42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
"""
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The Simplified Whiskas Model Python Formulation for the PuLP Modeller
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Authors: Antony Phillips, Dr Stuart Mitchell 2007
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"""
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# Import PuLP modeler functions
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from pulp import *
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# Create the 'prob' variable to contain the problem data
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prob = LpProblem("The Whiskas Problem",LpMinimize)
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# The 2 variables Beef and Chicken are created with a lower limit of zero
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x1=LpVariable("ChickenPercent",0,None,LpInteger)
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x2=LpVariable("BeefPercent",0)
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# The objective function is added to 'prob' first
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prob += 0.013*x1 + 0.008*x2, "Total Cost of Ingredients per can"
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# The five constraints are entered
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prob += x1 + x2 == 100, "PercentagesSum"
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prob += 0.100*x1 + 0.200*x2 >= 8.0, "ProteinRequirement"
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prob += 0.080*x1 + 0.100*x2 >= 6.0, "FatRequirement"
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prob += 0.001*x1 + 0.005*x2 <= 2.0, "FibreRequirement"
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prob += 0.002*x1 + 0.005*x2 <= 0.4, "SaltRequirement"
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# The problem data is written to an .lp file
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prob.writeLP("WhiskasModel.lp")
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# The problem is solved using PuLP's choice of Solver
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prob.solve()
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# The status of the solution is printed to the screen
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print("Status:", LpStatus[prob.status])
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# Each of the variables is printed with it's resolved optimum value
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for v in prob.variables():
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print(v.name, "=", v.varValue)
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# The optimised objective function value is printed to the screen
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print("Total Cost of Ingredients per can = ", value(prob.objective))
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