from FuncDesigner import *
from openopt import MINLP
a, b, c = oovars('a', 'b', 'c')
d = oovar('d', domain = [1, 2, 3, 3.5, -0.5, -4]) # domain should be Python list/set/tuple of allowed values
e = oovar('e', domain = [1, 2, 3, 3.5, -0.5, -4])
a=oovars(2)('a')
startPoint = {a:[100, 12], b:2, c:40, d:1, e:2} # however, you'd better use numpy arrays instead of Python lists
f = sum(a*[1, 2])**2 + b**2 + c**2 + d**2+e**2
constraints = [(2*c+a-10)**2 < 1.5 + 0.1*b, (a-10)**2<1.5, a[0]>8.9, (a+b > [ 7.97999836, 7.8552538 ])('sum_a_b', tol=1.00000e-12), \
a < 9, b < -1.02, c > 1.01, ((b + c * log10(a).sum() - 1) ** 2==0)(tol=1e-6), b+d**2 < 100]
p = MINLP(f, startPoint, constraints = constraints)
r = p.minimize('interalg', iprint=10, fTol = 1e-8, implicitBounds=1000)
print(r.xf)
a_opt, b_opt, c_opt, d_opt = r(a, b, c, d)
# or any of the following:
# a_opt, b_opt, c_opt,d_opt = r(a), r(b), r(c),r(d)
# r('a'), r('b'), r('c'), r('d') (provided you have assigned the names to oovars as above)
# r('a', 'b', 'c', 'd')
# a(r), b(r), c(r), d(r)
"""
Expected output:
...
OpenOpt info: Solution with required tolerance 1.0e-08 is guarantied
Solver: Time Elapsed = 4.72 CPU Time Elapsed = 4.48
objFunValue: 718.2574 (feasible, max(residuals/requiredTolerances) = 0.00144128)
{b: -1.0200000004647196, c: 1.0618036990619659, d: -0.5, e: -0.5, a_0: 8.9999983590234365, a_1: 8.8752537991469964}
"""
from FuncDesigner import *
from openopt import MINLP
import numpy as np
# two customer, one facility problem
vol, dump = oovars('vol','dump') # a is mass sent to LF, b is mass recovered at landfill, c is unrecovered
op = oovar('op', domain = np.array([1, 0])) # domain should be Python list/set/tuple of allowed values
startPoint = {vol:np.array([100,100]), dump:np.array([20,30]), op:np.array([0])} # however, you'd better use numpy arrays instead of Python lists
# variable attributes
cap = np.array([400]) # facility capacity per ton
demand = np.array([200,200]) # ton
elec = 5 # CO2 from electricity per ton
ghg = 19.54 # CO2 from transport per mile
dist = np.array([ 55, 15]) # distance in miles between pv and mrf
cost = 0 # social cost of dumping
f = sum(sum(op*dist))*ghg + sum(op*vol)*elec + sum(dump)*cost # min ghg impact
constraints = [ op*(vol + dump) == demand , op*sum(vol) < cap ,vol >= 0]
p = MINLP(f, startPoint, constraints = constraints)
r = p.minimize('branb', nlpSolver='ralg', plot=0, discrtol = 1e-6, xtol=1e-7)
vol_opt, dump_opt, op_opt = r(vol, dump, op)
# or any of the following:
# a_opt, b_opt, c_opt,d_opt = r(a), r(b), r(c),r(d)
# r('a'), r('b'), r('c'), r('d') (provided you have assigned the names to oovars as above)
# r('a', 'b', 'c', 'd')
# a(r), b(r), c(r), d(r)
print(vol_opt, dump_opt, op_opt)
