写出车辆调度问题的代码
时间: 2023-09-30 15:04:03 浏览: 47
车辆调度问题是一种经典的组合优化问题,它的目标是在给定的时间和空间限制下,安排车辆的行驶路线,使得所有订单都能够得到满足,并且最小化总的行驶距离或总的等待时间。
以下是一个简单的车辆调度问题的代码实现,使用了遗传算法作为求解方法:
```python
import random
# 车辆调度问题的优化目标函数
def fitness(route, distances, demands, capacity):
fitness_score = 0
load = 0
for i in range(len(route)):
if i == 0:
fitness_score += distances[0][route[i]]
else:
fitness_score += distances[route[i-1]][route[i]]
load += demands[route[i]]
if load > capacity:
fitness_score += 1000000 # 惩罚项,表示超载的惩罚
break
fitness_score += distances[route[-1]][0]
return fitness_score
# 生成一个随机的个体
def generate_individual(num_customers):
individual = list(range(1, num_customers+1))
random.shuffle(individual)
return individual
# 生成一个随机的种群
def generate_population(population_size, num_customers):
population = []
for i in range(population_size):
population.append(generate_individual(num_customers))
return population
# 选择操作,使用轮盘赌选择算法
def selection(population, distances, demands, capacity):
population_fitness = []
for individual in population:
fitness_score = fitness(individual, distances, demands, capacity)
population_fitness.append((individual, fitness_score))
total_fitness = sum([f for i, f in population_fitness])
cum_fitness = 0
for i in range(len(population_fitness)):
cum_fitness += population_fitness[i][1] / total_fitness
if random.random() < cum_fitness:
return population_fitness[i][0]
# 交叉操作,使用部分映射交叉算法
def crossover(parent1, parent2):
child1 = parent1[:]
child2 = parent2[:]
index1 = random.randint(0, len(parent1)-1)
index2 = random.randint(index1, len(parent1)-1)
for i in range(index1, index2+1):
temp = child1[i]
child1[i] = child2[i]
child2[i] = temp
return child1, child2
# 变异操作,使用交换变异算法
def mutation(individual):
index1 = random.randint(0, len(individual)-1)
index2 = random.randint(0, len(individual)-1)
individual[index1], individual[index2] = individual[index2], individual[index1]
return individual
# 遗传算法求解车辆调度问题
def solve_vrp(num_customers, distances, demands, capacity, population_size, num_generations):
population = generate_population(population_size, num_customers)
for i in range(num_generations):
new_population = []
for j in range(population_size//2):
parent1 = selection(population, distances, demands, capacity)
parent2 = selection(population, distances, demands, capacity)
child1, child2 = crossover(parent1, parent2)
child1 = mutation(child1)
child2 = mutation(child2)
new_population.append(child1)
new_population.append(child2)
population = new_population
best_individual = min(population, key=lambda x: fitness(x, distances, demands, capacity))
best_fitness = fitness(best_individual, distances, demands, capacity)
return best_individual, best_fitness
```
在上述代码中,我们使用了遗传算法作为求解方法。遗传算法是一种启发式优化算法,它通过模拟自然界中的遗传和进化过程来搜索最优解。具体而言,遗传算法将问题的解表示为一个个体,每个个体都有一个适应度评价值,代表该解的优劣程度。遗传算法通过选择、交叉和变异等操作来生成新的个体,并逐代迭代,直到找到最优解为止。
在车辆调度问题中,我们将每个个体表示为一个订单的排列,即一个车辆的行驶路线。适应度评价值为该车辆行驶路线的总距离,加上超载的惩罚项。我们使用轮盘赌选择算法、部分映射交叉算法和交换变异算法来对个体进行选择、交叉和变异操作。最终,我们得到了车辆调度问题的一个近似最优解。
需要注意的是,这只是一个简单的实现,实际上车辆调度问题还有很多细节需要处理,比如车辆的容量限制、订单的时间窗口等。这里只是为了演示遗传算法求解车辆调度问题的基本思路和方法。
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