1017. Queueing at Bank (25)——PAT (Advanced Level) Practise

本文详细介绍了如何通过编程解决银行排队问题,包括将时间转化为秒进行处理,剔除非有效时间段的客户,以及按秒模拟整个过程来计算所有客户的平均等待时间。通过实际案例演示了解题思路并提供了相应的AC代码。

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题目信息

1017.Queueing at Bank (25)

时间限制 400 ms
内存限制 65536 kB
代码长度限制 16000 B

Suppose a bank has K windows open for service. There is a yellow line in front of the windows which devides the waiting area into two parts. All the customers have to wait in line behind the yellow line, until it is his/her turn to be served and there is a window available. It is assumed that no window can be occupied by a single customer for more than 1 hour.

Now given the arriving time T and the processing time P of each customer, you are supposed to tell the average waiting time of all the customers.

Input Specification:

Each input file contains one test case. For each case, the first line contains 2 numbers: N (<=10000) - the total number of customers, and K (<=100) - the number of windows. Then N lines follow, each contains 2 times: HH:MM:SS - the arriving time, and P - the processing time in minutes of a customer. Here HH is in the range [00, 23], MM and SS are both in [00, 59]. It is assumed that no two customers arrives at the same time.

Notice that the bank opens from 08:00 to 17:00. Anyone arrives early will have to wait in line till 08:00, and anyone comes too late (at or after 17:00:01) will not be served nor counted into the average.

Output Specification:

For each test case, print in one line the average waiting time of all the customers, in minutes and accurate up to 1 decimal place.

Sample Input:
7 3
07:55:00 16
17:00:01 2
07:59:59 15
08:01:00 60
08:00:00 30
08:00:02 2
08:03:00 10
Sample Output:
8.2

解题思路

把时间转化成秒,超出17:00的时间剔除,然后按秒模拟

AC代码

#include <cstdio>
#include <algorithm>
using namespace std;

struct node{
    long long a, w;
    bool operator<(const node& b)const{
        return a < b.a;
    }
}s[10005];
long long k[105];
long long n, m, a, b, c, t, id, wait, waitp;
int main()
{
    scanf("%lld%lld", &n, &m);
    for (int i = 0; i < n; ++i){
        scanf("%lld:%lld:%lld %lld", &a, &b, &c, &s[id].w);
        s[id].w *= 60;
        s[id].a = a*60*60+b*60+c;
        if (s[id].a >= 61200){
            --id;
        }
        ++id;
    }
    sort(s, s + id);
    c = 28800; 
    while (t < id){
        bool flag = true;
        for (int i = 0; i < m; ++i){
            if (k[i])  --k[i];
        }
        while (flag && t < id && s[t].a <= c){
            flag = false;
            for (int i = 0; i < m; ++i){
                if (!k[i]) {
                    flag = true;
                    a = i;
                }
            }
            if (flag){
                k[a] = s[t].w;
                wait += c - s[t].a;
                ++t;
            }
        }
        ++c;
    }
    printf("%.1f\n", 1.0*wait/60/id);
    return 0;
}
### 银行排队问题的Python实现 银行排队问题是典型的并行处理和任务分配场景,可以通过ZeroMQ框架中的Ventilator-Worker-Sink模型来解决[^1]。以下是基于该模型的一个简单Python实现: #### ZeroMQ Ventilator 实现 Ventilator负责生成任务并将它们发送给Workers。 ```python import zmq import time context = zmq.Context() # Socket to send tasks to workers sender = context.socket(zmq.PUSH) sender.bind("tcp://*:5557") print("Press Enter when the workers are ready...") _ = input() print("Sending tasks to workers...") # Send out tasks total_msec = 0 for task_nbr in range(100): workload = int((task_nbr * task_nbr) % 100 + 1) # Some random work load total_msec += workload sender.send_string(str(workload)) print(f"Total expected cost: {total_msec} msec") time.sleep(1) # Give 0MQ time to deliver ``` #### ZeroMQ Worker 实现 Worker接收来自Ventilator的任务并执行计算后将结果返回给Sink。 ```python import zmq import sys import time context = zmq.Context() # Socket to receive messages on receiver = context.socket(zmq.PULL) receiver.connect("tcp://localhost:5557") # Socket to send messages to sender = context.socket(zmq.PUSH) sender.connect("tcp://localhost:5558") while True: s = receiver.recv_string() print(f"Received request: {s}") # Do some 'work' time.sleep(int(s) / 10) # Send results to sink sender.send(b'') ``` #### ZeroMQ Sink 实现 Sink收集所有Worker的结果,并统计完成时间。 ```python import zmq import time context = zmq.Context() # Socket to collect worker responses receiver = context.socket(zmq.PULL) receiver.bind("tcp://*:5558") # Wait for start of batch s = receiver.recv() # Start our clock now tstart = time.time() # Process 100 confirmations for task_nbr in range(100): s = receiver.recv() if task_nbr % 10 == 0: sys.stdout.write(':') else: sys.stdout.write('.') sys.stdout.flush() # Calculate and report duration of batch tend = time.time() print(f"\nTotal elapsed time: {(tend-tstart)*1000} msec") ``` 上述代码展示了如何通过ZeroMQ构建一个简单的分布式任务管理系统[^2]。对于银行排队问题,可以将其视为多个客户作为任务被分配到不同的柜员(即Worker),而最终的结果由Sink汇总。 此外,在高并发环境下,还需要注意内存管理和I/O性能优化[^3]。建议使用缓存机制减少磁盘操作频率,并利用消息队列实现各模块间的异步通信。
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