dietmodel.py#
#!/usr/bin/env python3.7
# Copyright 2024, Gurobi Optimization, LLC
# 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.
import gurobipy as gp
from gurobipy import GRB
def solve(categories, minNutrition, maxNutrition, foods, cost,
nutritionValues):
# Model
m = gp.Model("diet")
# 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((gp.quicksum(nutritionValues[f, c] * buy[f] for f in foods) ==
[minNutrition[c], maxNutrition[c]] for c in categories), "_")
def printSolution():
if m.status == GRB.OPTIMAL:
print('\nCost: %g' % m.ObjVal)
print('\nBuy:')
for f in foods:
if buy[f].X > 0.0001:
print('%s %g' % (f, buy[f].X))
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()