Teamwork[HDU4494]
Teamwork
Time Limit: 2000/1000 MS (Java/Others) Memory Limit: 65535/65535 K (Java/Others)
Total Submission(s): 497 Accepted Submission(s): 258
Problem Description
Some locations in city A has been destroyed in the fierce battle. So the government decides to send some workers to repair these locations. There are m kinds of workers that were trained for different skills. Each location need some number of some kinds of workers and has a schedule that at what time can the repair begins, and the time cost of repair. Any job cannot begin until all the workers required arrived.
For example, location 1 needs 2 workers of type 1 and 3 workers of type 2, and the beginning time and time cost is 100 minute and 90 minute correspondingly, then 5 workers that satisfy the requirement should arrive before 100 minute, start working at 100 minute and get the job done at 190 minute. Notice that two different types of workers cannot replace each other, so with 3 workers of type 1 and only 2 workers of type 2, this job cannot be done.
Workers can go from one location to another after their jobs are done. You can take the Euclidean distance between locations as the time workers need to travel between them. Each worker should be sent from a depot initially at 0 minute. Now your task is to determine the minimum number of workers needed to be sent from depot so that all the jobs can be done.
Input
There are multiple test cases, the integer on the first line T (T<25) indicates the number of test cases.
Each test case begins with two integers n (<=150), the number of location(including the depot) and m(<=5), the number of different skills.
The next line gives two integers x0, y0 indicates the coordinate of depot.
Then follows n - 1 lines begins with 4 integer numbers: xi, yi, bi(bi>0), pi(pi>0), (xi, yi) gives the coordinate of the i-th location, bi gives the beginning time and pi gives the time cost. The rest of the line gives m non-negative integers v1, v2, ..., vm, of which the i-th number indicates the the number of workers of type i needed (for all vi, 0<=vi<10, each location at least requires one worker).
All integers are less than 1000000 (106).
Output
For each test cases output one line, the minimum workers to be sent. It is guaranteed that there's always a feasible solution that all the jobs can be done.
Sample Input
2
4 1
0 0
0 1 1 1 3
1 1 3 3 4
1 0 10 1 5
4 1
0 0
0 1 1 1 3
1 1 3 3 4
1 0 3 1 5
Sample Output
5
9
一开始不会,在网上找了一下,看到有一个博客里是这么说的:

没看明白他是什么意思,为什么要把一个点分成3个点。我拿第一组示例输入画了一下图:

1是源点,3n+1是汇点,中间的3列是拆分之后的3组点。关键看第三列的点,它的流量有两个来源,一是从源点派出,费用是1,代表这部分工人是另行派出的;二是从其他节点派出,费用是0,代表这部分工人是完成其他任务后转来的。
顺便,从图里可以看出,确实不用分成3个点,第一列完全没必要。这道题整理了一个最小费用流的模板--->
#include<iostream>
#include<cstring>
#include<cstring>
#include<cmath>
#include<cstdio>
using namespace std;
const int Maxn = ;
int n, m, k;
int sta[Maxn], en[Maxn], ty[Maxn][];
double d[Maxn][Maxn];
struct Point {
double x, y;
} p[Maxn];
double DIS(Point a, Point b) {
return sqrt((a.x - b.x) * (a.x - b.x) + (a.y - b.y) * (a.y - b.y));
}
//ALGORITHM MINCOSTFLOW ->
#define ALGORITHM_MINCOSTFLOW_MAXN 600
#define ALGORITHM_MINCOSTFLOW_MAXM 360000
#define ALGORITHM_MINCOSTFLOW_INF 0X7FFFFFFF
struct ALGORITHM_MINCOSTFLOW_Edge {
int v;
int val;
int cost;
int next;
} ALGORITHM_MINCOSTFLOW_edge[ALGORITHM_MINCOSTFLOW_MAXM];
int ALGORITHM_MINCOSTFLOW_head[ALGORITHM_MINCOSTFLOW_MAXN];
int ALGORITHM_MINCOSTFLOW_countedge;
int ALGORITHM_MINCOSTFLOW_pre[ALGORITHM_MINCOSTFLOW_MAXN];
int ALGORITHM_MINCOSTFLOW_pos[ALGORITHM_MINCOSTFLOW_MAXN];
int ALGORITHM_MINCOSTFLOW_dis[ALGORITHM_MINCOSTFLOW_MAXN];
int ALGORITHM_MINCOSTFLOW_que[ALGORITHM_MINCOSTFLOW_MAXM];
bool ALGORITHM_MINCOSTFLOW_vis[ALGORITHM_MINCOSTFLOW_MAXN];
void ALGORITHM_MINCOSTFLOW_addedge(int u, int v, int val, int cost) {
ALGORITHM_MINCOSTFLOW_edge[ALGORITHM_MINCOSTFLOW_countedge].v = v;
ALGORITHM_MINCOSTFLOW_edge[ALGORITHM_MINCOSTFLOW_countedge].val = val;
ALGORITHM_MINCOSTFLOW_edge[ALGORITHM_MINCOSTFLOW_countedge].cost = cost;
ALGORITHM_MINCOSTFLOW_edge[ALGORITHM_MINCOSTFLOW_countedge].next = ALGORITHM_MINCOSTFLOW_head[u];
ALGORITHM_MINCOSTFLOW_head[u] = ALGORITHM_MINCOSTFLOW_countedge++;
ALGORITHM_MINCOSTFLOW_edge[ALGORITHM_MINCOSTFLOW_countedge].v = u;
ALGORITHM_MINCOSTFLOW_edge[ALGORITHM_MINCOSTFLOW_countedge].val = ;
ALGORITHM_MINCOSTFLOW_edge[ALGORITHM_MINCOSTFLOW_countedge].cost = -cost;
ALGORITHM_MINCOSTFLOW_edge[ALGORITHM_MINCOSTFLOW_countedge].next = ALGORITHM_MINCOSTFLOW_head[v];
ALGORITHM_MINCOSTFLOW_head[v] = ALGORITHM_MINCOSTFLOW_countedge++;
}
void ALGORITHM_MINCOSTFLOW_clear() {
memset(ALGORITHM_MINCOSTFLOW_head, -, sizeof(ALGORITHM_MINCOSTFLOW_head));
ALGORITHM_MINCOSTFLOW_countedge = ;
}
bool ALGORITHM_MINCOSTFLOW_spfa(int s, int t) {
memset(ALGORITHM_MINCOSTFLOW_pre, -, sizeof(ALGORITHM_MINCOSTFLOW_pre));
memset(ALGORITHM_MINCOSTFLOW_vis, , sizeof(ALGORITHM_MINCOSTFLOW_vis));
int Head, tail;
Head = tail = ;
for (int i = ; i < ALGORITHM_MINCOSTFLOW_MAXN; i++) {
ALGORITHM_MINCOSTFLOW_dis[i] = ALGORITHM_MINCOSTFLOW_INF;
}
ALGORITHM_MINCOSTFLOW_que[tail++] = s;
ALGORITHM_MINCOSTFLOW_pre[s] = s;
ALGORITHM_MINCOSTFLOW_dis[s] = ;
ALGORITHM_MINCOSTFLOW_vis[s] = ;
while (Head != tail) {
int now = ALGORITHM_MINCOSTFLOW_que[Head++];
ALGORITHM_MINCOSTFLOW_vis[now] = ;
for (int i = ALGORITHM_MINCOSTFLOW_head[now]; i != -; i = ALGORITHM_MINCOSTFLOW_edge[i].next) {
int adj = ALGORITHM_MINCOSTFLOW_edge[i].v;
if (ALGORITHM_MINCOSTFLOW_edge[i].val > && ALGORITHM_MINCOSTFLOW_dis[now] + ALGORITHM_MINCOSTFLOW_edge[i].cost < ALGORITHM_MINCOSTFLOW_dis[adj]) {
ALGORITHM_MINCOSTFLOW_dis[adj] = ALGORITHM_MINCOSTFLOW_dis[now] + ALGORITHM_MINCOSTFLOW_edge[i].cost;
ALGORITHM_MINCOSTFLOW_pre[adj] = now;
ALGORITHM_MINCOSTFLOW_pos[adj] = i;
if (!ALGORITHM_MINCOSTFLOW_vis[adj]) {
ALGORITHM_MINCOSTFLOW_vis[adj] = ;
ALGORITHM_MINCOSTFLOW_que[tail++] = adj;
}
}
}
}
return ALGORITHM_MINCOSTFLOW_pre[t] != -;
}
int ALGORITHM_MINCOSTFLOW_MinCostFlow(int s, int t) {
int cost = , flow = ;
while (ALGORITHM_MINCOSTFLOW_spfa(s, t)) {
int f = ALGORITHM_MINCOSTFLOW_INF;
for (int i = t; i != s; i = ALGORITHM_MINCOSTFLOW_pre[i])
if (ALGORITHM_MINCOSTFLOW_edge[ALGORITHM_MINCOSTFLOW_pos[i]].val < f) {
f = ALGORITHM_MINCOSTFLOW_edge[ALGORITHM_MINCOSTFLOW_pos[i]].val;
}
flow += f;
cost += ALGORITHM_MINCOSTFLOW_dis[t] * f;
for (int i = t; i != s; i = ALGORITHM_MINCOSTFLOW_pre[i]) {
ALGORITHM_MINCOSTFLOW_edge[ALGORITHM_MINCOSTFLOW_pos[i]].val -= f;
ALGORITHM_MINCOSTFLOW_edge[ALGORITHM_MINCOSTFLOW_pos[i] ^ ].val += f;
}
}
return cost;
}
// <- ALGORITHM MINCOSTFLOW
void build(int type) {
int i, j;
ALGORITHM_MINCOSTFLOW_clear();
for (i = ; i <= n; i++) {
ALGORITHM_MINCOSTFLOW_addedge(, i + * n, ty[i][type], );
ALGORITHM_MINCOSTFLOW_addedge(, i + n, ty[i][type], );
ALGORITHM_MINCOSTFLOW_addedge(i + * n, * n + , ty[i][type], );
for (j = ; j <= n; j++) {
if (sta[i] + en[i] + d[i][j] <= sta[j]) {
ALGORITHM_MINCOSTFLOW_addedge(i + n, j + * n, ty[i][type], );
}
}
}
}
int solve() {
int i, j, u, v;
int ans = ;
for (i = ; i <= m; i++) {
build(i);
ans += ALGORITHM_MINCOSTFLOW_MinCostFlow(, * n + );
}
return ans;
}
int main() {
int i, j, u, v, c, t;
scanf("%d", &t);
while (t--) {
ALGORITHM_MINCOSTFLOW_clear();
scanf("%d%d", &n, &m);
scanf("%lf%lf", &p[].x, &p[].y);
for (i = ; i <= n; i++) {
scanf("%lf%lf%d%d", &p[i].x, &p[i].y, &sta[i], &en[i]);
for (j = ; j <= m; j++) {
scanf("%d", &ty[i][j]);
}
}
for (i = ; i <= n; i++) {
for (j = i + ; j <= n; j++) {
d[i][j] = d[j][i] = DIS(p[i], p[j]);
}
}
printf("%d\n", solve());
}
return ;
}
Teamwork[HDU4494]的更多相关文章
- hdu 4494 Teamwork 最小费用最大流
Teamwork Time Limit: 20 Sec Memory Limit: 256 MB 题目连接 http://acm.hdu.edu.cn/showproblem.php?pid=4494 ...
- Scrum And Teamwork
Scrum Learning 概念 Scrum是迭代式增量软件开发过程,通常用于敏捷软件开发.Scrum包括了一系列实践和预定义角色的过程骨架.Scrum中的主要角色包括同项目经理类似的Scrum主管 ...
- GIT TEAMWORK
Learn GIT TEAMWORK generalizations Congratulations, you now know enough to start collaborating on Gi ...
- CSUOJ 1525 Algebraic Teamwork
Problem A Algebraic Teamwork The great pioneers of group theory and linear algebra want to cooperate ...
- P5124 Teamwork(DP)
题目: P5124 [USACO18DEC]Teamwork 解析: 动态规划,设\(f[i]\)表示到第\(i\)位的最大值,我们枚举i之前的j个位置\((j<k)\),记录一下这\(j+1\ ...
- 2019 GDUT Rating Contest I : Problem B. Teamwork
题面: 传送门 B. Teamwork Input file: standard input Output file: standard output Time limit: 1 second Memor ...
- Teamwork——Week4 团队分工和预估项目时间
由于我们给每个组员预估的每天用在该团队项目的时间为2h左右,因此我们的时间计算也已2h为基数.下面就是我们的团队分工和预估项目时间. 任务编号 实现人员 任务详细描述 预估时间 任务0 全体组员 看学 ...
- Teamwork——Week 4 Daily Scrum Meeting#1 2013.10.23
一.会议议题 1)根据确立的项目题目,进一步明确PM,DEV,TEST的工作. 2)确定团队分工和预估项目时间. 3)完成项目架构NABC模型. 4)确定第一轮开发团队分工 二.会议时间 2013年1 ...
- Teamwork——Week4 团队项目之NABC
项目框架——NABC模型 一.N(Need需求) 我们组主要的用户对象是第三小组——UI小组的同学们,因此我们的用户需求就是他们的数据需求. 1)提供给UI小组整理好的数据库,和前一组讨论好数据结构. ...
随机推荐
- Android Service 与 Thread 的区别
Ref:http://blog.csdn.net/jiangwei0910410003/article/details/17008687 1). Thread:Thread 是程序执行的最小单元,它是 ...
- 做一个App前需要考虑的几件事
本文转载于文章原文链接,版本归原作者所有! 随着工具链的完善,语言的升级以及各种优质教程的涌现,做一个 App 的成本也越来越低了.尽管如此,有些事情最好前期就做起来,避免当 App 有了一定规模后, ...
- n数乘积第m小
这是从Java贴吧看到的一道面试题,看了别人的解题思路实现的.... 如题: n个数,他们的乘积可得到一些其它的数,求第m小的. 输入格式: n m n1 n2 n3 ... 例: 输入: 3 8 2 ...
- 环信SDK集成
利用环信SDK可以实现即时通讯,但在集成的过程中碰到了不少的坑. 注意 选择项目路径,这里以最新版环信demo为例 注意:环信的ChatDemoUI这个demo里边因为研发的同事为了照顾老版本的And ...
- 二、JavaScript语言--JS实践--商城分类导航效果
商城类导航菜单制作(以京东为例--竖向列表横向伸缩) 可以用两种方式来实现:用CSS实现和用JS实现 方法一:用CSS实现(要点:使用hover) <!DOCTYPE html PUBLIC & ...
- Jmeter 函数
一._csvRead 函数 _cvsRead函数是从外部读取参数,csvRead函数可以从一个文件中读取多个参数. 步骤: 1.先新建一个文件,例如c.txt,里面的数据存放为 web@qq.com, ...
- shell test 數值 字符串 文件比較
數值比較 描述 n1 –eq n2 等於 n1 –gt n2 大於 n1 –ge n2 大於等於 n1 –lt n2 小於 n1 –le n2 小於等於 n1 –ne n2 不等於 字符串比較 ...
- android 入门-工程属性介绍
工程属性 (1)drawable-hdpi里面存放高分辨率的图片,如WVGA (480x800),FWVGA (480x854) (2)drawable-mdpi里面存放中等分辨率的图片,如HVGA ...
- hdu 4039 2011成都赛区网络赛I ***
两层搜索,直接for循环就行了,还要注意不能是自己的朋友 #include<cstdio> #include<iostream> #include<algorithm&g ...
- ServerSocket 默认邦定IP
转自:http://cuisuqiang.iteye.com/blog/2037769 开发中需要开启服务端的时候,本地测试都是直接写端口,实际环境也是需要指定要邦定的IP才可以. 因为对于服务器来说 ...