我需要快速地将节约启发式和最近邻法应用到一组VRP (车辆路径问题)中

import numpy as np

# 距离矩阵
distance_matrix = np.array([
    [0, 10, 15, 20],
    [10, 0, 35, 25],
    [15, 35, 0, 30],
    [20, 25, 30, 0]
])

def savings_heuristic(distance_matrix):
    n = len(distance_matrix)
    savings = []
    for i in range(1, n):
        for j in range(i + 1, n):
            s_ij = distance_matrix[0, i] + distance_matrix[0, j] - distance_matrix[i, j]
            savings.append((s_ij, i, j))
    savings.sort(reverse=True)
    return savings

def nearest_neighbor(distance_matrix, start=0):
    n = len(distance_matrix)
    visited = [False] * n
    visited[start] = True
    route = [start]
    current = start
    while len(route) < n:
        next_node = -1
        min_dist = float('inf')
        for j in range(n):
            if not visited[j] and distance_matrix[current, j] < min_dist:
                next_node = j
                min_dist = distance_matrix[current, j]
        route.append(next_node)
        visited[next_node] = True
        current = next_node
    return route

# 使用节约启发式生成初始解
savings = savings_heuristic(distance_matrix)
print("Savings Heuristic:", savings)

# 使用最近邻法生成初始解
route_nn = nearest_neighbor(distance_matrix)
print("Nearest Neighbor Route:", route_nn)