def simulated_annealing(sol,
nodes,
vehicles,
old_z,
temp0=25,
r=0.65,
tempf=10**-3):
min_neighbor = copy.deepcopy(sol)
temp = temp0
while temp > tempf:
neighborhood = two_opt(min_neighbor, nodes, 0, 0.5)
min_z = np.inf
for neighbor in neighborhood:
z = of(vehicles, nodes, neighbor)
if z < min_z:
min_neighbor = neighbor
min_z = z
delta = min_z - old_z
if delta < 0 or np.random.rand() < np.exp(-delta / temp):
s = copy.deepcopy(min_neighbor)
old_z = min_z
temp *= r
s = two_opt(s, nodes)
z = of(vehicles, nodes, s)
return s, z
def one_one_exchange(sol, nodes, vehicles, old_z):
new_sol = copy.deepcopy(sol)
prev_sol = copy.deepcopy(sol)
for r1 in range(len(new_sol) - 1):
for r2 in range(r1, len(new_sol)):
for i in range(1, len(new_sol[r1]) - 1):
for j in range(1, len(new_sol[r2]) - 1):
new_sol[r1][i], new_sol[r2][j] = new_sol[r2][j], new_sol[
r1][i]
nodes['assigned'][i][1], nodes['assigned'][j][1] = nodes[
'assigned'][j][1], nodes['assigned'][i][1]
if check_route(new_sol[r1], nodes, vehicles['capacity'][nodes['assigned'][i][1]]) and \
check_route(new_sol[r2], nodes, vehicles['capacity'][nodes['assigned'][j][1]]):
new_z = of(vehicles, nodes, new_sol)
if new_z - old_z < 0:
prev_sol = copy.deepcopy(new_sol)
old_z = new_z
else:
new_sol[r1][i], new_sol[r2][j] = new_sol[r2][
j], new_sol[r1][i]
nodes['assigned'][i][1], nodes['assigned'][j][1] = nodes['assigned'][j][1], \
nodes['assigned'][i][1]
else:
new_sol[r1][i], new_sol[r2][j] = new_sol[r2][
j], new_sol[r1][i]
nodes['assigned'][i][1], nodes['assigned'][j][1] = nodes['assigned'][j][1], \
nodes['assigned'][i][1]
return prev_sol, old_z
def vnd(sol, nodes, vehicles, old_z, l=5):
j = 0
new_sol = copy.deepcopy(sol)
z = old_z
while j <= l:
if j % 3 == 0:
new_sol = two_opt(new_sol, nodes)
z = of(vehicles, nodes, new_sol)
elif j % 3 == 1:
new_sol, z = one_one_exchange(new_sol, nodes, vehicles, z)
elif j % 3 == 2:
new_sol, z = move(new_sol, nodes, vehicles, z)
j += 1
return new_sol, z
def lower_bound_vrp(nodes, vehicles):
min_cap = max(vehicles['capacity'])
max_vel = min(vehicles['velocity'])
lb_routes = []
ideal_vehicle = {'index': [0], 'num_vehicles': [float('Inf')], 'capacity': [min_cap], 'velocity': [max_vel]}
for node in nodes['index']:
lb_routes.append([node])
nodes['assigned'] = []
for i in range(len(nodes['index'])):
nodes['assigned'].append([1, 0])
sol_lb = of(ideal_vehicle, nodes, lb_routes)
return sol_lb
def swap(sol, nodes, vehicles, old_z):
new_sol = copy.deepcopy(sol)
prev_sol = copy.deepcopy(sol)
for r in range(len(new_sol)):
route = new_sol[r]
n = len(route)
for i in range(1, n - 2):
for j in range(i + 1, n - 1):
route[i], route[j] = route[i], route[j]
new_z = of(vehicles, nodes, new_sol)
if new_z < old_z:
route_prev = prev_sol[r]
route_prev[i], route_prev[j] = route_prev[j], route_prev[i]
old_z = new_z
else:
route[i], route[j] = route[i], route[j]
return prev_sol, old_z
def move(sol, nodes, vehicles, old_z):
new_sol = copy.deepcopy(sol)
prev_sol = copy.deepcopy(sol)
for r in range(len(new_sol)):
route = new_sol[r]
n = len(route)
if len(route) > 4:
for i in range(1, n - 2):
for j in range(i + 1, n - 1):
route.insert(j, route.pop(i))
z = of(vehicles, nodes, new_sol)
if z < old_z:
route_prev = prev_sol[r]
route_prev.insert(j, route_prev.pop(i))
old_z = z
else:
route.insert(j, route.pop(i))
return prev_sol, old_z
from sweepHCCVRP import sweep, of
from lowerBoundHCCVRP import lower_bound_vrp
from ioData import read_data, print_routes
for i in range(1, 22):
# Read data set
vehicles, nodes = read_data(i)
# Calculate lower bound
lb = lower_bound_vrp(nodes, vehicles)
# Constructive Algorithm
sol = sweep(vehicles, nodes)
# Calculate Objective Function
z = of(vehicles, nodes, sol)
# Output data
print_routes(i, vehicles, nodes, sol)
