def test_pulp_075(self):
# Column Based modelling of test_pulp_1 with empty constraints
prob = LpProblem("test075", const.LpMinimize)
obj = LpConstraintVar("obj")
# constraints
a = LpConstraintVar("C1", const.LpConstraintLE, 5)
b = LpConstraintVar("C2", const.LpConstraintGE, 10)
c = LpConstraintVar("C3", const.LpConstraintEQ, 7)
prob.setObjective(obj)
prob += a
prob += b
prob += c
# Variables
x = LpVariable("x", 0, 4, const.LpContinuous, obj + b)
y = LpVariable("y", -1, 1, const.LpContinuous, 4 * obj - c)
z = LpVariable("z", 0, None, const.LpContinuous, 9 * obj + b + c)
if self.solver.__class__ in [
CPLEX_DLL, CPLEX_CMD, COINMP_DLL, YAPOSIB, PYGLPK
]:
print("\t Testing column based modelling with empty constraints")
pulpTestCheck(prob, self.solver, [const.LpStatusOptimal], {
x: 4,
y: -1,
z: 6
})
def generate_xtreme_point(self):
obj = LpConstraintVar()
for v in self.prob.variables():
obj.addVariable(v, self.piCurrent[v.name])
self.prob.setObjective(obj)
self.prob.solve()
#solvers.COIN_CMD.solve(self.prob)
for v in self.var:
self.currentXtremePoint[v] = self.var[v].value()
if self.output == 1:
currentXtremePointList = self.currentXtremePoint.items()
currentXtremePointList.sort()
for v in currentXtremePointList:
print v[0]+'\t', v[1]
self.extremePoints.append(self.currentXtremePoint.values())
return self.prob.objective.value()
def generate_xtreme_point(self):
obj = LpConstraintVar()
for v in self.prob.variables():
obj.addVariable(v, self.piCurrent[v.name])
self.prob.setObjective(obj)
self.prob.solve()
#solvers.COIN_CMD.solve(self.prob)
for v in self.var:
self.currentXtremePoint[v] = self.var[v].value()
if self.output == 1:
currentXtremePointList = self.currentXtremePoint.items()
currentXtremePointList.sort()
for v in currentXtremePointList:
print v[0] + '\t', v[1]
self.extremePoints.append(self.currentXtremePoint.values())
return self.prob.objective.value()
def _formulate_master(self):
"""
Formulate set covering formulation for aircraft recovery
"""
# Variable dicts
a_dict = {
(s_label[1], s): s.cost for s_label, s in self.Data.scheds.items()
}
dum_dict = {
f: 20000 + 20000 * (f.arrival - f.departure)
for f in self.Data.flights
}
# CONSTRAINTS #
# Generate empty >= constraints for all flights
self.master.flight_constrs = {
key: LpConstraintVar(key, LpConstraintGE, 1) for key in dum_dict
}
# Generate empty <= constraints for all aircraft
self.master.assign_constrs = {
k: LpConstraintVar(k, LpConstraintLE, 1) for k in self.Data.aircraft
}
# VARIABLES #
# Create dummy variables for each flight
dummy = {
key: LpVariable(
'dummy{0}'.format(key), 0, 1, LpBinary,
lpSum(val * self.master.objective +
self.master.flight_constrs[key]))
for key, val in dum_dict.items()
}
# Create assignment variables for each aircraft (and schedule)
a = {
key_tuple:
LpVariable('a[{}]'.format(key_tuple), 0, 1, LpBinary,
lpSum(self.master.assign_constrs[key_tuple[0]]))
for key_tuple, val in a_dict.items()
}
# Add flight constraints to model
for constr in self.master.flight_constrs.values():
self.master.model += constr
# Add assignment constraints to the model
for constr in self.master.assign_constrs.values():
self.master.model += constr
# Set objective function
self.master.model.sense = LpMinimize
self.master.model.setObjective(self.master.objective)
def test_pulp_070(self):
# Column Based modelling of test_pulp_1
prob = LpProblem("test070", const.LpMinimize)
obj = LpConstraintVar("obj")
# constraints
a = LpConstraintVar("C1", const.LpConstraintLE, 5)
b = LpConstraintVar("C2", const.LpConstraintGE, 10)
c = LpConstraintVar("C3", const.LpConstraintEQ, 7)
prob.setObjective(obj)
prob += a
prob += b
prob += c
# Variables
x = LpVariable("x", 0, 4, const.LpContinuous, obj + a + b)
y = LpVariable("y", -1, 1, const.LpContinuous, 4 * obj + a - c)
z = LpVariable("z", 0, None, const.LpContinuous, 9 * obj + b + c)
print("\t Testing column based modelling")
pulpTestCheck(prob, self.solver, [const.LpStatusOptimal], {x: 4, y: -1, z: 6})
def test_pulp_090(self):
# Column Based modelling of test_pulp_1 with a resolve
prob = LpProblem("test090", const.LpMinimize)
obj = LpConstraintVar("obj")
# constraints
a = LpConstraintVar("C1", const.LpConstraintLE, 5)
b = LpConstraintVar("C2", const.LpConstraintGE, 10)
c = LpConstraintVar("C3", const.LpConstraintEQ, 7)
prob.setObjective(obj)
prob += a
prob += b
prob += c
prob.setSolver(self.solver) # Variables
x = LpVariable("x", 0, 4, const.LpContinuous, obj + a + b)
y = LpVariable("y", -1, 1, const.LpContinuous, 4 * obj + a - c)
prob.resolve()
z = LpVariable("z", 0, None, const.LpContinuous, 9 * obj + b + c)
if self.solver.__class__ in [CPLEX_DLL, COINMP_DLL]:
print("\t Testing resolve of problem")
prob.resolve()
def __init__(self, Data):
"""
Formulate the master problem.
Parameters
----------
Data : object,
classes.Data object with problem data.
"""
self.Data = Data
self.master = Expand()
# Init lp relaxation of the master problem
self.relax = Expand()
# Init master problem with empty constraints and objective
self.master.model = LpProblem("master problem")
self.master.objective = LpConstraintVar("obj")
# Container for flight constraints
self.master.flight_constrs = {}
# Container for assignment constraints
self.master.assign_constrs = {}
self._formulate_master()
