LinOptimointi.py

# Linear Optimization
# Juhani Kaukoranta 1.3.2021
# "Advanced Engineering Mathematics", Sec 21.4 probl 5,s 1129,
from pulp import LpMaximize, LpProblem, LpStatus, LpVariable
# Create the model, LpMaximize or LpMinimize
model = LpProblem(name="small-problem", sense=LpMaximize)
# Initialize variables
# lamps type I, II, III
x1 = LpVariable(name="x1", lowBound=0)
x2 = LpVariable(name="x2", lowBound=0)
x3 = LpVariable(name="x3", lowBound=0)
# Add the constraints to the model
model += (3 * x1 + x2  <= 450,"metal supply")
model += (2*x2 + 3 * x3  <= 900,"plastic supply")
model += (2*x1 + x2 <= 350, "glass supply")
# Add the objective function to the model (object of optimization)
# object : total profit ( profit per lamp 10,8,5 USD)
model += 10*x1 + 8 * x2 + 5*x3
# Solve the problem
status = model.solve()
print(model) # show model
print(f"status: {model.status}, {LpStatus[model.status]}")
print(f"objective: {model.objective.value()}")
for var in model.variables():
    print(f"{var.name}: {var.value()}")