def construct_nvmc():
from msppy.msp import MSLP
nvmc = MSLP(T=3, sense=-1, bound=100)
nvmc.add_MC_uncertainty(Markov_states=[[[0]], [[4], [6]], [[4], [6]]],
transition_matrix=[[[1]], [[0.5, 0.5]],
[[0.3, 0.7], [0.7, 0.3]]])
for t in range(3):
m = nvmc[t]
buy_now, buy_past = m.addStateVar(name='bought', obj=-1.0)
if t != 0:
sold = m.addVar(name='sold', obj=2)
unsatisfied = m.addVar(name='unsatisfied')
recycled = m.addVar(name='recycled', obj=0.5)
m.addConstr(sold + unsatisfied == 0,
uncertainty_dependent={'rhs': 0})
m.addConstr(sold + recycled == buy_past)
return nvmc
from msppy.msp import MSLP
from msppy.solver import Extensive,SDDP
import gurobipy
T = 4
PurchasePrice = [5.0, 8.0]
Demand = [[10.0, 15.0], [12.0, 20.0], [8.0, 20.0]]
RetailPrice = 7.0
newsVendor = MSLP(T=T, sense=1, bound=-1000)
newsVendor.add_MC_uncertainty(
Markov_states = [
[[5.0]],
[[5.0]],
[[5.0],[8.0]],
[[5.0],[8.0]]
],
transition_matrix = [
[[1]],
[[1]],
[[0.6,0.4]],
[[0.3,0.7],[0.3,0.7]]
]
)
for t in range(T):
m = newsVendor[t]
now, past = m.addStateVar(ub=100, name="stock")
if t > 0:
buy = m.addVar(name="buy", uncertainty_dependent=0)
sell = m.addVar(name="sell", obj=- RetailPrice)
m.addConstr(now == past + buy - sell)
random = m.addVar(lb=-gurobipy.GRB.INFINITY, ub=gurobipy.GRB.INFINITY, name="demand")
m.addConstr(random == 20, uncertainty={'rhs': Demand[t-1]})