def _testParameters(self, modelType):
mdl = Model(modelType, "parameters")
mdl.set_parameters(MIP_Gap=0.01)
def solve(dat, progress):
assert isinstance(progress, Progress)
assert input_schema.good_tic_dat_object(dat)
assert not input_schema.find_foreign_key_failures(dat)
assert not input_schema.find_data_type_failures(dat)
full_parameters = input_schema.create_full_parameters_dict(dat)
def get_distance(x, y):
if (x, y) in dat.distance:
return dat.distance[x, y]["Distance"]
if (y, x) in dat.distance:
return dat.distance[y, x]["Distance"]
return float("inf")
def can_assign(x, y):
return dat.sites[y]["Center Status"] == "Can Be Center" \
and get_distance(x,y)<float("inf")
m = Model(model_name="cog", model_type=full_parameters["Core Model Type"])
assign_vars = {(n, assigned_to): m.add_var(type="binary",
name="%s_%s" % (n, assigned_to))
for n in dat.sites for assigned_to in dat.sites
if can_assign(n, assigned_to)}
open_vars = {
n: m.add_var(type="binary", name="open_%s" % n)
for n in dat.sites if dat.sites[n]["Center Status"] == "Can Be Center"
}
assert open_vars, "nothing can be center"
assign_slicer = Slicer(assign_vars)
for n, r in dat.sites.items():
if r["Demand"] > 0:
m.add_constraint(m.sum(
assign_vars[n, assign_to]
for _, assign_to in assign_slicer.slice(n, "*")) == 1,
name="must_assign_%s" % n)
for assigned_to, r in dat.sites.items():
if r["Center Status"] == "Can Be Center":
_assign_vars = [
assign_vars[n, assigned_to]
for n, _ in assign_slicer.slice("*", assigned_to)
]
# this is the weak formulation
m.add_constraint(m.sum(_assign_vars) <=
len(_assign_vars) * open_vars[assigned_to],
name="weak_force_open%s" % assigned_to)
number_of_centroids = full_parameters["Number of Centroids"]
m.add_constraint(m.sum(v
for v in open_vars.values()) == number_of_centroids,
name="numCentroids")
m.set_parameters(MIP_Gap=full_parameters["MIP Gap"])
m.set_objective(m.sum(var * get_distance(n, assigned_to) *
dat.sites[n]["Demand"]
for (n, assigned_to), var in assign_vars.items()),
sense="minimize")
if full_parameters["Core Model Type"] == "cplex":
progress.add_cplex_listener("COG Optimization", m.core_model)
worked = m.optimize(*(
[progress.gurobi_call_back_factory("COG Optimization", m.core_model)]
if full_parameters["Core Model Type"] == "gurobi" else []))
assert worked, "testing model set up only for success"
sln = solution_schema.TicDat()
if full_parameters["Core Model Type"] == "gurobi":
sln.parameters["Lower Bound"] = getattr(m.core_model, "objBound",
m.core_model.objVal)
sln.parameters["Upper Bound"] = m.core_model.objVal
else:
lb = m.core_model.solve_details.best_bound
sln.parameters["Lower Bound"] = worked.get_objective_value() if isnan(
lb) else lb
sln.parameters["Upper Bound"] = worked.get_objective_value()
def almostone(x):
return abs(x - 1) < 0.0001
for (n, assigned_to), var in assign_vars.items():
if almostone(m.get_solution_value(var)):
sln.assignments[n, assigned_to] = {}
for n, var in open_vars.items():
if almostone(m.get_solution_value(var)):
sln.openings[n] = {}
return sln