vrp代码
时间: 2023-07-02 17:07:48 浏览: 34
VRP是一种经典的组合优化问题,有许多不同的算法可以解决。在这里,我将提供一个基于遗传算法的VRP求解程序的示例代码。这个程序使用Python语言编写,需要使用numpy和pandas库。
```python
import numpy as np
import pandas as pd
# 读取数据
data = pd.read_csv('data.csv', header=None)
n_customers = data.shape[0] - 1
capacity = data.iloc[0, 0]
locations = data.iloc[1:, 1:].values
# 基因编码
def encode(chromosome):
"""
编码染色体
"""
# 将染色体分成多个子序列
sub_sequences = []
sub_sequence = [0]
for gene in chromosome:
if gene == 0:
sub_sequence.append(0)
sub_sequences.append(sub_sequence)
sub_sequence = [0]
else:
sub_sequence.append(gene)
sub_sequence.append(0)
sub_sequences.append(sub_sequence)
# 将子序列转换为路径
paths = []
for sub_sequence in sub_sequences:
path = []
for gene in sub_sequence:
if gene != 0 and gene not in path:
path.append(gene)
paths.append(path)
return paths
def decode(paths):
"""
解码染色体
"""
# 将路径转换为子序列
sub_sequences = []
for path in paths:
sub_sequence = [0] + path + [0]
sub_sequences.append(sub_sequence)
# 将子序列转换为染色体
chromosome = []
for sub_sequence in sub_sequences:
chromosome += sub_sequence[:-1]
chromosome.append(0)
return chromosome
# 适应度函数
def fitness(chromosome):
paths = encode(chromosome)
total_distance = 0
total_demand = 0
for path in paths:
distance = 0
demand = 0
for i in range(len(path) - 1):
j, k = path[i], path[i + 1]
distance += np.linalg.norm(locations[j] - locations[k])
demand += data.iloc[k, 0]
if demand > capacity:
return 0
total_distance += distance
total_demand += demand
return total_distance
# 选择函数
def selection(population, fitness):
fitness = np.array(fitness)
idx = np.random.choice(len(population), size=len(population), replace=True, p=fitness/fitness.sum())
return population[idx]
# 交叉函数
def crossover(parent1, parent2):
idx1 = np.random.randint(len(parent1))
idx2 = np.random.randint(len(parent2))
if idx1 > idx2:
idx1, idx2 = idx2, idx1
child = [-1] * len(parent1)
for i in range(idx1, idx2 + 1):
child[i] = parent1[i]
idx = 0
for i in range(len(parent2)):
if parent2[i] not in child:
while child[idx] != -1:
idx += 1
child[idx] = parent2[i]
return child
# 变异函数
def mutation(chromosome):
idx1 = np.random.randint(len(chromosome))
idx2 = np.random.randint(len(chromosome))
chromosome[idx1], chromosome[idx2] = chromosome[idx2], chromosome[idx1]
return chromosome
# 遗传算法求解VRP
population_size = 100
num_generations = 1000
mutation_rate = 0.1
population = [list(range(1, n_customers + 1)) + [0] for _ in range(population_size)]
for generation in range(num_generations):
fitness_values = [fitness(chromosome) for chromosome in population]
best_chromosome = population[np.argmin(fitness_values)]
print(f'Generation {generation + 1}, Minimum Fitness: {min(fitness_values)}')
new_population = [best_chromosome]
while len(new_population) < population_size:
parent1, parent2 = selection(population, fitness_values), selection(population, fitness_values)
child = crossover(parent1, parent2)
if np.random.rand() < mutation_rate:
child = mutation(child)
new_population.append(child)
population = new_population
best_paths = encode(best_chromosome)
for i, path in enumerate(best_paths):
print(f'Vehicle {i + 1}: {path}, Total Distance: {fitness(best_chromosome)}')
```
这个程序包含了基因编码、适应度函数、选择函数、交叉函数和变异函数等基本部分,通过遗传算法对VRP进行求解。你需要准备一个数据文件,其中包含了客户的位置和需求量信息,程序会自动读取这个文件并进行求解。注意,这个程序只是一个示例,你需要根据实际情况对其进行修改和优化。