""" 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)