demand[customer][prod]
* recipe[prod][accessory],
name=f"demand_satisfaction_{customer}_{prod}_{accessory}_{day}",
)
return model
def build_objective(
model: gp.Model, indices: Dict, variables: Dict, demand: Dict
) -> gp.Model:
dispatch = variables["dispatch"]
extra_production = variables["extra_production"]
delay_penalty_costs = gp.quicksum(
gp.quicksum(
day * dispatch[customer, day]
for customer, day in dispatch
if customer == customer_
)
- demand[customer_]["date"]
for customer_ in indices["customers"]
)
cost_per_extra_prod = 2
extra_production_costs = cost_per_extra_prod * extra_production.sum()
total_cost = delay_penalty_costs + extra_production_costs
model.setObjective(total_cost, gp.GRB.MINIMIZE)
return model
def build(data: Dict) -> gp.Model:
model = gp.Model(name="supply_chain_blended")
indices = build_indices(data)
variables = build_variables(model, indices)
model = build_constraints(model, indices, variables)
model = build_objective(model, indices, variables, demand=data["demand"])
model.update()
return model
Tracking an Optimization Experiment#
Add description here.
# import sys; sys.path.append("/Users/Juan.ChaconLeon/opt/opt-sugar/src") # when running locally
from opt_sugar import low_sugar
# Setting the experiment
import datetime
import logging
logging.getLogger("mlflow").setLevel(logging.CRITICAL) # Can be set DEBUG
import mlflow
from mlflow import MlflowException
experiment_name = f"superheros_blended_{datetime.datetime.now().strftime('%Y_%m_%d')}"
try:
experiment_id = mlflow.create_experiment(name=experiment_name)
except MlflowException:
experiment_id = mlflow.get_experiment_by_name(name=experiment_name).experiment_id
Tracking an Optimization Experiment#
Add description here.
import requests
import matplotlib.pyplot as plt
with mlflow.start_run(experiment_id=experiment_id):
examples_path = (
"https://raw.githubusercontent.com/juandados/opt-sugar/main/examples/data"
)
data = requests.get(f"{examples_path}/supply_chain_blended_mini_toy.json").json()
opt_model = low_sugar.Model(build)
result = opt_model.optimize(data=data)
from utils.supply_chain import plot_supply_chain
plot_supply_chain(data=data, solution=result['vars'])
plt.show()
model_info = mlflow.sklearn.log_model(opt_model, "supply_chain_blended")
Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64)
Thread count: 1 physical cores, 2 logical processors, using up to 2 threads
Optimize a model with 30 rows, 58 columns and 108 nonzeros
Model fingerprint: 0x67d2fc4e
Variable types: 24 continuous, 34 integer (2 binary)
Coefficient statistics:
Matrix range [1e+00, 2e+00]
Objective range [1e+00, 2e+00]
Bounds range [1e+00, 1e+00]
RHS range [1e+00, 2e+01]
Found heuristic solution: objective 12.0000000
Presolve removed 30 rows and 58 columns
Presolve time: 0.00s
Presolve: All rows and columns removed
Explored 0 nodes (0 simplex iterations) in 0.00 seconds (0.00 work units)
Thread count was 1 (of 2 available processors)
Solution count 2: 0 12
Optimal solution found (tolerance 1.00e-04)
Best objective 0.000000000000e+00, best bound 0.000000000000e+00, gap 0.0000%
Total running time of the script: ( 0 minutes 1.894 seconds)