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DESCRIPTION = '''The Capacitated Vehicle Routing Problem (CVRP) is a classic optimization problem that extends the Traveling Salesman Problem. In the CVRP, a fleet of vehicles with limited capacity must service a set of customers with specific demands, starting and ending at a central depot. Each customer must be visited exactly once by exactly one vehicle, and the total demand of customers on a single vehicle's route cannot exceed the vehicle's capacity. The objective is to minimize the total travel distance while satisfying all customer demands and vehicle capacity constraints.'''
import numpy as np
import math
def solve(**kwargs):
"""
Solve a CVRP instance.
Args:
- nodes (list): List of (x, y) coordinates representing locations (depot and customers)
Format: [(x1, y1), (x2, y2), ..., (xn, yn)]
- demands (list): List of customer demands, where demands[i] is the demand for customer i
Format: [d0, d1, d2, ..., dn]
- capacity (int): Vehicle capacity
- depot_idx (int): Index of the depot in the nodes list (typically 0)
Returns:
dict: Solution information with:
- 'routes' (list): List of routes, where each route is a list of node indices
Format: [[0, 3, 1, 0], [0, 2, 5, 0], ...] where 0 is the depot
"""
# This is a placeholder implementation
# Your solver implementation would go here
# Your function must yield multiple solutions over time, not just return one solution
# Use Python's yield keyword repeatedly to produce a stream of solutions
# Each yielded solution should be better than the previous one
while True:
yield {
'routes': [],
}
def load_data(file_path):
"""
Load CVRP instances from .vrp files.
Args:
file_path (str): Path to the file containing CVRP instances
Returns:
list: List of dictionaries, each containing a CVRP instance with:
- 'nodes': List of (x, y) coordinates
- 'demands': List of customer demands
- 'capacity': Vehicle capacity
- 'depot_idx': Index of the depot (typically 0)
- 'optimal_routes': List of optimal routes (if available)
"""
instances = []
try:
n, capacity, coordinates, demands, dist_matrix, depot_idx = read_vrp_file(file_path)
# Create a dictionary for this instance
instance = {
'nodes': coordinates,
'demands': demands,
'capacity': capacity,
'depot_idx': depot_idx,
'dist_matrix': dist_matrix,
'optimal_routes': None # Typically not available in .vrp files
}
instances.append(instance)
except Exception as e:
print(f"Error processing file {file_path}: {e}")
return instances
def read_vrp_file(filename):
"""Read CVRP Problem instance from a .vrp file."""
with open(filename, 'r') as f:
lines = f.readlines()
# Parse metadata from header
n = None # Number of nodes (including depot)
capacity = None
coordinates = []
demands = []
depot_idx = 0 # Default depot index - node 1 (0-indexed) is standard default
depot_found = False
# Read through file sections
section = None
for line in lines:
line = line.strip()
# Skip empty lines
if not line:
continue
# Check for section headers
if line.startswith("DIMENSION"):
n = int(line.split(":")[1].strip())
elif line.startswith("CAPACITY"):
capacity = int(line.split(":")[1].strip())
elif line == "NODE_COORD_SECTION":
section = "coords"
continue
elif line == "DEMAND_SECTION":
section = "demand"
continue
elif line == "DEPOT_SECTION":
section = "depot"
depot_found = True
continue
elif line == "EOF":
break
# Parse data based on current section
if section == "coords":
parts = line.split()
if len(parts) >= 3:
node_id = int(parts[0]) - 1 # Convert to 0-indexed
x = float(parts[1])
y = float(parts[2])
# Ensure we have a spot in the array for this node
while len(coordinates) <= node_id:
coordinates.append(None)
coordinates[node_id] = (x, y)
elif section == "demand":
parts = line.split()
if len(parts) >= 2:
node_id = int(parts[0]) - 1 # Convert to 0-indexed
demand = int(parts[1])
# Ensure we have a spot in the array for this demand
while len(demands) <= node_id:
demands.append(None)
demands[node_id] = demand
elif section == "depot":
try:
depot = int(line)
if depot > 0: # Valid depot ID
depot_idx = depot - 1 # Convert to 0-indexed
except ValueError:
pass # Skip if not a valid depot ID
# Ensure all node coordinates and demands are loaded
if n is None:
n = len(coordinates)
# Handle special cases for depot identification
if not depot_found:
# Check if there's a node with zero demand - that's often the depot
for i, demand in enumerate(demands):
if demand == 0:
depot_idx = i
break
# Otherwise, use node 0 as default depot (common convention)
# Calculate distance matrix
coords = np.array(coordinates)
# Calculate pairwise squared differences
x_diff = coords[:, 0, np.newaxis] - coords[:, 0]
y_diff = coords[:, 1, np.newaxis] - coords[:, 1]
# Calculate Euclidean distances
dist_matrix = np.sqrt(x_diff ** 2 + y_diff ** 2)
# Set diagonal to zero (optional, as it should already be zero)
np.fill_diagonal(dist_matrix, 0)
return n, capacity, coordinates, demands, dist_matrix, depot_idx
def eval_func(nodes, demands, capacity, depot_idx, optimal_routes, routes, **kwargs):
"""
Evaluate a predicted CVRP solution against optimal routes or calculate total distance.
Args:
nodes (list): List of (x, y) coordinates representing locations
Format: [(x1, y1), (x2, y2), ..., (xn, yn)]
demands (list): List of customer demands
Format: [d0, d1, d2, ..., dn]
capacity (int): Vehicle capacity
depot_idx (int): Index of the depot (typically 0)
optimal_routes (list): Reference optimal routes (may be None if not available)
Format: [[0, 3, 1, 0], [0, 2, 5, 0], ...]
predicted_routes (list): Predicted routes from the solver
Format: [[0, 3, 1, 0], [0, 2, 5, 0], ...]
Returns:
float: Optimality gap percentage if optimal_routes is provided,
or just the predicted solution's total distance
"""
# Validate solution
validate_cvrp_solution(nodes, demands, capacity, depot_idx, routes)
# Calculate the predicted solution cost (total distance)
pred_cost = calculate_total_distance(nodes, routes)
# If optimal routes are provided, calculate optimality gap
if optimal_routes:
opt_cost = calculate_total_distance(nodes, optimal_routes)
opt_gap = ((pred_cost / opt_cost) - 1) * 100
return opt_gap
# Otherwise, just return the predicted cost
return pred_cost
def validate_cvrp_solution(nodes, demands, capacity, depot_idx, routes):
"""
Validate that a CVRP solution meets all constraints.
Args:
nodes (list): List of (x, y) coordinates
demands (list): List of customer demands
capacity (int): Vehicle capacity
depot_idx (int): Index of the depot
routes (list): List of routes to validate
Raises:
Exception: If the solution is invalid
"""
num_nodes = len(nodes)
all_visited = set()
for route_idx, route in enumerate(routes):
# Check that route starts and ends at depot
if route[0] != depot_idx or route[-1] != depot_idx:
raise Exception(f"Route {route_idx} does not start and end at the depot")
# Check capacity constraint
route_demand = sum(demands[i] for i in route[1:-1]) # Exclude depot
if route_demand > capacity:
raise Exception(f"Route {route_idx} exceeds capacity: {route_demand} > {capacity}")
# Check that nodes are valid indices
for node in route:
if node < 0 or node >= num_nodes:
raise Exception(f"Invalid node index {node} in route {route_idx}")
# Add to visited set (excluding depot)
if node != depot_idx:
all_visited.add(node)
# Check that all customers are visited exactly once
expected_visited = set(range(num_nodes))
expected_visited.remove(depot_idx) # Exclude depot
if all_visited != expected_visited:
missing = expected_visited - all_visited
duplicate = all_visited - expected_visited
if missing:
raise Exception(f"Nodes not visited: {missing}")
if duplicate:
raise Exception(f"Nodes visited more than once: {duplicate}")
def calculate_total_distance(nodes, routes):
"""
Calculate the total distance of a CVRP solution.
Args:
nodes (list): List of (x, y) coordinates
routes (list): List of routes
Returns:
float: Total distance of all routes
"""
total_distance = 0
for route in routes:
route_distance = 0
for i in range(len(route) - 1):
from_node = route[i]
to_node = route[i + 1]
# Calculate Euclidean distance
from_x, from_y = nodes[from_node]
to_x, to_y = nodes[to_node]
segment_distance = math.sqrt((to_x - from_x) ** 2 + (to_y - from_y) ** 2)
route_distance += segment_distance
total_distance += route_distance
return total_distance
def norm_score(results):
optimal_scores = {'easy_test_instances/Golden_13.vrp': [857.188745], 'easy_test_instances/Golden_17.vrp': [707.755935], 'easy_test_instances/Golden_10.vrp': [735.43], 'easy_test_instances/Golden_19.vrp': [1365.6], 'easy_test_instances/Golden_7.vrp': [10023.844627], 'easy_test_instances/Golden_3.vrp': [10785.779388], 'easy_test_instances/Golden_1.vrp': [5370.545835], 'easy_test_instances/Golden_8.vrp': [11486.585777], 'easy_test_instances/Golden_12.vrp': [1100.67], 'easy_test_instances/Golden_5.vrp': [6460.979519], 'easy_test_instances/Golden_18.vrp': [995.13], 'easy_test_instances/Golden_9.vrp': [579.7], 'easy_test_instances/Golden_11.vrp': [911.98], 'easy_test_instances/Golden_4.vrp': [13541.657456], 'easy_test_instances/Golden_16.vrp': [1611.28], 'easy_test_instances/Golden_6.vrp': [8348.949187], 'easy_test_instances/Golden_15.vrp': [1337.27], 'easy_test_instances/Golden_2.vrp': [8205.866802], 'easy_test_instances/Golden_20.vrp': [1817.59], 'easy_test_instances/Golden_14.vrp': [1080.55], 'hard_test_instances/Leuven1.vrp': [192848.0], 'hard_test_instances/Leuven2.vrp': [111391.0], 'hard_test_instances/Antwerp1.vrp': [477277.0], 'hard_test_instances/Antwerp2.vrp': [291350.0], 'hard_test_instances/Ghent1.vrp': [469531.0], 'hard_test_instances/Ghent2.vrp': [257748.0], 'hard_test_instances/Brussels1.vrp': [501719.0], 'hard_test_instances/Brussels2.vrp': [345468.0], 'hard_test_instances/Flanders1.vrp': [7240118.0], 'hard_test_instances/Flanders2.vrp': [4373244.0], 'valid_instances/instance_100_5.vrp': [1269.090891], 'valid_instances/instance_100_1.vrp': [1322.220351], 'valid_instances/instance_100_2.vrp': [1273.835052], 'valid_instances/instance_100_3.vrp': [1239.690157], 'valid_instances/instance_100_4.vrp': [1289.129098], 'valid_instances/instance_50_1.vrp': [753.946825], 'valid_instances/instance_50_3.vrp': [826.843367], 'valid_instances/instance_50_5.vrp': [818.44171], 'valid_instances/instance_50_4.vrp': [776.674785], 'valid_instances/instance_20_1.vrp': [453.252768], 'valid_instances/instance_50_2.vrp': [743.427813], 'valid_instances/instance_20_3.vrp': [487.1528], 'valid_instances/instance_20_4.vrp': [463.300497], 'valid_instances/instance_20_5.vrp': [388.950037], 'valid_instances/instance_20_2.vrp': [455.784836]}
normed = {}
for case, (scores, error_message) in results.items():
if case not in optimal_scores:
continue # Skip if there's no optimal score defined.
optimal_list = optimal_scores[case]
normed_scores = []
# Compute normalized score for each index.
for idx, score in enumerate(scores):
if isinstance(score, (int, float)):
normed_scores.append(1 - abs(score - optimal_list[idx]) / max(score, optimal_list[idx]))
else:
normed_scores.append(score)
normed[case] = (normed_scores, error_message)
return normed
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