# get nr of constraints in model
nrConstraints = model.num_rows
print("The mode consists of " + str(nrVariables) + " decision variables and " +
str(nrConstraints) + " constraints.")
print("Optimization process started.")
# get time limit in seconds for optimization
timeInSeconds = optimization_parameters.loc["timeInSeconds", "Value"]
# get mip gap
mipGap = optimization_parameters.loc["mipGap", "Value"]
# if time limit was given use this time limit else default value: +inf
if not pd.isna(timeInSeconds):
model.max_seconds = timeInSeconds
print("Maximal solution time: " + str(timeInSeconds) + " seconds")
# if mip gap was given use this gap else default value: 1e-4
if not pd.isna(mipGap):
model.max_mip_gap = mipGap
print("MIP gap set to " + str(mipGap * 100) + "%")
# start optimizing
status = model.optimize()
solved = False
# optimal solution found
if status == OptimizationStatus.OPTIMAL:
if model.gap < 1e-4:
def test_tsp_cuts(solver: str):
"""tsp related tests"""
N = ["a", "b", "c", "d", "e", "f", "g"]
n = len(N)
i0 = N[0]
A = {
("a", "d"): 56,
("d", "a"): 67,
("a", "b"): 49,
("b", "a"): 50,
("d", "b"): 39,
("b", "d"): 37,
("c", "f"): 35,
("f", "c"): 35,
("g", "b"): 35,
("b", "g"): 25,
("a", "c"): 80,
("c", "a"): 99,
("e", "f"): 20,
("f", "e"): 20,
("g", "e"): 38,
("e", "g"): 49,
("g", "f"): 37,
("f", "g"): 32,
("b", "e"): 21,
("e", "b"): 30,
("a", "g"): 47,
("g", "a"): 68,
("d", "c"): 37,
("c", "d"): 52,
("d", "e"): 15,
("e", "d"): 20,
}
# input and output arcs per node
Aout = {n: [a for a in A if a[0] == n] for n in N}
Ain = {n: [a for a in A if a[1] == n] for n in N}
m = Model(solver_name=solver)
m.verbose = 0
x = {
a: m.add_var(name="x({},{})".format(a[0], a[1]), var_type=BINARY)
for a in A
}
m.objective = xsum(c * x[a] for a, c in A.items())
for i in N:
m += xsum(x[a] for a in Aout[i]) == 1, "out({})".format(i)
m += xsum(x[a] for a in Ain[i]) == 1, "in({})".format(i)
# continuous variable to prevent subtours: each
# city will have a different "identifier" in the planned route
y = {i: m.add_var(name="y({})".format(i), lb=0.0) for i in N}
# subtour elimination
for (i, j) in A:
if i0 not in [i, j]:
m.add_constr(y[i] - (n + 1) * x[(i, j)] >= y[j] - n)
m.cuts_generator = SubTourCutGenerator()
# tiny model, should be enough to find the optimal
m.max_seconds = 10
m.max_nodes = 100
m.max_solutions = 1000
m.optimize()
assert m.status == OptimizationStatus.OPTIMAL # "mip model status"
assert abs(m.objective_value - 262) <= TOL # "mip model objective"
L = [cost for i in range(50)]
sl, cur_cost, best = seq.copy(), cost, cost
for it in range(5000000):
(i, j) = rnd.randint(1, len(sl) - 2), rnd.randint(1, len(sl) - 2)
dlt = delta(c, sl, i, j)
if cur_cost + dlt <= L[it % len(L)]:
sl[i], sl[j], cur_cost = sl[j], sl[i], cur_cost + dlt
if cur_cost < best:
seq, best = sl.copy(), cur_cost
L[it % len(L)] = cur_cost
print('improved cost %g' % best)
model.start = [(x[seq[i]][seq[i + 1]], 1) for i in range(len(seq) - 1)]
model.max_seconds = timeLimit
model.threads = threads
model.optimize()
end = time()
print(model.status)
print(model.solver_name)
objv = 1e20
gap = 1e20
n_nodes = 0
if model.status in [OptimizationStatus.OPTIMAL, OptimizationStatus.FEASIBLE]:
out.write('route with total distance %g found: 0' %
(model.objective_value))
