Profit Maximization Problem with Pulp

pulp_optimization_1.py

import pulp

# Code for this walkthrough: http://benalexkeen.com/linear-programming-with-python-and-pulp-part-3/

model = pulp.LpProblem("Profit maximising problem", pulp.LpMaximize)

# Declare variables

A = pulp.LpVariable('A', lowBound=0, cat='Integer')
B = pulp.LpVariable('B', lowBound=0, cat='Integer')


# Objective function
model += 30000 * A + 45000 * B, "Profit"

# Constraints
model += 3 * A + 4 * B <= 30
model += 5 * A + 6 * B <= 60
model += 1.5 * A + 3 * B <= 21

# Solve our problem
model.solve()
print(pulp.LpStatus[model.status])


# Print our decision variable values
print("Production of Car A = {}".format(A.varValue))
print("Production of Car B = {}".format(B.varValue))

# Print our objective function value
print(pulp.value(model.objective))