<｜begin▁of▁sentence｜>### Instruction:
Imagine we're setting up a special event across multiple venues, and we want to design a route for our server 'D' to deliver refreshments efficiently to stations 'A', 'B', and 'C'. Each path between the stations has a different time 'cost' associated, forming a network of deliveries - exactly like the times it takes to move from one station to another, which have been noted as follows: moving from 'A' to 'B' takes 3 minutes, 'A' to 'C' is 17 minutes, 'A' to 'D' is 14 minutes; from 'B' back to 'A' is also 3 minutes, 'B' to 'C' is 12 minutes, 'B' to 'D' is 16 minutes; from 'C' to 'A' is 13 minutes, 'C' to 'B' is 12 minutes, 'C' to 'D' is 4 minutes; and from 'D' back to 'A' is 14 minutes, 'D' to 'B' is 15 minutes, and 'D' to 'C' is 2 minutes. Could we find the most time-effective route for our server 'D' to visit each station once, ensuring they end back at their starting point, by using a savvy scheduling strategy, similar to the threshold accepting method, so we can keep our guests happy with prompt service?
### Response:
from networkx.algorithms import approximation as approx
import networkx as nx

# Create a weighted directed graph
G = nx.DiGraph()
G.add_weighted_edges_from([
    ('A', 'B', 3), ('A', 'C', 17), ('A', 'D', 14), ('B', 'A', 3),
    ('B', 'C', 12), ('B', 'D', 16), ('C', 'A', 13), ('C', 'B', 12),
    ('C', 'D', 4), ('D', 'A', 14), ('D', 'B', 15), ('D', 'C', 2)
])

# Approximate the TSP problem using threshold_accepting method
init_cycle = 'greedy'
source_node = 'D'
threshold = 1
max_iterations = 100
N_inner = 10
alpha = 0.1
seed = None

cycle = approx.threshold_accepting_tsp(G, init_cycle, source=source_node,
                                          threshold=threshold, max_iterations=max_iterations,
                                          N_inner=N_inner, alpha=alpha, seed=seed)

# Calculate the total cost of the cycle
cost = sum(G[n][nbr]['weight'] for n, nbr in nx.utils.pairwise(cycle))

print('Cycle:', cycle)
print('Cost:', cost)
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