def _get_base_model(self):
model = aml.ConcreteModel()
model.x = aml.Var()
model.y = aml.Var()
model.d1 = aml.Param(mutable=True, initialize=0.0)
model.d2 = aml.Param(mutable=True, initialize=0.0)
model.d3 = aml.Param(mutable=True, initialize=0.0)
model.cost = aml.Expression([1, 2])
model.cost[1].expr = model.x
model.cost[2].expr = model.d1 * model.y
model.o = aml.Objective(expr=model.cost[1] + model.cost[2])
model.c1 = aml.Constraint(expr=model.x >= 0)
model.c2 = aml.Constraint(expr=model.y * model.d2 >= model.d3)
model.varstage = VariableStageAnnotation()
model.varstage.declare(model.x, 1)
model.varstage.declare(model.y, 2)
model.stagecost = StageCostAnnotation()
model.stagecost.declare(model.cost[1], 1)
model.stagecost.declare(model.cost[2], 2)
model.stochdata = StochasticDataAnnotation()
model.stochdata.declare(model.d1,
distribution=TableDistribution([0.0, 1.0]))
model.stochdata.declare(model.d2,
distribution=TableDistribution([0.0, 1.0]))
model.stochdata.declare(model.d3,
distribution=TableDistribution([0.0, 1.0]))
return model
def _get_baa99_sp(self):
model = baa99_basemodel.model.clone()
model.varstage = VariableStageAnnotation()
model.varstage.declare(model.x1, 1)
model.varstage.declare(model.x2, 1)
model.stagecost = StageCostAnnotation()
model.stagecost.declare(model.FirstStageCost, 1)
model.stagecost.declare(model.SecondStageCost, 2)
model.stochdata = StochasticDataAnnotation()
model.stochdata.declare(model.d1_rhs,
distribution=TableDistribution(
model.d1_rhs_table))
model.stochdata.declare(model.d2_rhs,
distribution=TableDistribution(
model.d2_rhs_table))
return EmbeddedSP(model)
def create_embedded():
model = aml.ConcreteModel()
model.d1 = aml.Param(mutable=True, initialize=0)
model.d2 = aml.Param(mutable=True, initialize=0)
model.d3 = aml.Param(mutable=True, initialize=0)
model.d4 = aml.Param(mutable=True, initialize=0)
# first stage
model.x = aml.Var(bounds=(0, 10))
# first stage derived
model.y = aml.Expression(expr=model.x + 1)
model.fx = aml.Var()
# second stage
model.z = aml.Var(bounds=(-10, 10))
# second stage derived
model.q = aml.Expression(expr=model.z**2)
model.fz = aml.Var()
model.r = aml.Var()
# stage costs
model.StageCost = aml.Expression([1, 2])
model.StageCost.add(1, model.fx)
model.StageCost.add(2, -model.fz + model.r + model.d1)
model.o = aml.Objective(expr=aml.sum_product(model.StageCost))
model.c_first_stage = aml.Constraint(expr=model.x >= 0)
# test our handling of intermediate variables that
# are created by Piecewise but can not necessarily
# be declared on the scenario tree
model.p_first_stage = aml.Piecewise(model.fx,
model.x,
pw_pts=[0., 2., 5., 7., 10.],
pw_constr_type='EQ',
pw_repn='INC',
f_rule=[10., 10., 9., 10., 10.],
force_pw=True)
model.c_second_stage = aml.Constraint(
expr=model.x + model.r * model.d2 >= -100)
model.cL_second_stage = aml.Constraint(expr=model.d3 >= -model.r)
model.cU_second_stage = aml.Constraint(expr=model.r <= 0)
# exercise more of the code by making this an indexed
# block
model.p_second_stage = aml.Piecewise([1],
model.fz,
model.z,
pw_pts=[-10, -5., 0., 5., 10.],
pw_constr_type='EQ',
pw_repn='INC',
f_rule=[0., 0., -1., model.d4, 1.],
force_pw=True)
# annotate the model
model.varstage = VariableStageAnnotation()
# first stage
model.varstage.declare(model.x, 1)
model.varstage.declare(model.y, 1, derived=True)
model.varstage.declare(model.fx, 1, derived=True)
model.varstage.declare(model.p_first_stage, 1, derived=True)
# second stage
model.varstage.declare(model.z, 2)
model.varstage.declare(model.q, 2, derived=True)
model.varstage.declare(model.fz, 2, derived=True)
model.varstage.declare(model.r, 2, derived=True)
model.varstage.declare(model.p_second_stage, 2, derived=True)
model.stagecost = StageCostAnnotation()
for i in [1, 2]:
model.stagecost.declare(model.StageCost[i], i)
model.stochdata = StochasticDataAnnotation()
model.stochdata.declare(model.d1,
distribution=TableDistribution([0.0, 1.0, 2.0]))
model.stochdata.declare(model.d2,
distribution=TableDistribution([0.0, 1.0, 2.0]))
model.stochdata.declare(model.d3,
distribution=TableDistribution([0.0, 1.0, 2.0]))
model.stochdata.declare(model.d4,
distribution=TableDistribution([0.0, 1.0, 2.0]))
return EmbeddedSP(model)
def test_compute_time_stage(self):
model = ConcreteModel()
model.x = Var()
model.y = Var([0, 1])
model.z = Var()
model.p = Param(mutable=True)
model.cost = Expression([0, 1])
model.cost[0] = model.x + model.y[0]
model.cost[1] = model.p + model.y[1] + model.y[0] * model.p
model.o = Objective(expr=model.cost[0] + model.cost[1])
model.c = ConstraintList()
model.c.add(model.x >= 1) # 1
model.c.add(model.y[0] >= 1) # 2
model.c.add(model.p * model.y[0] >= 1) # 3
model.c.add(model.y[0] >= model.p) # 4
model.c.add(model.p <= model.y[1]) # 5
model.c.add(model.y[1] <= 1) # 6
model.c.add(model.x >= model.p) # 7
model.c.add(model.z == 1) # 8
model.varstage = VariableStageAnnotation()
model.varstage.declare(model.x, 1)
model.varstage.declare(model.y[0], 1, derived=True)
model.varstage.declare(model.y[1], 2)
model.varstage.declare(model.z, 2, derived=True)
model.stagecost = StageCostAnnotation()
model.stagecost.declare(model.cost[0], 1)
model.stagecost.declare(model.cost[1], 2)
model.stochdata = StochasticDataAnnotation()
model.stochdata.declare(model.p,
distribution=UniformDistribution(0, 1))
sp = EmbeddedSP(model)
#
# check variables
#
self.assertEqual(sp.compute_time_stage(model.x), min(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.x, derived_last_stage=True),
min(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.y[0]),
min(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.y[0], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.y[1]),
max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.y[1], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.z), max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.z, derived_last_stage=True),
max(sp.time_stages))
#
# check constraints
#
self.assertEqual(sp.compute_time_stage(model.c[1]),
min(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.c[1], derived_last_stage=True),
min(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.c[2]),
min(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.c[2], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.c[3]),
max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.c[3], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.c[4]),
max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.c[4], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.c[5]),
max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.c[5], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.c[6]),
max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.c[6], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.c[7]),
max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.c[7], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.c[8]),
max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.c[8], derived_last_stage=True),
max(sp.time_stages))
#
# check objectives and expressions
#
self.assertEqual(sp.compute_time_stage(model.cost[0]),
min(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.cost[0], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.cost[1]),
max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.cost[1], derived_last_stage=True),
max(sp.time_stages))
self.assertEqual(sp.compute_time_stage(model.o), max(sp.time_stages))
self.assertEqual(
sp.compute_time_stage(model.o, derived_last_stage=True),
max(sp.time_stages))