def swap(solution):
calls = get_calls_including_zeroes(solution)
vehicle = random.randrange(1, x.vehicles)
route = calls[vehicle]
it = 0
while not route or len(route) <= 3:
vehicle = random.randrange(1, x.vehicles)
route = calls[vehicle]
if it == 25:
return solution
else:
it += 1
new_route = route
new_calls = calls
new_calls[vehicle] = new_route
it = 0
while f(calls_to_solution(new_calls)) > f(solution) and not check_solution(
calls_to_solution(new_calls)):
rand1 = random.randrange(0, len(new_route))
rand2 = random.randrange(0, len(new_route))
if rand1 == 0 or rand2 == 0 or (rand1 == rand2):
continue
new_route[rand1], new_route[rand2] = new_route[rand2], new_route[rand1]
new_calls[vehicle] = new_route
if it == 100:
return solution
else:
it += 1
c = calls_to_solution(new_calls)
return c
def try_for_best(solution):
# print("Starting solution:", solution)
new_solutions = []
calls = get_calls_including_zeroes(solution)
dummy_calls = calls[x.vehicles + 1]
# print("Dummy calls:", dummy_calls)
most_expensive_calls = get_most_expensive_calls(solution)
most_expensive_call = 0
for key in most_expensive_calls.keys():
# print("Key:", key)
if key in dummy_calls:
most_expensive_call = key
break
if most_expensive_call == 0:
return solution
# print(most_expensive_call)
dummy_most_expensive_removed = [i for i in dummy_calls if i != most_expensive_call]
calls[x.vehicles + 1] = dummy_most_expensive_removed
# print(dummy_most_expensive_removed)
vehicle = random.randrange(1, len(x.vehicles_dict))
# print("Vehicle", vehicle)
new_calls = copy.deepcopy(calls)
new_calls[vehicle].insert(0, most_expensive_call)
if len(calls[vehicle]) > 1:
for index in range(len(calls[vehicle])):
# print("Calls in loop:", calls)
newnew_calls = copy.deepcopy(new_calls)
newnew_calls[vehicle].insert(index + 1, most_expensive_call)
new_solutions.append(calls_to_solution(newnew_calls))
# print("New calls in loop:", newnew_calls)
else:
# print("Calls:", calls)
new_calls[vehicle].insert(0, most_expensive_call)
# print("New calls:", new_calls)
new_solutions.append(calls_to_solution(new_calls))
best_solution = solution
# print("New solutions:", new_solutions)
for sol in new_solutions:
if f(sol) < f(best_solution) or random.uniform(0, 1) < 0.25:
# print("Chosen solution:", sol)
return sol
return solution
def tabu_shuffle(solution):
calls = get_calls_including_zeroes(solution)
rand_vehicle = random.randrange(1, x.vehicles)
if len(calls[rand_vehicle]) < 2:
return solution
current = solution
new_sol = swingers(solution)
# print("New sol:", new_sol)
if f(new_sol) < f(current):
current = new_sol
elif check_solution(new_sol) and random.uniform(0, 1) < 0.33:
current = new_sol
return current
def one_reinsert_most_expensive_from_dummy(solution):
most_expensive_calls = most_expensive_dummy(solution)
if not most_expensive_calls:
return solution
calls = get_calls_including_zeroes(solution)
chosen_call = next(iter(most_expensive_calls))
vehicle = 0
for i in range(1, len(calls.keys())):
if chosen_call in x.vehicles_dict.get(i).valid_calls:
vehicle = i
break
if vehicle == 0:
return solution
calls[x.vehicles + 1] = [i for i in calls[x.vehicles + 1] if i != chosen_call]
calls[vehicle].insert(0, chosen_call)
calls[vehicle].insert(0, chosen_call)
return calls_to_solution(calls)
def swingers(solution):
# print(solution)
calls = route_planner(solution)
if not calls:
return solution
calls_w_0 = get_calls_including_zeroes(solution)
# print("All calls:", calls)
for vehicle in calls:
# print("Vehicle:", vehicle)
# print("Calls:", calls[vehicle])
if len(calls[vehicle]) <= 2:
continue
else:
calls_w_0[vehicle] = neighbor_switch(calls, vehicle)
# print(calls_w_0)
calls_w_0[vehicle].append(0)
# print("Calls after a swing:", calls_to_solution(calls_w_0))
return calls_to_solution(calls_w_0)
def one_reinsert_most_expensive_call(solution):
calls = get_calls_including_zeroes(solution)
if not calls[x.vehicles + 1]:
return solution
cost_no_transport = get_most_expensive_calls(solution)
cnt_list = list(cost_no_transport.keys())
if not cnt_list or len(cnt_list) < 3:
return solution
most_expensive_call = cnt_list[random.randrange(0, 3)]
if most_expensive_call in calls[x.vehicles + 1]:
calls[x.vehicles + 1].remove(most_expensive_call)
calls[x.vehicles + 1].remove(most_expensive_call)
for i in range(1, x.vehicles + 2):
vehicle = x.vehicles_dict[i]
if most_expensive_call in vehicle.valid_calls:
calls[i].insert(random.randrange(0, len(calls[i])), most_expensive_call)
calls[i].insert(random.randrange(0, len(calls[i])), most_expensive_call)
break
return calls_to_solution(calls)
def try_for_best(solution):
if solution in tested_solutions:
return solution
tested_solutions.append(solution)
new_solutions = []
calls = get_calls_including_zeroes(solution)
dummy_calls = calls[x.vehicles + 1]
most_expensive_calls = most_expensive_dummy(solution)
mec = list(most_expensive_calls.keys())
most_expensive_call = mec.pop(0)
calls_wo_dummy = copy.deepcopy(calls)
calls_wo_dummy.pop(x.vehicles + 1)
sample = random.sample(calls_wo_dummy.keys(), 2)
dummy_most_expensive_removed = [i for i in dummy_calls if i != most_expensive_call]
calls[x.vehicles + 1] = dummy_most_expensive_removed
for vehicle in sample:
new_calls = copy.deepcopy(calls)
new_calls[vehicle].insert(0, most_expensive_call)
if len(calls[vehicle]) > 1:
for index in range(len(calls[vehicle])):
# print("Calls in loop:", calls)
newnew_calls = copy.deepcopy(new_calls)
newnew_calls[vehicle].insert(index + 1, most_expensive_call)
if calls_to_solution(newnew_calls) in tested_solutions:
continue
else:
new_solutions.append(calls_to_solution(newnew_calls))
tested_solutions.append(calls_to_solution(newnew_calls))
# print("New calls in loop:", newnew_calls)
else:
# print("Calls:", calls)
new_calls[vehicle].insert(0, most_expensive_call)
# print("New calls:", new_calls)
new_solutions.append(calls_to_solution(new_calls))
tested_solutions.append(calls_to_solution(new_calls))
best_solution = solution
for sol in new_solutions:
if f(sol) < f(best_solution) and check_solution(sol):
return sol
return solution
def best_route(solution):
calls = route_planner(solution)
vehicle = random.randrange(1, x.vehicles + 1)
if not calls[vehicle]:
return solution
route = calls[vehicle]
global tested_solutions
if route in tested_solutions:
return solution
tested_solutions.append(route)
# if len(route) > 7:
# return solution
all_combs = list(set(itertools.permutations(route)))
nl = [list(row) for row in all_combs]
best_solution = solution
for c in nl:
rt = copy.deepcopy(get_calls_including_zeroes(solution))
rt[vehicle] = c
rt[vehicle].append(0)
# print(calls_to_solution(rt))
if f(calls_to_solution(rt)) < f(best_solution):
best_solution = calls_to_solution(rt)
return best_solution