def test_woody_max_add_variable(self):
m = Model()
x1 = m.add_var("real+", name="a")
x2 = m.add_var("real+", name="b")
m.maximize(35 * x1 + 60 * x2)
m.add_constraint(8 * x1 + 12 * x2 <= 120)
m.add_constraint(15 * x2 <= 60)
m.add_constraint(3 * x1 + 6 * x2 <= 48)
m.solve(consider_dual=0)
m.add_new_variable([16, 20, 9], 75)
computed_solution = m.get_solution_object()
real_sol = [12, 2, 0, 540]
for x_idx in range(len(real_sol)):
self.assertAlmostEqual(computed_solution[x_idx], real_sol[x_idx])
def test_maximize_2v_4c_2o(self):
m = Model(print_obj={})
x = []
for i in range(1, 3):
x.append(m.add_var("real+", name="x_%d" % i))
m.maximize(4 * x[0] + 3 * x[1])
m.add_constraint(2 * x[0] + 3 * x[1] <= 6)
m.add_constraint(-3 * x[0] + 2 * x[1] <= 3)
m.add_constraint(0 * x[0] + 2 * x[1] <= 5)
m.add_constraint(2 * x[0] + 1 * x[1] <= 4)
m.solve()
computed_solution = m.get_solution_object()
real_sol = [1.5, 1, 9]
for x_idx in range(len(real_sol)):
self.assertAlmostEqual(computed_solution[x_idx], real_sol[x_idx])
Fulltime = 5 consecutive days + 2 free days
Weekday | Employees needed
Monday | 17
Tuesday | 13
Wednesday | 15
Thursday | 19
Friday | 14
Saturday | 16
Sunday | 11
"""
mon = m.add_var("real+", name="Monday")
tue = m.add_var("real+", name="Tuesday")
wed = m.add_var("real+", name="Wednesday")
thu = m.add_var("real+", name="Thursday")
fri = m.add_var("real+", name="Friday")
sat = m.add_var("real+", name="Saturday")
sun = m.add_var("real+", name="Sunday")
m.minimize(mon+tue+wed+thu+fri+sat+sun)
m.add_constraint(mon+thu+fri+sat+sun >= 17)
m.add_constraint(tue+fri+sat+sun+mon >= 13)
m.add_constraint(wed+sat+sun+mon+tue >= 15)
m.add_constraint(thu+sun+mon+tue+wed >= 19)
m.add_constraint(fri+mon+tue+wed+thu >= 14)
import numpy as np
from time import time
import random, string
from Model.model import Model
m = Model(print_obj={'end_conf': True})
x = []
for i in range(4):
x.append(m.add_var("real+", name=i))
x = np.array(x)
m.maximize(sum(np.array([4, 1, 5, 3]) * x))
m.add_constraint(x[0] - x[1] - x[2] + 3 * x[3] <= 1)
m.add_constraint(5 * x[0] + x[1] + 3 * x[2] + 8 * x[3] <= 55)
m.add_constraint(-x[0] + 2 * x[1] + 3 * x[2] - 5 * x[3] <= 3)
print("all added")
t0 = time()
m.solve()
print("Solved in %f" % (time() - t0))
m.print_solution(slack=False)
print("Steps: ", m.steps)
"price": 98,
"max": 2
}
}
MIN_REQ = {
"kcal": 2000,
"protein": 55,
"calcium": 800,
}
list_of_ingredients = get_keys(ingredients)
x = []
for ing in list_of_ingredients:
x.append(m.add_var("real+", name=ing, ub=ingredients[ing]["max"]))
x = np.array(x)
m.minimize(sum(get_by_key(ingredients, "price", list_of_ingredients) * x))
for cst in MIN_REQ:
left = get_by_key(ingredients, cst, list_of_ingredients)
m.add_constraint(sum(left * x) >= MIN_REQ[cst])
print("all added")
t0 = time()
m.solve(consider_dual=0)
print("Solved first in %f" % (time() - t0))
from Model.model import Model
import numpy as np
from time import time
import sys
m = Model(print_obj={'start_conf': True})
a = m.add_var("real+", name="a")
b = m.add_var("real+", name="b")
m.maximize(3 * a - b)
m.add_constraint(-3 * a + 3 * b <= 6)
m.add_constraint(-8 * a + 4 * b <= 4)
t0 = time()
m.solve()
print("Solved in %f" % (time() - t0))
m.print_solution()
from Model.model import Model
import numpy as np
from time import time
import sys
m = Model(print_obj={'start_conf': True})
x1 = m.add_var("real+", name="a")
x2 = m.add_var("real+", name="b")
x3 = m.add_var("real+", name="c")
m.minimize(35 * x1 + 60 * x2 + 75 * x3)
# """
m.add_constraint(8 * x1 + 12 * x2 + 16 * x3 >= 120)
m.add_constraint(15 * x2 + 20 * x3 >= 60)
m.add_constraint(3 * x1 + 6 * x2 + 9 * x3 >= 48)
"""
m.add_constraint(8 * x1 + 12 * x2 + 16 * x3 + 12 * x4 <= 120)
m.add_constraint(15 * x2 + 20 * x3 + 30 * x4 <= 60)
m.add_constraint(3 * x1 + 6 * x2 + 9 * x3 + 15 * x4 <= 48)
"""
m.solve(consider_dual=0)
m.print_solution()
print(m.t.tableau)
m.add_new_variable([12, 30, 10], 15)
m.print_solution()
K | 15 | 6 | 16 | 2400
L | 10 | 15 | 0 | 2400
Total | 57 | 59 | 42 | 9600
Item | Product A | Product B | Product C
Revenue p.u. | 90$ | 100$ | 70$
Material p.u. | 45$ | 40$ | 20$
Profit p.u. | 45$ | 60$ | 50$
Maximum sales | 100 | 40 | 60
Maximize profit
"""
a = m.add_var("real+", name="apple")
b = m.add_var("real+", name="banana")
c = m.add_var("real+", name="carrot")
m.maximize(45*a+60*b+50*c)
m.add_constraint(20*a+10*b+10*c <= 2400)
m.add_constraint(12*a+28*b+16*c <= 2400)
m.add_constraint(15*a+6*b+16*c <= 2400)
m.add_constraint(10*a+15*b <= 2400)
m.add_constraint(a <= 100)
m.add_constraint(b <= 40)
m.add_constraint(c <= 60)