whiskas_blending

"""
The Simplified Whiskas Model Python Formulation for the PuLP Modeller

Authors: Antony Phillips, Dr Stuart Mitchell  2007
"""

# Import PuLP modeler functions
from pulp import *

# Create the 'prob' variable to contain the problem data
prob = LpProblem("The Whiskas Problem", LpMinimize)

# The 2 variables Beef and Chicken are created with a lower limit of zero
x1 = LpVariable("ChickenPercent", 0, None, LpInteger)
x2 = LpVariable("BeefPercent", 0)

# The objective function is added to 'prob' first
prob += 0.013*x1 + 0.008*x2, "Total Cost of Ingredients per can"

# The five constraints are entered
prob += x1 + x2 == 100, "PercentagesSum"
prob += 0.100*x1 + 0.200*x2 >= 8.0, "ProteinRequirement"
prob += 0.080*x1 + 0.100*x2 >= 6.0, "FatRequirement"
prob += 0.001*x1 + 0.005*x2 <= 2.0, "FibreRequirement"
prob += 0.002*x1 + 0.005*x2 <= 0.4, "SaltRequirement"

# The problem data is written to an .lp file
prob.writeLP("WhiskasModel.lp")

# The problem is solved using PuLP's choice of Solver
prob.solve()

# The status of the solution is printed to the screen
print("Status:", LpStatus[prob.status])

# Each of the variables is printed with it's resolved optimum value
for v in prob.variables():
    print(v.name, "=", v.varValue)

# The optimised objective function value is printed to the screen
print("Total Cost of Ingredients per can = ", value(prob.objective))