gistfile1.txt

from pulp import *

# Creates a list of the Ingredients
Ingredients = ['TOMATO', 'LETTUCE', 'SPINACH', 'CARROT', 'SUNFLOWER', 'TOFU', 'CHICKPEAS', 'OIL']

kcal = {'TOMATO': 21, 
         'LETTUCE': 16, 
         'SPINACH': 40, 
         'CARROT': 41, 
         'SUNFLOWER': 585, 
         'TOFU': 120,
         'CHICKPEAS': 164,
         'OIL': 884}

protein = {'TOMATO': 0.85, 
         'LETTUCE': 1.62, 
         'SPINACH': 2.86, 
         'CARROT': 0.93, 
         'SUNFLOWER': 23.4, 
         'TOFU': 16,
         'CHICKPEAS': 9,
         'OIL': 0}

fat = {'TOMATO': 0.33, 
         'LETTUCE': 0.20, 
         'SPINACH': 0.39, 
         'CARROT': 0.24, 
         'SUNFLOWER': 48.7, 
         'TOFU': 5.0,
         'CHICKPEAS': 2.6,
         'OIL': 100.0}

carbs = {'TOMATO': 4.64, 
         'LETTUCE': 2.37, 
         'SPINACH': 3.63, 
         'CARROT': 9.58, 
         'SUNFLOWER': 15.0, 
         'TOFU': 3.0,
         'CHICKPEAS': 27.0,
         'OIL': 0.0}

sodium = {'TOMATO': 9.0, 
         'LETTUCE': 28.0, 
         'SPINACH': 65.0, 
         'CARROT': 69.0, 
         'SUNFLOWER': 3.80, 
         'TOFU': 120.0,
         'CHICKPEAS': 78.0,
         'OIL': 0.0}

cost = {'TOMATO': 1.0, 
         'LETTUCE': 0.75, 
         'SPINACH': 0.50, 
         'CARROT': 0.50, 
         'SUNFLOWER': 0.45, 
         'TOFU': 2.15,
         'CHICKPEAS': 0.95,
         'OIL': 2.00}

# Create the 'prob' variable to contain the problem data
prob = LpProblem("The Salad 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([kcal[i]*ingredient_vars[i] for i in Ingredients]), "Total kCal of Ingredients per salad"

# The constraints are added to 'prob'
prob += lpSum([protein[i] * ingredient_vars[i] for i in Ingredients]) >= 15.0, "ProteinRequirement"
prob += 8.0 >= lpSum([fat[i] * ingredient_vars[i] for i in Ingredients]) >= 2.0, "FatRequirement"
prob += lpSum([carbs[i] * ingredient_vars[i] for i in Ingredients]) >= 4.0, "CarbRequirement"
prob += lpSum([sodium[i] * ingredient_vars[i] for i in Ingredients]) <= 200.0, "SodiumRequirement"
prob += lpSum([lettuce[i] * ingredient_vars[i] + spinach[i] * ingredient_vars[i] for i in Ingredients]) / lpSum([lettuce[i] * ingredient_vars[i] + spinach[i] * ingredient_vars[i] + [tomato[i] * ingredient_vars[i] + carrot[i] * ingredient_vars[i] + sunflower[i] * ingredient_vars[i] + tofu[i] * ingredient_vars[i] + chickpeas[i] * ingredient_vars[i] + oil[i] * ingredient_vars[i] for i in Ingredients]) >= 0.40, "GreensRequirement"

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 kCal of Ingredients per salad = ", value(prob.objective))