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import itertools
# Define the distances between the airports
distances = {
('SIN', 'LAX'): 14101.48,
('LAX', 'JFK'): 3974.20,
('JFK', 'CDG'): 5833.66,
}
# Define the factors for each route segment
route_factors = {('SIN', 'LAX'): {'weather': 'clear sky', 'temperature': 27.18}, ('LAX', 'JFK'): {'weather': 'clear sky', 'temperature': 25.37}, ('JFK',
'CDG'): {'weather': 'clear sky', 'temperature': 21.18}}
# Ensure the graph is bidirectional (undirected)
for (a, b), dist in list(distances.items()):
distances[(b, a)] = dist
for (a, b), factors in list(route_factors.items()):
route_factors[(b, a)] = factors
# Function to assign a risk factor based on weather
def weather_risk(weather):
risk_factors = {
"clear sky": 0.1,
"few clouds": 0.2,
"scattered clouds": 0.3,
"broken clouds": 0.4,
"overcast clouds": 0.5,
"light rain": 0.6,
"rain": 0.7,
"storm": 0.9
}
return risk_factors.get(weather, 0.5) # Default risk factor if not listed
# Function to normalize temperature impact
def temperature_impact(temperature):
# Assuming ideal temperature for fuel efficiency is around 20-25°C
if temperature < 20 or temperature > 25:
return abs(temperature - 22.5) / 30 # Normalize to a value between 0 and 1
return 0.1 # Low impact in the ideal range
# Calculate the adjusted cost for each route segment
def calculate_adjusted_cost(segment, base_distance):
if segment not in route_factors:
segment = (segment[1], segment[0]) # Check the reversed segment
weather = route_factors[segment]["weather"]
temperature = route_factors[segment]["temperature"]
weather_cost = weather_risk(weather) * 100 # Weight for weather impact
temperature_cost = temperature_impact(temperature) * 50 # Weight for temperature impact
total_cost = base_distance + weather_cost + temperature_cost
return total_cost
# Update the distance function to include additional factors
def calculate_route_distance(route, distances):
"""Calculate the total cost for a given route, including additional factors."""
total_distance = 0
for i in range(len(route) - 1):
segment = (route[i], route[i + 1])
if segment not in distances:
segment = (route[i + 1], route[i])
base_distance = distances[segment]
total_distance += calculate_adjusted_cost(segment, base_distance)
# Add distance to return to the starting point
last_segment = (route[-1], route[0])
if last_segment not in distances:
last_segment = (route[0], route[-1])
base_distance = distances[last_segment]
total_distance += calculate_adjusted_cost(last_segment, base_distance)
return total_distance
def find_optimal_route(airports, distances):
"""Find the optimal route that covers all airports."""
best_route = None
min_distance = float('inf')
# Generate all possible permutations of the route
for route in itertools.permutations(airports):
current_distance = calculate_route_distance(route, distances)
if current_distance < min_distance:
min_distance = current_distance
best_route = route
return best_route, min_distance
# List of all airports
airports = ['SIN', 'LAX', 'JFK', 'CDG']
# Find the optimal route with the new cost metric
optimal_route, optimal_distance = find_optimal_route(airports, distances)
print("Optimal Route:", " -> ".join(optimal_route) + f" -> {optimal_route[0]}")
print("Total Adjusted Distance/Cost:", optimal_distance)
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