def get_best_libs_unlimited_ships(self,
ind_libs_available,
days_available,
solverName="scip"):
ind_books_available = set()
for lib in ind_libs_available:
books = self.lib_books_lists[lib]
for book in books:
ind_books_available.add(book)
ind_books_available = np.array(list(ind_books_available))
book_libs_lists_available = [[] for _ in range(self.num_books)]
for lib in ind_libs_available:
books = self.lib_books_lists[lib]
for book in books:
book_libs_lists_available[book].append(lib)
self.model = pmo.block()
self.model.books = pmo.variable_dict()
for book in ind_books_available:
self.model.books[book] = pmo.variable(lb=0, ub=1)
self.model.libs = pmo.variable_dict()
for lib in ind_libs_available:
self.model.libs[lib] = pmo.variable(domain=pmo.Binary)
self.model.max_libs_in_time = pmo.constraint_list()
self.model.max_libs_in_time.append(
pmo.constraint(
sum([
self.model.libs[lib] * self.lib_days[lib]
for lib in ind_libs_available
]) <= days_available))
self.model.use_books = pmo.constraint_list()
for book in ind_books_available:
libs = book_libs_lists_available[book]
self.model.use_books.append(
pmo.constraint(
self.model.books[book] <= sum(self.model.libs[lib]
for lib in libs)))
self.model.objective = pmo.objective(sum(
self.book_points[book] * self.model.books[book]
for book in ind_books_available),
sense=-1)
solver = self.get_solver(solverName)
solver_result = solver.solve(self.model)
result = []
for lib in ind_libs_available:
if self.model.libs[lib] != 0:
result.append(lib)
return result
def __init__(
self,
interval_set,
params: dict,
):
super().__init__()
## Setup
self.id = params["name"]
self.objective_terms = {}
# Need to reference interval set in coupling method, either need to add it to class or pass it to function.
self.interval_set = interval_set
self.component_element_ids = params[
"component_element_names"] # resources or other collections
## Parameters
self.import_limit = aml.parameter(
params["import_limit"]) # assume positive
self.export_limit = aml.parameter(params["export_limit"])
## Expressions/Variables
# Should we define net_export as a variable and set equal later - allows to define upper and lower bounds here
# or should it be an expression here, and we reconstruct constraints later whenever we add elements - i.e. the coupling aspect of the problem
# It really depends on how the model is "constructed" - one option is to define everything as constructor functions that are called when the model is constructed, but order of construction matters here!
# If we define as a variable, then we decouple them.
self.net_export = aml.variable_dict()
for interval in interval_set:
self.net_export[interval] = aml.variable(lb=-self.import_limit,
ub=self.export_limit)
def _generate_model(self):
self.model = None
self.model = pmo.block()
model = self.model
model._name = self.description
model.s = list(range(1,13))
model.x = pmo.variable_dict(
(i, pmo.variable()) for i in model.s)
model.x[1].lb = -1
model.x[1].ub = 1
model.x[2].lb = -1
model.x[2].ub = 1
model.obj = pmo.objective(sum(model.x[i]*((-1)**(i+1))
for i in model.s))
model.c = pmo.constraint_dict()
# to make the variable used in the constraint match the name
model.c[3] = pmo.constraint(model.x[3]>=-1.)
model.c[4] = pmo.constraint(model.x[4]<=1.)
model.c[5] = pmo.constraint(model.x[5]==-1.)
model.c[6] = pmo.constraint(model.x[6]==-1.)
model.c[7] = pmo.constraint(model.x[7]==1.)
model.c[8] = pmo.constraint(model.x[8]==1.)
model.c[9] = pmo.constraint((-1.,model.x[9],-1.))
model.c[10] = pmo.constraint((-1.,model.x[10],-1.))
model.c[11] = pmo.constraint((1.,model.x[11],1.))
model.c[12] = pmo.constraint((1.,model.x[12],1.))
model.c_inactive = pmo.constraint_dict()
# to make the variable used in the constraint match the name
model.c_inactive[3] = pmo.constraint(model.x[3]>=-2.)
model.c_inactive[4] = pmo.constraint(model.x[4]<=2.)
def _generate_model(self):
self.model = None
self.model = pmo.block()
model = self.model
model._name = self.description
model.s = list(range(1, 13))
model.x = pmo.variable_dict((i, pmo.variable()) for i in model.s)
model.x[1].lb = -1
model.x[1].ub = 1
model.x[2].lb = -1
model.x[2].ub = 1
model.obj = pmo.objective(
sum(model.x[i] * ((-1)**(i + 1)) for i in model.s))
model.c = pmo.constraint_dict()
# to make the variable used in the constraint match the name
model.c[3] = pmo.constraint(model.x[3] >= -1.)
model.c[4] = pmo.constraint(model.x[4] <= 1.)
model.c[5] = pmo.constraint(model.x[5] == -1.)
model.c[6] = pmo.constraint(model.x[6] == -1.)
model.c[7] = pmo.constraint(model.x[7] == 1.)
model.c[8] = pmo.constraint(model.x[8] == 1.)
model.c[9] = pmo.constraint((-1., model.x[9], -1.))
model.c[10] = pmo.constraint((-1., model.x[10], -1.))
model.c[11] = pmo.constraint((1., model.x[11], 1.))
model.c[12] = pmo.constraint((1., model.x[12], 1.))
model.c_inactive = pmo.constraint_dict()
# to make the variable used in the constraint match the name
model.c_inactive[3] = pmo.constraint(model.x[3] >= -2.)
model.c_inactive[4] = pmo.constraint(model.x[4] <= 2.)
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_base_model(self):
self.model = pmo.block()
model = self.model
model._name = self.description
model.s = list(range(1,13))
model.x = pmo.variable_dict(
((i, pmo.variable()) for i in model.s))
model.x[1].lb = -1
model.x[1].ub = 1
model.x[2].lb = -1
model.x[2].ub = 1
model.obj = pmo.objective(expr=sum(model.x[i]*((-1)**(i+1))
for i in model.s))
variable_order = [
model.x[3],
model.x[4],
model.x[5],
model.x[6],
model.x[7],
model.x[8],
model.x[9],
model.x[10],
model.x[11],
model.x[12]]
return variable_order
def _generate_base_model(self):
self.model = pmo.block()
model = self.model
model._name = self.description
model.s = list(range(1,13))
model.x = pmo.variable_dict(
((i, pmo.variable()) for i in model.s))
model.x[1].lb = -1
model.x[1].ub = 1
model.x[2].lb = -1
model.x[2].ub = 1
model.obj = pmo.objective(expr=sum(model.x[i]*((-1)**(i+1))
for i in model.s))
variable_order = [
model.x[3],
model.x[4],
model.x[5],
model.x[6],
model.x[7],
model.x[8],
model.x[9],
model.x[10],
model.x[11],
model.x[12]]
return variable_order
def build_unordered_variable_dict():
"""Build an unordered variable_dict with no references to external
objects so its size can be computed."""
return variable_dict(
((i, variable(domain_type=None, lb=None, ub=None, value=None))
for i in range(N)),
ordered=False)
def __init__(self, V, W,
pyomo_solver="ipopt",
pyomo_solver_io="nl",
integer_tolerance=1e-4):
assert V > 0
assert integer_tolerance > 0
self.V = V
self.W = W
self._integer_tolerance = integer_tolerance
N = range(len(self.W))
m = self.model = pmo.block()
x = m.x = pmo.variable_dict()
y = m.y = pmo.variable_dict()
for i in N:
y[i] = pmo.variable(domain=pmo.Binary)
for j in N:
x[i,j] = pmo.variable(domain=pmo.Binary)
m.B = pmo.expression(sum(y.values()))
m.objective = pmo.objective(m.B, sense=pmo.minimize)
m.B_nontrivial = pmo.constraint(m.B >= 1)
m.capacity = pmo.constraint_dict()
for i in N:
m.capacity[i] = pmo.constraint(
sum(x[i,j]*self.W[j] for j in N) <= self.V*y[i])
m.assign_1 = pmo.constraint_dict()
for j in N:
m.assign_1[j] = pmo.constraint(
sum(x[i,j] for i in N) == 1)
# relax everything for the bound solves,
# since the objective uses a simple heuristic
self.true_domain_type = pmo.ComponentMap()
for xij in self.model.x.components():
self.true_domain_type[xij] = xij.domain_type
xij.domain_type = pmo.RealSet
for yi in self.model.y.components():
self.true_domain_type[yi] = yi.domain_type
yi.domain_type = pmo.RealSet
self.opt = pmo.SolverFactory(
pyomo_solver,
solver_io=pyomo_solver_io)
def _generate_model(self):
self.model = pmo.block()
model = self.model
model._name = self.description
model.a = pmo.parameter(value=0.1)
model.x = pmo.variable(domain=NonNegativeReals)
model.y = pmo.variable_dict()
model.y[1] = pmo.variable(domain=NonNegativeReals)
model.y[2] = pmo.variable(domain=NonNegativeReals)
model.obj = pmo.objective(model.x + model.y[1] + 2 * model.y[2])
model.c1 = pmo.constraint(model.a <= model.y[2])
model.c2 = pmo.constraint(2.0 <= model.x <= 10.0)
model.c3 = pmo.sos1(model.y.values())
model.c4 = pmo.constraint(sum(model.y.values()) == 1)
# Make an empty SOS constraint
model.c5 = pmo.sos1([])
def _generate_model(self):
self.model = pmo.block()
model = self.model
model._name = self.description
model.a = pmo.parameter(value=0.1)
model.x = pmo.variable(domain=NonNegativeReals)
model.y = pmo.variable_dict()
model.y[1] = pmo.variable(domain=NonNegativeReals)
model.y[2] = pmo.variable(domain=NonNegativeReals)
model.obj = pmo.objective(model.x + model.y[1]+2*model.y[2])
model.c1 = pmo.constraint(model.a <= model.y[2])
model.c2 = pmo.constraint((2.0, model.x, 10.0))
model.c3 = pmo.sos1(model.y.values())
model.c4 = pmo.constraint(sum(model.y.values()) == 1)
# Make an empty SOS constraint
model.c5 = pmo.sos1([])
def get_optimal_ordering_and_books(self,
ind_libs_available,
days_available=None,
book_points_available=None,
solverName="scip"):
if days_available is None:
days_available = self.num_days
if book_points_available is None:
book_points_available = self.book_points
ind_books_available = set()
for lib in ind_libs_available:
books = self.lib_books_lists[lib]
for book in books:
if book_points_available[book] != 0:
ind_books_available.add(book)
ind_books_available = np.array(list(ind_books_available))
book_libs_lists_available = [[] for _ in range(self.num_books)]
for lib in ind_libs_available:
books = self.lib_books_lists[lib]
for book in books:
book_libs_lists_available[book].append(lib)
ind_index_available = np.arange(len(ind_libs_available))
self.model = pmo.block()
self.model.books = pmo.variable_dict()
for book in ind_books_available:
self.model.books[book] = pmo.variable(lb=0, ub=1)
self.model.lib_books = pmo.variable_dict()
for lib in ind_libs_available:
for book in self.lib_books_lists[lib]:
self.model.lib_books[lib, book] = pmo.variable(lb=0, ub=1)
self.model.lib_index = pmo.variable_dict()
for lib in ind_libs_available:
for index in ind_index_available:
self.model.lib_index[lib,
index] = pmo.variable(domain=pmo.Binary)
self.model.times_remaining = pmo.variable_dict()
for time in ind_index_available:
self.model.times_remaining[time] = pmo.variable(lb=0,
ub=self.num_days)
self.model.lib_times_remaining = pmo.variable_dict()
for lib in ind_libs_available:
self.model.lib_times_remaining[lib] = pmo.variable(lb=0,
ub=2 *
self.num_days)
self.model.every_book_once = pmo.constraint_list()
for book in ind_books_available:
libs = book_libs_lists_available[book]
self.model.every_book_once.append(
pmo.constraint(
sum(self.model.lib_books[lib, book] for lib in libs) <= 1))
self.model.use_books = pmo.constraint_list()
for book in ind_books_available:
libs = book_libs_lists_available[book]
self.model.use_books.append(
pmo.constraint(self.model.books[book] <= sum(
self.model.lib_books[lib, book] for lib in libs)))
self.model.one_place_per_lib = pmo.constraint_list()
for lib in ind_libs_available:
self.model.one_place_per_lib.append(
pmo.constraint(
sum([
self.model.lib_index[lib, index]
for index in ind_index_available
]) == 1))
self.model.one_lib_per_place = pmo.constraint_list()
for index in ind_index_available:
self.model.one_lib_per_place.append(
pmo.constraint(
sum([
self.model.lib_index[lib, index]
for lib in ind_libs_available
]) == 1))
self.model.use_libs = pmo.constraint_list()
for lib in ind_libs_available:
books = self.lib_books_lists[lib]
for book in books:
self.model.use_libs.append(
pmo.constraint(self.model.lib_books[lib, book] <= sum([
self.model.lib_index[lib, index]
for index in ind_index_available
])))
self.model.remaining_times = pmo.constraint_list()
for time in ind_index_available:
prev_time = self.model.times_remaining[
time - 1] if time else days_available
self.model.remaining_times.append(
pmo.constraint(
self.model.times_remaining[time] == prev_time - sum([
self.model.lib_index[lib, time] * self.lib_days[lib]
for lib in ind_libs_available
])))
self.model.lib_remaining_times = pmo.constraint_list()
for time in ind_index_available:
for lib in ind_libs_available:
self.model.lib_remaining_times.append(
pmo.constraint(
self.model.lib_times_remaining[lib] <=
self.model.times_remaining[time] + days_available *
(1 - self.model.lib_index[lib, time])))
self.model.lib_max_num_books = pmo.constraint_list()
for lib in ind_libs_available:
books = self.lib_books_lists[lib]
self.model.lib_max_num_books.append(
pmo.constraint(
sum([self.model.lib_books[lib, book]
for book in books]) <= self.lib_ships[lib] *
self.model.lib_times_remaining[lib]))
self.model.objective = pmo.objective(sum(
book_points_available[book] * self.model.books[book]
for book in ind_books_available),
sense=-1)
solver = self.get_solver(solverName)
solver_result = solver.solve(self.model)
result = []
for index in range(len(ind_libs_available)):
for lib in ind_libs_available:
if self.model.lib_index.get((lib, index)) != 0:
books = self.lib_books_lists[lib]
result.append((lib, [
book for book in books
if self.model.lib_books.get((lib, book)) != 0
]))
return result
# 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_group1 = pmo.variable_dict()
for _pair in range(5):
model.var_group1[_pair] = pmo.variable(domain=pmo.Binary)
model.var_group2 = pmo.variable_dict()
for _pair in range(5):
model.var_group2[_pair] = pmo.variable(domain=pmo.Binary)
model.var_group3 = pmo.variable_dict()
for _pair in range(5):
model.var_group3[_pair] = pmo.variable(domain=pmo.Binary)
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)
def build_variable_dict():
"""Build a variable_dict with no references to external
objects so its size can be computed."""
return variable_dict(
((i, variable(domain_type=None, lb=None, ub=None, value=None))
for i in range(N)))
# 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:
model.con_worker.append(
pmo.constraint(
sum(model.var_x[(_worker, _task)]
for _task in model.set_tasks) <= 1))
## Each task is assigned to exactly 1 worker
model.con_task = pmo.constraint_list()
for _task in model.set_tasks:
def create_optimization_model(self, config):
self.logger.info('Creating optimization model.')
# Consider using context managers
# limit_sell = False
# GET COMPONENTS FROM SYSTEM
if self.system.has_battery:
battery = self.system.get_battery_object()
if self.system.has_external_grid:
supply = self.system.get_external_grid_object()
# STARTING MODEL
m = pk.block()
# Track the default attributes of the model to be aware of which the user adds.
default_attributes = set(m.__dict__.keys())
default_attributes.add('default_attributes')
# SETS
m.periods = range(config['periods'])
m.E_set = []
# VARIABLES
m.E = pk.variable_dict()
if self.system.has_stochastic_generators and not self.system.has_external_grid:
m.E_set.append('stochastic')
m.E['stochastic'] = pk.variable_list()
for t in m.periods:
m.E['stochastic'].append(
pk.variable(domain_type=pk.RealSet,
lb=0,
ub=self.system.stochastic_electrical_gen[t]))
if self.system.has_external_grid:
m.E_set.append('buy')
m.E_set.append('sell')
m.E['buy'] = pk.variable_list()
for _ in m.periods:
m.E['buy'].append(pk.variable(domain=pk.NonNegativeReals))
m.E['sell'] = pk.variable_list()
for _ in m.periods:
m.E['sell'].append(pk.variable(domain=pk.NonNegativeReals))
m.y_grid = pk.variable_list()
for _ in m.periods:
m.y_grid.append(
pk.variable(domain_type=pk.IntegerSet, lb=0, ub=1))
if self.system.has_battery:
m.E_set.append('batt_chrg')
m.E_set.append('batt_dis')
m.E['batt_chrg'] = pk.variable_list()
for _ in m.periods:
# The upper bound are impose in the constraints below
m.E['batt_chrg'].append(
pk.variable(domain_type=pk.RealSet, lb=0))
m.E['batt_dis'] = pk.variable_list()
for _ in m.periods:
m.E['batt_dis'].append(
pk.variable(domain_type=pk.RealSet, lb=0))
m.y_bat = pk.variable_list()
for _ in m.periods:
m.y_bat.append(
pk.variable(domain_type=pk.IntegerSet, lb=0, ub=1))
m.soc = pk.variable_list()
for _ in m.periods:
m.soc.append(
pk.variable(domain_type=pk.RealSet,
lb=battery.soc_lb,
ub=battery.soc_ub))
# Extra soc variable for the last value of soc that should be >= soc_l
m.soc.append(
pk.variable(domain_type=pk.RealSet,
lb=battery.soc_l,
ub=battery.soc_ub))
# PARAMETERS
if self.system.has_external_grid:
m.prices = {
'buy': supply.electricity_purchase_prices.copy(),
'sell': supply.electricity_selling_prices.copy(),
}
# OBJECTIVE FUNCTION
obj_exp = 0
obj_sense = pk.minimize
if self.system.has_external_grid:
obj_exp = quicksum((m.E['buy'][t] * m.prices['buy'][t] for t in m.periods), linear=True) \
- quicksum((m.E['sell'][t] * m.prices['sell'][t] for t in m.periods), linear=True)
m.obj = pk.objective(obj_exp, sense=obj_sense)
# CONSTRAINTS
# Grid constraints
# if limit_sell:
# m.c_limit_sell = pk.constraint(
# lb=0, body=system['selling_ratio']
# * sum(m.E['buy'][t] + system['E_pv'][t] for t in m.periods)
# - sum(m.E['sell'][t] for t in m.periods))
grid_m = 1e5
m.cl_y_buy = pk.constraint_list()
for t in m.periods:
m.cl_y_buy.append(
pk.constraint(body=m.y_grid[t] * grid_m - m.E['buy'][t], lb=0))
m.cl_y_sell = pk.constraint_list()
for t in m.periods:
m.cl_y_sell.append(
pk.constraint(body=(1 - m.y_grid[t]) * grid_m - m.E['sell'][t],
lb=0))
# Balance constraints
energy_balance_exp = [0 for _ in m.periods]
if self.system.has_fix_loads:
for t in m.periods:
energy_balance_exp[t] = -1 * self.system.fix_electrical_load[t]
if self.system.has_external_grid:
for t in m.periods:
energy_balance_exp[
t] = energy_balance_exp[t] + m.E['buy'][t] - m.E['sell'][t]
if self.system.has_battery:
for t in m.periods:
energy_balance_exp[t] = energy_balance_exp[t] + m.E[
'batt_dis'][t] - m.E['batt_chrg'][t]
if self.system.has_stochastic_generators and not self.system.has_external_grid:
for t in m.periods:
energy_balance_exp[
t] = energy_balance_exp[t] + m.E['stochastic'][t]
else:
for t in m.periods:
energy_balance_exp[t] = energy_balance_exp[
t] + self.system.stochastic_electrical_gen[t]
m.cl_balance = pk.constraint_list()
for t in m.periods:
m.cl_balance.append(
pk.constraint(body=energy_balance_exp[t], rhs=0))
# Battery constraints and restrictions
if self.system.has_battery:
m.soc[0].fix(battery.soc_0)
m.cl_soc = pk.constraint_list()
for t in m.periods:
m.cl_soc.append(
pk.constraint(
body=battery.batt_C * (m.soc[t + 1] - m.soc[t]) +
1 / battery.batt_dis_per * m.E['batt_dis'][t] -
battery.batt_chrg_per * m.E['batt_chrg'][t],
rhs=0))
m.cl_y_char = pk.constraint_list()
for t in m.periods:
m.cl_y_char.append(
pk.constraint(body=m.y_bat[t] * battery.batt_chrg_speed -
m.E['batt_chrg'][t],
lb=0))
m.cl_y_dis = pk.constraint_list()
for t in m.periods:
m.cl_y_char.append(
pk.constraint(
body=(1 - m.y_bat[t]) * battery.batt_dis_speed -
m.E['batt_dis'][t],
lb=0))
# FINISHING
# Determine the user defined attributes (written in this source code) by subtracting the defaults one.
all_attributes = set(m.__dict__.keys())
m.user_defined_attributes = list(all_attributes - default_attributes)
self.optimization_model = m
return m
def get_optimal_books_for_ordered_libs(self,
ind_libs_best,
days_available=None,
book_points_available=None,
solverName="scip"):
if days_available is None:
days_available = self.num_days
if book_points_available is None:
book_points_available = self.book_points
lib_num_books_available = np.zeros(self.num_libs, dtype=int)
ind_libs_available = []
for lib in ind_libs_best:
days_available -= self.lib_days[lib]
lib_num_books_available[lib] = days_available * self.lib_ships[lib]
ind_libs_available = ind_libs_best
ind_books_available = set()
for lib in ind_libs_available:
books = self.lib_books_lists[lib]
for book in books:
if book_points_available[book] != 0:
ind_books_available.add(book)
ind_books_available = np.array(list(ind_books_available))
book_libs_lists_available = [[] for _ in range(self.num_books)]
for lib in ind_libs_available:
books = self.lib_books_lists[lib]
for book in books:
book_libs_lists_available[book].append(lib)
self.model = pmo.block()
self.model.books = pmo.variable_dict()
for book in ind_books_available:
self.model.books[book] = pmo.variable(lb=0, ub=1)
self.model.lib_books = pmo.variable_dict()
for lib in ind_libs_available:
for book in self.lib_books_lists[lib]:
self.model.lib_books[lib,
book] = pmo.variable(domain=pmo.Binary)
self.model.every_book_once = pmo.constraint_list()
for book in ind_books_available:
libs = book_libs_lists_available[book]
self.model.every_book_once.append(
pmo.constraint(
sum(self.model.lib_books[lib, book] for lib in libs) <= 1))
self.model.use_books = pmo.constraint_list()
for book in ind_books_available:
libs = book_libs_lists_available[book]
self.model.use_books.append(
pmo.constraint(self.model.books[book] <= sum(
self.model.lib_books[lib, book] for lib in libs)))
self.model.max_books_per_lib = pmo.constraint_list()
for lib in ind_libs_available:
books = self.lib_books_lists[lib]
num_books = lib_num_books_available[lib]
self.model.max_books_per_lib.append(
pmo.constraint(
sum(self.model.lib_books[lib, book]
for book in books) <= num_books))
self.model.objective = pmo.objective(sum(
book_points_available[book] * self.model.books[book]
for book in ind_books_available),
sense=-1)
solver = self.get_solver(solverName)
solver_result = solver.solve(self.model)
# Can be used to see solver results
# print(solver_result)
result = []
for lib in ind_libs_available:
books = self.lib_books_lists[lib]
result.append((lib, [
book for book in books
if self.model.lib_books.get((lib, book)) != 0
]))
return result
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
# @Variables_dict
m.vd = pmo.variable_dict()
for i in m.s:
m.vd[i] = pmo.variable(ub=9)
# @Variables_dict
# @Variables_list
# used 0-based indexing
m.vl = pmo.variable_list()
for j in m.q:
m.vl.append(pmo.variable(lb=i))
# @Variables_list
# @Constraints_single
m.c = pmo.constraint(sum(m.vd.values()) <= 9)
# @Constraints_single
# @Constraints_dict
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()))
#
# Define a custom tiny_block
#
# The tiny_block class uses more efficient storage for the
vl = pmo.variable_list(pmo.variable() for i in range(10))
cl = pmo.constraint_list()
for i in range(10):
cl.append(pmo.constraint(vl[-1] == 1))
cl.insert(0, pmo.constraint(vl[0]**2 >= 1))
del cl[0]
#
# Dict containers
#
# uses OrderedDict when ordered=True
vd = pmo.variable_dict(((str(i), pmo.variable()) for i in range(10)),
ordered=True)
cd = pmo.constraint_dict((i, pmo.constraint(v == 1)) for i, v in vd.items())
cd = pmo.constraint_dict()
for i, v in vd.items():
cd[i] = pmo.constraint(v == 1)
cd = pmo.constraint_dict()
cd.update((i, pmo.constraint()) for i, v in vd.items())
cd[None] = pmo.constraint()
del cd[None]
#
vl = pmo.variable_list(
pmo.variable() for i in range(10))
cl = pmo.constraint_list()
for i in range(10):
cl.append(pmo.constraint(vl[-1] == 1))
cl.insert(0, pmo.constraint(vl[0]**2 >= 1))
del cl[0]
#
# Dict containers
#
vd = pmo.variable_dict(
((str(i), pmo.variable()) for i in range(10)))
cd = pmo.constraint_dict(
(i, pmo.constraint(v == 1)) for i,v in vd.items())
cd = pmo.constraint_dict()
for i, v in vd.items():
cd[i] = pmo.constraint(v == 1)
cd = pmo.constraint_dict()
cd.update((i, pmo.constraint()) for i,v in vd.items())
cd[None] = pmo.constraint()
del cd[None]
vl = pmo.variable_list(pmo.variable() for i in range(10))
cl = pmo.constraint_list()
for i in range(10):
cl.append(pmo.constraint(vl[-1] == 1))
cl.insert(0, pmo.constraint(vl[0]**2 >= 1))
del cl[0]
#
# Dict containers
#
vd = pmo.variable_dict(((str(i), pmo.variable()) for i in range(10)))
cd = pmo.constraint_dict((i, pmo.constraint(v == 1)) for i, v in vd.items())
cd = pmo.constraint_dict()
for i, v in vd.items():
cd[i] = pmo.constraint(v == 1)
cd = pmo.constraint_dict()
cd.update((i, pmo.constraint()) for i, v in vd.items())
cd[None] = pmo.constraint()
del cd[None]
#
def get_optimal_ordering_and_books_for_subsection(self,
ind_libs_available,
lo,
hi,
solverName="scip"):
days_available = self.num_days
book_points_available = self.book_points
ind_books_available = set()
for lib in ind_libs_available:
books = self.lib_books_lists[lib]
for book in books:
if book_points_available[book] != 0:
ind_books_available.add(book)
ind_books_available = np.array(list(ind_books_available))
book_libs_lists_available = [[] for _ in range(self.num_books)]
for lib in ind_libs_available:
books = self.lib_books_lists[lib]
for book in books:
book_libs_lists_available[book].append(lib)
ind_index_available = np.arange(hi - lo)
ind_libs_available_reorder = ind_libs_available[lo:hi]
days_available_reorder = days_available - self.lib_days[
ind_libs_available[:lo]].sum()
lib_num_books_available = np.zeros(self.num_libs, dtype=int)
days_remaining = days_available
for lib in ind_libs_available:
days_remaining -= self.lib_days[lib]
lib_num_books_available[lib] = days_remaining * self.lib_ships[lib]
self.model = pmo.block()
self.model.books = pmo.variable_dict()
for book in ind_books_available:
self.model.books[book] = pmo.variable(lb=0, ub=1)
self.model.lib_books = pmo.variable_dict()
for lib in ind_libs_available:
for book in self.lib_books_lists[lib]:
self.model.lib_books[lib, book] = pmo.variable(lb=0, ub=1)
self.model.lib_index = pmo.variable_dict()
for lib in ind_libs_available_reorder:
for index in ind_index_available:
self.model.lib_index[lib,
index] = pmo.variable(domain=pmo.Binary)
self.model.times_remaining = pmo.variable_dict()
for time in ind_index_available:
self.model.times_remaining[time] = pmo.variable(lb=0,
ub=self.num_days)
self.model.lib_times_remaining = pmo.variable_dict()
for lib in ind_libs_available_reorder:
self.model.lib_times_remaining[lib] = pmo.variable(lb=0,
ub=2 *
self.num_days)
self.model.every_book_once = pmo.constraint_list()
for book in ind_books_available:
libs = book_libs_lists_available[book]
self.model.every_book_once.append(
pmo.constraint(
sum(self.model.lib_books[lib, book] for lib in libs) <= 1))
self.model.use_books = pmo.constraint_list()
for book in ind_books_available:
libs = book_libs_lists_available[book]
self.model.use_books.append(
pmo.constraint(self.model.books[book] <= sum(
self.model.lib_books[lib, book] for lib in libs)))
self.model.one_place_per_lib = pmo.constraint_list()
for lib in ind_libs_available_reorder:
self.model.one_place_per_lib.append(
pmo.constraint(
sum([
self.model.lib_index[lib, index]
for index in ind_index_available
]) == 1))
self.model.one_lib_per_place = pmo.constraint_list()
for index in ind_index_available:
self.model.one_lib_per_place.append(
pmo.constraint(
sum([
self.model.lib_index[lib, index]
for lib in ind_libs_available_reorder
]) == 1))
self.model.use_libs = pmo.constraint_list()
for lib in ind_libs_available_reorder:
books = self.lib_books_lists[lib]
for book in books:
self.model.use_libs.append(
pmo.constraint(self.model.lib_books[lib, book] <= sum([
self.model.lib_index[lib, index]
for index in ind_index_available
])))
self.model.remaining_times = pmo.constraint_list()
for time in ind_index_available:
prev_time = self.model.times_remaining[
time - 1] if time else days_available_reorder
self.model.remaining_times.append(
pmo.constraint(
self.model.times_remaining[time] == prev_time - sum([
self.model.lib_index[lib, time] * self.lib_days[lib]
for lib in ind_libs_available_reorder
])))
self.model.lib_remaining_times = pmo.constraint_list()
for time in ind_index_available:
for lib in ind_libs_available_reorder:
self.model.lib_remaining_times.append(
pmo.constraint(
self.model.lib_times_remaining[lib] <=
self.model.times_remaining[time] + days_available *
(1 - self.model.lib_index[lib, time])))
self.model.lib_max_num_books = pmo.constraint_list()
for lib in ind_libs_available_reorder:
books = self.lib_books_lists[lib]
self.model.lib_max_num_books.append(
pmo.constraint(
sum([self.model.lib_books[lib, book]
for book in books]) <= self.lib_ships[lib] *
self.model.lib_times_remaining[lib]))
self.model.max_books_per_lib = pmo.constraint_list()
for lib in ind_libs_available:
if not lib in list(ind_libs_available_reorder):
books = self.lib_books_lists[lib]
num_books = lib_num_books_available[lib]
self.model.max_books_per_lib.append(
pmo.constraint(
sum(self.model.lib_books[lib, book]
for book in books) <= num_books))
self.model.objective = pmo.objective(sum(
book_points_available[book] * self.model.books[book]
for book in ind_books_available),
sense=-1)
solver = self.get_solver(solverName)
solver_result = solver.solve(self.model)
# Can be used to see solver results
# print(solver_result)
result = []
for lib in ind_libs_available[:lo]:
books = self.lib_books_lists[lib]
result.append((lib, [
book for book in books
if self.model.lib_books.get((lib, book)) != 0
]))
for index in ind_index_available:
for lib in ind_libs_available_reorder:
if self.model.lib_index.get((lib, index)) != 0:
books = self.lib_books_lists[lib]
result.append((lib, [
book for book in books
if self.model.lib_books.get((lib, book)) != 0
]))
for lib in ind_libs_available[hi:]:
books = self.lib_books_lists[lib]
result.append((lib, [
book for book in books
if self.model.lib_books.get((lib, book)) != 0
]))
return result
def _generate_model(self):
self.model = None
self.model = pmo.block()
model = self.model
model._name = self.description
model.s = [1,2]
model.x_unused = pmo.variable()
model.x_unused.stale = False
model.x_unused_initialy_stale = pmo.variable()
model.x_unused_initialy_stale.stale = True
model.X_unused = pmo.variable_dict(
(i, pmo.variable()) for i in model.s)
model.X_unused_initialy_stale = pmo.variable_dict(
(i, pmo.variable()) for i in model.s)
for i in model.X_unused:
model.X_unused[i].stale = False
model.X_unused_initialy_stale[i].stale = True
model.x = pmo.variable()
model.x.stale = False
model.x_initialy_stale = pmo.variable()
model.x_initialy_stale.stale = True
model.X = pmo.variable_dict(
(i, pmo.variable()) for i in model.s)
model.X_initialy_stale = pmo.variable_dict(
(i, pmo.variable()) for i in model.s)
for i in model.X:
model.X[i].stale = False
model.X_initialy_stale[i].stale = True
model.obj = pmo.objective(model.x + \
model.x_initialy_stale + \
sum(model.X.values()) + \
sum(model.X_initialy_stale.values()))
model.c = pmo.constraint_dict()
model.c[1] = pmo.constraint(model.x >= 1)
model.c[2] = pmo.constraint(model.x_initialy_stale >= 1)
model.c[3] = pmo.constraint(model.X[1] >= 0)
model.c[4] = pmo.constraint(model.X[2] >= 1)
model.c[5] = pmo.constraint(model.X_initialy_stale[1] >= 0)
model.c[6] = pmo.constraint(model.X_initialy_stale[2] >= 1)
# Test that stale flags do not get updated
# on inactive blocks (where "inactive blocks" mean blocks
# that do NOT follow a path of all active parent blocks
# up to the top-level model)
flat_model = model.clone()
model.b = pmo.block()
model.B = pmo.block_dict()
model.B[1] = pmo.block()
model.B[2] = pmo.block()
model.b.b = flat_model.clone()
model.B[1].b = flat_model.clone()
model.B[2].b = flat_model.clone()
model.b.deactivate()
model.B.deactivate(shallow=False)
model.b.b.activate()
model.B[1].b.activate()
model.B[2].b.deactivate()
assert model.b.active is False
assert model.B[1].active is False
assert model.B[1].active is False
assert model.b.b.active is True
assert model.B[1].b.active is True
assert model.B[2].b.active is False
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)
# Solve
opt = pmo.SolverFactory('cplex')
result = opt.solve(model, tee=True)
# Print the solution
packed_items = []
def __init__(
self,
interval_set,
params: dict,
):
super().__init__()
## Setup
# self.id = uuid4()
self.id = params["name"]
self.objective_terms = {}
## Parameters
self.maxMW = aml.parameter(params["maxMW"])
self.minMW = aml.parameter(params["minMW"])
self.max_ramp = aml.parameter(params["max_ramp"])
self.marginal_cost = aml.parameter(params["marginal_cost"])
self.initial_commit = aml.parameter(params["initial_commit"])
## Variables
self.output = aml.variable_dict()
for interval in interval_set:
self.output[interval] = aml.variable(lb=0, ub=self.maxMW)
self.commit = aml.variable_dict()
for interval in interval_set:
self.commit[interval] = aml.variable(domain=aml.Binary)
## Expressions - this is where we define a common interface for model resource elements
# Note in Optopy we have to have a generic and dynamic common interface for market products, different time indices, etc.
# Will just use net_export as an interface for now
self.net_export = aml.expression_dict()
for interval in interval_set:
self.net_export[interval] = aml.expression(expr=self.output)
## Constraints
# Can express constraints abstractly (lhs, rhs, body, ub, lb, etc.) or with interpreted syntax - see below
self.con_output_commit_upper_bound = aml.constraint_dict()
for interval in interval_set:
# body_expr = self.output[interval] - self.commit[interval] * self.maxMW
# self.con_output_commit_upper_bound[interval] = aml.constraint(body=body_expr, ub=0)
self.con_output_commit_upper_bound[interval] = aml.constraint(
self.output[interval] -
self.commit[interval] * self.maxMW <= 0)
self.con_output_commit_lower_bound = aml.constraint_dict()
for interval in interval_set:
# body_expr = self.commit[interval] * self.minMW - self.output[interval]
# self.con_output_commit_lower_bound[interval] = aml.constraint(body=body_expr, ub=0)
self.con_output_commit_lower_bound[interval] = aml.constraint(
self.commit[interval] * self.minMW -
self.output[interval] <= 0)
# todo Add constraints/costs for ramping, min up, min down, startup/shutdown costs, etc.
## Objective Terms
# Unclear whether this expression object needs to be added to block/model - may be enough just to have it in the objective
self.interval_cost = aml.expression_dict()
for interval in interval_set:
self.interval_cost[interval] = aml.expression(
self.marginal_cost * self.output[interval])
self.objective_terms["total_cost"] = sum(self.interval_cost[interval]
for interval in interval_set)
def _generate_model(self):
self.model = pmo.block()
model = self.model
model._name = self.description
model.s = [1, 2]
model.x_unused = pmo.variable(domain=pmo.IntegerSet)
model.x_unused.stale = False
model.x_unused_initialy_stale = pmo.variable(domain=pmo.IntegerSet)
model.x_unused_initialy_stale.stale = True
model.X_unused = pmo.variable_dict(
(i, pmo.variable(domain=pmo.IntegerSet)) for i in model.s)
model.X_unused_initialy_stale = pmo.variable_dict(
(i, pmo.variable(domain=pmo.IntegerSet)) for i in model.s)
for i in model.s:
model.X_unused[i].stale = False
model.X_unused_initialy_stale[i].stale = True
model.x = pmo.variable(domain=RangeSet(None, None))
model.x.stale = False
model.x_initialy_stale = pmo.variable(domain=pmo.IntegerSet)
model.x_initialy_stale.stale = True
model.X = pmo.variable_dict(
(i, pmo.variable(domain=pmo.IntegerSet)) for i in model.s)
model.X_initialy_stale = pmo.variable_dict(
(i, pmo.variable(domain=pmo.IntegerSet)) for i in model.s)
for i in model.s:
model.X[i].stale = False
model.X_initialy_stale[i].stale = True
model.obj = pmo.objective(model.x + \
model.x_initialy_stale + \
sum(model.X.values()) + \
sum(model.X_initialy_stale.values()))
model.c = pmo.constraint_dict()
model.c[1] = pmo.constraint(model.x >= 1)
model.c[2] = pmo.constraint(model.x_initialy_stale >= 1)
model.c[3] = pmo.constraint(model.X[1] >= 0)
model.c[4] = pmo.constraint(model.X[2] >= 1)
model.c[5] = pmo.constraint(model.X_initialy_stale[1] >= 0)
model.c[6] = pmo.constraint(model.X_initialy_stale[2] >= 1)
# Test that stale flags do not get updated
# on inactive blocks (where "inactive blocks" mean blocks
# that do NOT follow a path of all active parent blocks
# up to the top-level model)
flat_model = model.clone()
model.b = pmo.block()
model.B = pmo.block_dict()
model.b.b = flat_model.clone()
model.B[1] = pmo.block()
model.B[1].b = flat_model.clone()
model.B[2] = pmo.block()
model.B[2].b = flat_model.clone()
model.b.deactivate()
model.B.deactivate(shallow=False)
model.b.b.activate()
model.B[1].b.activate()
model.B[2].b.deactivate()
assert model.b.active is False
assert model.B[1].active is False
assert model.B[1].active is False
assert model.b.b.active is True
assert model.B[1].b.active is True
assert model.B[2].b.active is False
# 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
# @Variables_dict
m.vd = pmo.variable_dict()
for i in m.s:
m.vd[i] = pmo.variable(ub=9)
# @Variables_dict
# @Variables_list
# used 0-based indexing
m.vl = pmo.variable_list()
for j in m.q:
m.vl.append(
pmo.variable(lb=i))
# @Variables_list
# @Constraints_single
