Spaces:
Sleeping
Sleeping
| 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) | |