def _generate_model(self):
self.model = pmo.block()
model = self.model
model._name = self.description
model.f = pmo.variable()
model.x = pmo.variable(lb=1,ub=3)
model.fi = pmo.parameter_dict()
model.fi[1] = pmo.parameter(value=1.0)
model.fi[2] = pmo.parameter(value=2.0)
model.fi[3] = pmo.parameter(value=0.0)
model.xi = pmo.parameter_dict()
model.xi[1] = pmo.parameter(value=1.0)
model.xi[2] = pmo.parameter(value=2.0)
model.xi[3] = pmo.parameter(value=3.0)
model.p = pmo.variable(domain=NonNegativeReals)
model.n = pmo.variable(domain=NonNegativeReals)
model.lmbda = pmo.variable_dict(
(i, pmo.variable()) for i in range(1,4))
model.obj = pmo.objective(model.p+model.n)
model.c1 = pmo.constraint_dict()
model.c1[1] = pmo.constraint((0.0, model.lmbda[1], 1.0))
model.c1[2] = pmo.constraint((0.0, model.lmbda[2], 1.0))
model.c1[3] = pmo.constraint(0.0 <= model.lmbda[3])
model.c2 = pmo.sos2(model.lmbda.values())
model.c3 = pmo.constraint(sum(model.lmbda.values()) == 1)
model.c4 = pmo.constraint(model.f==sum(model.fi[i]*model.lmbda[i]
for i in model.lmbda))
model.c5 = pmo.constraint(model.x==sum(model.xi[i]*model.lmbda[i]
for i in model.lmbda))
model.x.fix(2.75)
# Make an empty SOS constraint
model.c6 = pmo.sos2([])
def _generate_model(self):
self.model = pmo.block()
model = self.model
model._name = self.description
model.a1 = pmo.parameter(value=1.0)
model.a2 = pmo.parameter_dict({1: pmo.parameter(value=1.0)})
model.a3 = pmo.parameter(value=1.0)
model.a4 = pmo.parameter_dict({1: pmo.parameter(value=1.0)})
model.x = pmo.variable(domain=NonNegativeReals)
model.y = pmo.variable(domain=NonNegativeReals)
model.z1 = pmo.variable()
model.z2 = pmo.variable()
model.dummy_expr1 = pmo.expression(model.a1 * model.a2[1])
model.dummy_expr2 = pmo.expression(model.y / model.a3 * model.a4[1])
model.inactive_obj = pmo.objective(model.x + 3.0 * model.y + 1.0 +
model.z1 - model.z2)
model.inactive_obj.deactivate()
model.p = pmo.parameter(value=0.0)
model.obj = pmo.objective(model.p + model.inactive_obj)
model.c1 = pmo.constraint(
model.dummy_expr1 <= pmo.noclone(model.dummy_expr2))
model.c2 = pmo.constraint((2.0, model.x / model.a3 - model.y, 10))
model.c3 = pmo.constraint((0, model.z1 + 1, 10))
model.c4 = pmo.constraint((-10, model.z2 + 1, 0))
def _generate_model(self):
self.model = pmo.block()
model = self.model
model._name = self.description
model.a1 = pmo.parameter(value=1.0)
model.a2 = pmo.parameter_dict(
{1: pmo.parameter(value=1.0)})
model.a3 = pmo.parameter(value=1.0)
model.a4 = pmo.parameter_dict(
{1: pmo.parameter(value=1.0)})
model.x = pmo.variable(domain=NonNegativeReals)
model.y = pmo.variable(domain=NonNegativeReals)
model.z1 = pmo.variable()
model.z2 = pmo.variable()
model.dummy_expr1 = pmo.expression(model.a1*model.a2[1])
model.dummy_expr2 = pmo.expression(model.y/model.a3*model.a4[1])
model.inactive_obj = pmo.objective(
model.x + 3.0*model.y + 1.0 + model.z1 - model.z2)
model.inactive_obj.deactivate()
model.p = pmo.parameter(value=0.0)
model.obj = pmo.objective(model.p + model.inactive_obj)
model.c1 = pmo.constraint(model.dummy_expr1 <= pmo.noclone(model.dummy_expr2))
model.c2 = pmo.constraint((2.0, model.x/model.a3 - model.y, 10))
model.c3 = pmo.constraint((0, model.z1 + 1, 10))
model.c4 = pmo.constraint((-10, model.z2 + 1, 0))
[125, 95, 90, 95],
[45, 110, 95, 115],
[50, 100, 90, 100],
]
NUM_WORKERS = len(costs)
NUM_TASKS = len(costs[0])
# Define the model
model = pmo.block()
# Create the sets
model.set_workers = range(NUM_WORKERS)
model.set_tasks = range(NUM_TASKS)
# Create the parameters
model.param_cost = pmo.parameter_dict()
for _worker in model.set_workers:
for _task in model.set_tasks:
model.param_cost[(_worker,
_task)] = pmo.parameter(costs[_worker][_task])
# Create the variables
model.var_x = pmo.variable_dict()
for _worker in model.set_workers:
for _task in model.set_tasks:
model.var_x[(_worker, _task)] = pmo.variable(domain=pmo.Binary)
# Create the constraints
## Each worker is assigned to at most 1 task
model.con_worker = pmo.constraint_list()
for _worker in model.set_workers:
312]
weights = [
7, 0, 30, 22, 80, 94, 11, 81, 70, 64, 59, 18, 0, 36, 3, 8, 15, 42, 9, 0,
42, 47, 52, 32, 26, 48, 55, 6, 29, 84, 2, 4, 18, 56, 7, 29, 93, 44, 71,
3, 86, 66, 31, 65, 0, 79, 20, 65, 52, 13]
capacities = 850
model = pmo.block()
# Create the sets
model.set_labels = range(len(values))
# Create the parameters
model.param_label_value = pmo.parameter_dict()
model.param_label_weight = pmo.parameter_dict()
for i in model.set_labels:
model.param_label_value[i] = pmo.parameter(values[i])
model.param_label_weight[i] = pmo.parameter(weights[i])
# Create the variables
model.var_pack = pmo.variable_dict()
for i in model.set_labels:
model.var_pack[i] = pmo.variable(domain=pmo.Binary)
# Create the constraints
model.con_capacity = pmo.constraint(sum(model.var_pack[i] * weights[i] for i in model.set_labels) <= capacities)
# Create the objective
model.obj = pmo.objective(sum(model.var_pack[i] * values[i] for i in model.set_labels), sense=pmo.maximize)
# @Sets_1
m.s = [1, 2]
# @Sets_1
# @Sets_2
# [0,1,2]
m.q = range(3)
# @Sets_2
# @Parameters_single
m.p = pmo.parameter(0)
# @Parameters_single
# @Parameters_dict
# pd[1] = 0, pd[2] = 1
m.pd = pmo.parameter_dict()
for k, i in enumerate(m.s):
m.pd[i] = pmo.parameter(k)
# @Parameters_dict
# @Parameters_list
# uses 0-based indexing
# pl[0] = 0, pl[0] = 1, ...
m.pl = pmo.parameter_list()
for j in m.q:
m.pl.append(pmo.parameter(j))
# @Parameters_list
# @Variables_single
m.v = pmo.variable(value=1, lb=1, ub=4)
# @Variables_single
#
# Blocks
#
# define a simple optimization model
b = pmo.block()
b.x = pmo.variable()
b.c = pmo.constraint(expr=b.x >= 1)
b.o = pmo.objective(expr=b.x)
# define an optimization model with indexed containers
b = pmo.block()
b.p = pmo.parameter()
b.plist = pmo.parameter_list(pmo.parameter() for i in range(10))
b.pdict = pmo.parameter_dict(
((i, j), pmo.parameter()) for i in range(10) for j in range(10))
b.x = pmo.variable()
b.xlist = pmo.variable_list(pmo.variable() for i in range(10))
b.xdict = pmo.variable_dict(
((i, j), pmo.variable()) for i in range(10) for j in range(10))
b.c = pmo.constraint(b.x >= 1)
b.clist = pmo.constraint_list(
pmo.constraint(b.xlist[i] >= i) for i in range(10))
b.cdict = pmo.constraint_dict(((i, j), pmo.constraint(b.xdict[i, j] >= i * j))
for i in range(10) for j in range(10))
b.o = pmo.objective(b.x + sum(b.xlist) + sum(b.xdict.values()))
#
# @Sets_1
# @Sets_2
# [0,1,2]
m.q = range(3)
# @Sets_2
# @Parameters_single
m.p = pmo.parameter(0)
# @Parameters_single
# @Parameters_dict
# pd[1] = 0, pd[2] = 1
m.pd = pmo.parameter_dict()
for k,i in enumerate(m.s):
m.pd[i] = pmo.parameter(k)
# @Parameters_dict
# @Parameters_list
# uses 0-based indexing
# pl[0] = 0, pl[0] = 1, ...
m.pl = pmo.parameter_list()
for j in m.q:
m.pl.append(
pmo.parameter(j))
# @Parameters_list