洗车问题 有两台洗车机，一台性能好（3分钟洗一台小汽车，5分钟清洗一台SUV），一台性能较差（6分钟洗一台小汽车，10分钟清洗一台SUV）。车流量为随机，在上午是每小时20辆车，大小随机，比例1:1；下午是每小时30辆车，大小随机，比例为4：6。请问如何分别实现上下午平均等待时间最短。python代码

下面是一个用 Python 实现洗车问题的代码，使用了 numpy 库来生成随机数：

import numpy as np

class Queue:
    def __init__(self):
        self.queue = []
        self.waiting_time = []
    
    def push(self, car):
        self.queue.append(car)
    
    def pop(self):
        return self.queue.pop(0)
    
    def get_size(self):
        return len(self.queue)

class Car:
    def __init__(self, size):
        self.size = size
        self.enter_time = 0
        self.start_time = 0
        self.finish_time = 0
        self.wait_time = 0

class CarWashMachine:
    def __init__(self, wash_time, suv_wash_time):
        self.wash_time = wash_time
        self.suv_wash_time = suv_wash_time
        self.status = 0
    
    def is_available(self):
        return self.status == 0
    
    def wash_car(self, car):
        if car.size == 'SUV':
            wash_time = self.suv_wash_time
        else:
            wash_time = self.wash_time
        car.start_time = max(car.enter_time, self.finish_time)
        car.wait_time = car.start_time - car.enter_time
        car.finish_time = car.start_time + wash_time
        self.status = 1

def simulate(queue, machine1, machine2, num_cars, suv_ratio):
    car_count = 0
    suv_count = 0
    while car_count < num_cars:
        # generate car type
        if np.random.random() < suv_ratio:
            size = 'SUV'
            suv_count += 1
        else:
            size = 'car'
        # generate car object
        car = Car(size)
        car.enter_time = car_count
        queue.push(car)
        car_count += 1
        # check available machine
        if machine1.is_available() and (car.size == 'car' or suv_count % 2 == 0):
            machine1.wash_car(queue.pop())
        elif machine2.is_available():
            machine2.wash_car(queue.pop())
        # update machine status
        if machine1.status == 1 and machine1.finish_time <= car_count:
            machine1.status = 0
        if machine2.status == 1 and machine2.finish_time <= car_count:
            machine2.status = 0
        # update finish time
        finish_time = min(machine1.finish_time, machine2.finish_time)
        if queue.get_size() > 0 and queue.queue[0].enter_time <= finish_time:
            finish_time = queue.queue[0].enter_time
        machine1.finish_time = finish_time
        machine2.finish_time = finish_time

def calculate_avg_wait_time(cars):
    wait_time = [car.wait_time for car in cars]
    return sum(wait_time) / len(wait_time)

# morning
print('Morning:')
queue = Queue()
machine1 = CarWashMachine(3, 5)
machine2 = CarWashMachine(6, 10)
simulate(queue, machine1, machine2, 20, 0.5)
print('Machine 1 total wait time:', sum(machine1.waiting_time))
print('Machine 2 total wait time:', sum(machine2.waiting_time))
if calculate_avg_wait_time(queue.waiting_time) < calculate_avg_wait_time(machine1.waiting_time):
    print('Choose machine 1')
else:
    print('Choose machine 2')
    
# afternoon
print('Afternoon:')
queue = Queue()
machine1 = CarWashMachine(3, 5)
machine2 = CarWashMachine(6, 10)
simulate(queue, machine1, machine2, 30, 0.6)
print('Machine 1 total wait time:', sum(machine1.waiting_time))
print('Machine 2 total wait time:', sum(machine2.waiting_time))
if calculate_avg_wait_time(queue.waiting_time) < calculate_avg_wait_time(machine1.waiting_time):
    print('Choose machine 1')
else:
    print('Choose machine 2')

上面的代码中，我们定义了 Queue 类来模拟车辆排队的过程，Car 类来表示车辆对象，CarWashMachine 类来表示洗车机对象。在 simulate() 函数中，我们模拟了车流量的到达过程，并根据洗车机的性能将车辆分配到不同的洗车机进行洗车。在模拟过程中，我们记录了每辆车的进入队列时间、开始洗车时间、结束洗车时间和等待时间，并计算每小时的平均等待时间。最后，根据计算结果选择平均等待时间最短的洗车机进行洗车。

注意，上午和下午分别进行了一次模拟，因此可以分别得出上午和下午的平均等待时间并进行比较。