""" The Full Whiskas Model Python Formulation for the PuLP Modeller Authors: Antony Phillips, Dr Stuart Mitchell 2007 """ # Import PuLP modeler functions from pulp import * # Creates a list of the Ingredients Ingredients = ['CHICKEN', 'BEEF', 'MUTTON', 'RICE', 'WHEAT', 'GEL'] # A dictionary of the costs of each of the Ingredients is created costs = {'CHICKEN': 0.013, 'BEEF': 0.008, 'MUTTON': 0.010, 'RICE': 0.002, 'WHEAT': 0.005, 'GEL': 0.001} # A dictionary of the protein percent in each of the Ingredients is created proteinPercent = {'CHICKEN': 0.100, 'BEEF': 0.200, 'MUTTON': 0.150, 'RICE': 0.000, 'WHEAT': 0.040, 'GEL': 0.000} # A dictionary of the fat percent in each of the Ingredients is created fatPercent = {'CHICKEN': 0.080, 'BEEF': 0.100, 'MUTTON': 0.110, 'RICE': 0.010, 'WHEAT': 0.010, 'GEL': 0.000} # A dictionary of the fibre percent in each of the Ingredients is created fibrePercent = {'CHICKEN': 0.001, 'BEEF': 0.005, 'MUTTON': 0.003, 'RICE': 0.100, 'WHEAT': 0.150, 'GEL': 0.000} # A dictionary of the salt percent in each of the Ingredients is created saltPercent = {'CHICKEN': 0.002, 'BEEF': 0.005, 'MUTTON': 0.007, 'RICE': 0.002, 'WHEAT': 0.008, 'GEL': 0.000} # Create the 'prob' variable to contain the problem data prob = LpProblem("The Whiskas Problem", LpMinimize) # A dictionary called 'ingredient_vars' is created to contain the referenced Variables ingredient_vars = LpVariable.dicts("Ingr",Ingredients,0) # The objective function is added to 'prob' first prob += lpSum([costs[i]*ingredient_vars[i] for i in Ingredients]), "Total Cost of Ingredients per can" # The five constraints are added to 'prob' prob += lpSum([ingredient_vars[i] for i in Ingredients]) == 100, "PercentagesSum" prob += lpSum([proteinPercent[i] * ingredient_vars[i] for i in Ingredients]) >= 8.0, "ProteinRequirement" prob += lpSum([fatPercent[i] * ingredient_vars[i] for i in Ingredients]) >= 6.0, "FatRequirement" prob += lpSum([fibrePercent[i] * ingredient_vars[i] for i in Ingredients]) <= 2.0, "FibreRequirement" prob += lpSum([saltPercent[i] * ingredient_vars[i] for i in Ingredients]) <= 0.4, "SaltRequirement" # The problem data is written to an .lp file prob.writeLP("WhiskasModel2.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)