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"""
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))
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