HDU 1690 Bus System(Floyd)

本文介绍了一种针对公交系统中特殊计费规则的最短路径算法实现,该算法使用Floyd算法解决两个站点间最小费用的问题,并考虑了不同距离段的票价差异。

Bus System

Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others)
Total Submission(s): 8999    Accepted Submission(s): 2444


Problem Description
Because of the huge population of China, public transportation is very important. Bus is an important transportation method in traditional public transportation system. And it’s still playing an important role even now.
The bus system of City X is quite strange. Unlike other city’s system, the cost of ticket is calculated based on the distance between the two stations. Here is a list which describes the relationship between the distance and the cost.



Your neighbor is a person who is a really miser. He asked you to help him to calculate the minimum cost between the two stations he listed. Can you solve this problem for him?
To simplify this problem, you can assume that all the stations are located on a straight line. We use x-coordinates to describe the stations’ positions.
 

Input
The input consists of several test cases. There is a single number above all, the number of cases. There are no more than 20 cases.
Each case contains eight integers on the first line, which are L1, L2, L3, L4, C1, C2, C3, C4, each number is non-negative and not larger than 1,000,000,000. You can also assume that L1<=L2<=L3<=L4.
Two integers, n and m, are given next, representing the number of the stations and questions. Each of the next n lines contains one integer, representing the x-coordinate of the ith station. Each of the next m lines contains two integers, representing the start point and the destination.
In all of the questions, the start point will be different from the destination.
For each case,2<=N<=100,0<=M<=500, each x-coordinate is between -1,000,000,000 and 1,000,000,000, and no two x-coordinates will have the same value.
 

Output
For each question, if the two stations are attainable, print the minimum cost between them. Otherwise, print “Station X and station Y are not attainable.” Use the format in the sample.
 

Sample Input
2 1 2 3 4 1 3 5 7 4 2 1 2 3 4 1 4 4 1 1 2 3 4 1 3 5 7 4 1 1 2 3 10 1 4
 

Sample Output
Case 1: The minimum cost between station 1 and station 4 is 3. The minimum cost between station 4 and station 1 is 3. Case 2: Station 1 and station 4 are not attainable.

最短路数据开—int 64否则wa inf要开1e18否则wa Floyd和dijkstra之类的问题主要是如何建立一个map[][]的图建立

Floyd的做法:

#include <stdio.h>
#include<algorithm>
#include<string.h>
using namespace std;
const __int64 inf = 1e18;
__int64 map[105][105];
__int64 flag=1;
int main(int argc, char *argv[])
{
	__int64 t,i,j;
	__int64 len[5],c[5];
	__int64 x[500];
	scanf("%I64d",&t);
	while(t--)
	{
		for(i=0;i<=100;i++)
		{
			for(j=0;j<=100;j++)
			{
				map[i][j]=inf;
			}
			map[i][i]=0;
		}
		for(i=1;i<5;i++)
		scanf("%I64d",&len[i]);
		for(i=1;i<5;i++)
		scanf("%I64d",&c[i]);;
		__int64 m,n;
		scanf("%I64d %I64d",&n,&m);
		for(i=1;i<=n;i++)
		{
			scanf("%I64d",&x[i]);
		} 
		__int64 a;
		for(i=1;i<=n-1;i++)
		{
			for(j=i+1;j<=n;j++)
			{
				a=abs(x[j]-x[i]);
				if(a>0&&a<=len[1])
				map[i][j]=map[j][i]=c[1];
				if(a>len[1]&&a<=len[2])
				map[i][j]=map[j][i]=c[2];
				if(a>len[2]&&a<=len[3])
				map[i][j]=map[j][i]=c[3];
				if(a>len[3]&&a<=len[4])
				map[i][j]=map[j][i]=c[4];
				else if(a>len[4])
				{
				map[i][j]=map[j][i]=inf;
				}
			}
		}
		__int64 k;
		for(k=1;k<=n;k++)
		{
			for(i=1;i<=n;i++)
			{
				for(j=1;j<=n;j++)
				{
					map[i][j]=min(map[i][j],map[i][k]+map[k][j]);
				}
			}
		}	
		printf("Case %I64d:\n",flag++);
		while(m--)
		{
			__int64 c,d;
			scanf("%I64d %I64d",&c,&d);
			if(map[c][d]!=inf)
			printf("The minimum cost between station %I64d and station %I64d is %I64d.\n",c,d,map[c][d]);
			else
			printf("Station %I64d and station %I64d are not attainable.\n",c,d);
		}
	}	
	return 0;
}


考虑柔性负荷的综合能源系统低碳经济优化调度【考虑碳交易机制】(Matlab代码实现)内容概要:本文围绕“考虑柔性负荷的综合能源系统低碳经济优化调度”展开,重点研究在碳交易机制下如何实现综合能源系统的低碳化与经济性协同优化。通过构建包含风电、光伏、储能、柔性负荷等多种能源形式的系统模型,结合碳交易成本与能源调度成本,提出优化调度策略,以降低碳排放并提升系统运行经济性。文中采用Matlab进行仿真代码实现,验证了所提模型在平衡能源供需、平抑可再生能源波动、引导柔性负荷参与调度等方面的有效性,为低碳能源系统的设计与运行提供了技术支撑。; 适合人群:具备一定电力系统、能源系统背景,熟悉Matlab编程,从事能源优化、低碳调度、综合能源系统等相关领域研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①研究碳交易机制对综合能源系统调度决策的影响;②实现柔性负荷在削峰填谷、促进可再生能源消纳中的作用;③掌握基于Matlab的能源系统建模与优化求解方法;④为实际综合能源项目提供低碳经济调度方案参考。; 阅读建议:建议读者结合Matlab代码深入理解模型构建与求解过程,重点关注目标函数设计、约束条件设置及碳交易成本的量化方式,可进一步扩展至多能互补、需求响应等场景进行二次开发与仿真验证。
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