1017 Queueing at Bank

本文介绍了一个银行排队系统的模拟案例,通过算法计算在特定营业时间内,多个窗口服务下顾客的平均等待时间。考虑到银行营业时间限制、顾客到达时间及服务时间上限,文章详细解析了算法实现步骤。

1017 Queueing at Bank (25 分)

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 (≤10​4​​) - 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


题型分类:快乐模拟

题目大意:银行的营业时间为8:00 - 17:00,给定K个窗口,顾客依次在线后等待,计算被服务到的(17:00以后的顾客没有被服务到)顾客的平均等待时间。

解题思路:注意几个细节就好:①每个顾客的服务时间不超过1小时。②银行8:00以后才开始营业。③17:00以后的顾客不会被服务到,不计入数据。


#include <cstdio>
#include <algorithm>

using namespace std;

const int maxCus = 10010;
const int maxWin = 110;
const int INF = 0x3f3f3f3f;

typedef struct {
	int arriveTime;
	int processTime;
}customer;

customer cus[maxCus];
int window[maxWin];

int convertToSecond(int hh, int mm, int ss);
bool cmp(customer a, customer b);

int main(int argc, char** argv) {
	int N, K;
	int stTime = convertToSecond(8, 0, 0), edTime = convertToSecond(17, 0, 0);
	scanf("%d %d", &N, &K);
	for(int i = 0; i < N; i++){
		int hh, mm, ss;
		scanf("%d:%d:%d %d", &hh, &mm, &ss, &cus[i].processTime);
		cus[i].processTime = cus[i].processTime > 60 ? 60 * 60 : cus[i].processTime * 60; //一个客户最多服务1H 
		cus[i].arriveTime = convertToSecond(hh, mm, ss);
	}
	sort(cus, cus + N, cmp);
	int totalWait = 0;
	for(int i = 0; i < K; i++){
		window[i] = stTime;
	}
	int cnt = 0;
	for(int i = 0; i < N; i++){
		if(cus[i].arriveTime > edTime) break; 
		int winNum = -1, min = INF;
		for(int j = 0; j < K; j++){
			if(window[j] < min){
				winNum = j;
				min = window[j];
			}
		}
		if(window[winNum] < cus[i].arriveTime){ //此时表示顾客不用等待 
			window[winNum] = cus[i].arriveTime + cus[i].processTime;
		}else{
			totalWait += window[winNum] - cus[i].arriveTime;
			window[winNum] = window[winNum] + cus[i].processTime;
		}
		cnt++;
	}	
	printf("%.1f", (double)totalWait / 60 / cnt);
	
	return 0;
}

int convertToSecond(int hh, int mm, int ss){
	return hh * 3600 + mm * 60 + ss; 
}

bool cmp(customer a, customer b){
	return a.arriveTime < b.arriveTime;
}

 

### 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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