dietmodel.py
#!/usr/bin/python
# Copyright 2016, Gurobi Optimization, Inc.
# Solve the classic diet model. This file implements
# a function that formulates and solves the model,
# but it contains no model data. The data is
# passed in by the calling program. Run example 'diet2.py',
# 'diet3.py', or 'diet4.py' to invoke this function.
from gurobipy import *
def solve(categories, minNutrition, maxNutrition, foods, cost,
nutritionValues):
# Model
m = Model("diet")
# Create decision variables for the nutrition information,
# which we limit via bounds
nutrition = m.addVars(categories, lb=minNutrition, ub=maxNutrition, name="nutrition")
# Create decision variables for the foods to buy
buy = m.addVars(foods, name="buy")
# The objective is to minimize the costs
m.setObjective(buy.prod(cost), GRB.MINIMIZE)
# Nutrition constraints
m.addConstrs(
(quicksum(nutritionValues[f,c] * buy[f] for f in foods) == nutrition[c]
for c in categories), "_")
def printSolution():
if m.status == GRB.Status.OPTIMAL:
print('\nCost: %g' % m.objVal)
print('\nBuy:')
buyx = m.getAttr('x', buy)
nutritionx = m.getAttr('x', nutrition)
for f in foods:
if buy[f].x > 0.0001:
print('%s %g' % (f, buyx[f]))
print('\nNutrition:')
for c in categories:
print('%s %g' % (c, nutritionx[c]))
else:
print('No solution')
# Solve
m.optimize()
printSolution()
print('\nAdding constraint: at most 6 servings of dairy')
m.addConstr(buy.sum(['milk','ice cream']) <= 6, "limit_dairy")
# Solve
m.optimize()
printSolution()
