PAT 甲级 1018 Public Bike Management(dijkstra+dfs)

本文介绍了一个基于最短路径和最小成本原则的公共自行车调度算法,用于解决城市中自行车分布不均的问题。通过迪杰斯特拉算法寻找从调度中心到问题站点的最短路径,并结合深度优先搜索确定最少送回车辆数的最优路径。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

1018 Public Bike Management(30 分)

There is a public bike service in Hangzhou City which provides great convenience to the tourists from all over the world. One may rent a bike at any station and return it to any other stations in the city.

The Public Bike Management Center (PBMC) keeps monitoring the real-time capacity of all the stations. A station is said to be in perfect condition if it is exactly half-full. If a station is full or empty, PBMC will collect or send bikes to adjust the condition of that station to perfect. And more, all the stations on the way will be adjusted as well.

When a problem station is reported, PBMC will always choose the shortest path to reach that station. If there are more than one shortest path, the one that requires the least number of bikes sent from PBMC will be chosen.

The above figure illustrates an example. The stations are represented by vertices and the roads correspond to the edges. The number on an edge is the time taken to reach one end station from another. The number written inside a vertex SSS is the current number of bikes stored at SSS. Given that the maximum capacity of each station is 10. To solve the problem at S3S_3S​3​​, we have 2 different shortest paths:

  1. PBMC -> S1S_1S​1​​ -> S3S_3S​3​​. In this case, 4 bikes must be sent from PBMC, because we can collect 1 bike from S1S_1S​1​​ and then take 5 bikes to S3S_3S​3​​, so that both stations will be in perfect conditions.

  2. PBMC -> S2S_2S​2​​ -> S3S_3S​3​​. This path requires the same time as path 1, but only 3 bikes sent from PBMC and hence is the one that will be chosen.

Input Specification:

Each input file contains one test case. For each case, the first line contains 4 numbers: CmaxC_{max}C​max​​ (≤100\le 100≤100), always an even number, is the maximum capacity of each station; NNN (≤500\le 500≤500), the total number of stations; SpS_pS​p​​, the index of the problem station (the stations are numbered from 1 to NNN, and PBMC is represented by the vertex 0); and MMM, the number of roads. The second line contains NNN non-negative numbers CiC_iC​i​​ (i=1,⋯,Ni=1,\cdots ,Ni=1,⋯,N) where each CiC_iC​i​​ is the current number of bikes at SiS_iS​i​​ respectively. Then MMM lines follow, each contains 3 numbers: SiS_iS​i​​, SjS_jS​j​​, and TijT_{ij}T​ij​​ which describe the time TijT_{ij}T​ij​​ taken to move betwen stations SiS_iS​i​​ and SjS_jS​j​​. All the numbers in a line are separated by a space.

Output Specification:

For each test case, print your results in one line. First output the number of bikes that PBMC must send. Then after one space, output the path in the format: 0−>S1−>⋯−>Sp0->S_1->\cdots ->S_p0−>S​1​​−>⋯−>S​p​​. Finally after another space, output the number of bikes that we must take back to PBMC after the condition of SpS_pS​p​​ is adjusted to perfect.

Note that if such a path is not unique, output the one that requires minimum number of bikes that we must take back to PBMC. The judge's data guarantee that such a path is unique.

Sample Input:

10 3 3 5
6 7 0
0 1 1
0 2 1
0 3 3
1 3 1
2 3 1

Sample Output:

3 0->2->3 0

Analysis:

a trap is that it's easy to mistake this problem for greedy algorithm, the number of bike sent from and to PMBC doesn't satisfy locally optimal solution,hence can't be treated as array dis[].

the best algorithm is dijkstra +dfs.

c++:

#include<stdio.h>
#include<iostream>
#include<string>
#include<vector>
#include<map>
#include<queue>
#include<set>
#include<math.h>
#include<string.h>
#include<algorithm>
using namespace std;
int dis[501],vertex[501],edge[501][501],vis[501];
int cmax,n,p,m,mincarry=1e9,minsurplus=1e9;
vector<int>pre[501];
vector<int>path; //temporary variable
vector<int>re;
void dijkstra()//a shorter dijkstra
{
	dis[0]=0;
	for(int i=0;i<=n;i++){
		int mint=1e9,v=-1;
		for(int j=0;j<=n;j++){//find the shortest
			if(vis[j]!=1&&dis[j]<mint){
				mint=dis[j];
				v=j;
			}
		}
		if(v==-1)break;//not find
		vis[v]=1;//avoid repeating 
		for(int j=0;j<=n;j++){
			if(vis[j]!=1){
				if(dis[j]>dis[v]+edge[v][j]){
					dis[j]=dis[v]+edge[v][j];
					pre[j].clear();//clear all the other path 
					pre[j].push_back(v);
				}
				else if(dis[j]==dis[v]+edge[v][j]){
					pre[j].push_back(v);
				}
			}
		}
	}
}
void dfs(int v)
{
	path.push_back(v);
	if(v==0){
		int carry=0,surplus=0;
		for(int i=path.size()-2;i>=0;i--){
			int num=vertex[path[i]];
			surplus+=num-cmax/2;
			if(surplus<0){
				carry-=surplus;
				surplus=0;
			}
		}
		if(carry<mincarry){
			mincarry=carry;
			minsurplus=surplus;
			re.assign(path.begin(),path.end());//copy
		}
		else if(carry==mincarry&&surplus<minsurplus){
			minsurplus=surplus;
			re.assign(path.begin(),path.end());
		}
		return ;	
	}	
	for(int i=0;i<pre[v].size();i++){
		dfs(pre[v][i]);
		path.pop_back();//backtracking
	}
}
int main()
{
	cin>>cmax>>n>>p>>m;
	for(int i=0;i<=n;i++){
		dis[i]=1e9;
		for(int j=0;j<=n;j++)
			edge[i][j]=edge[i][j]=1e9;
	}
	for(int i=1;i<=n;i++)
		cin>>vertex[i];
	for(int i=0;i<m;i++){
		int a,b,t;
		cin>>a>>b>>t;
		edge[a][b]=edge[b][a]=t;
	}
	dijkstra();
	dfs(p);
	cout<<mincarry<<" 0";
	for(int i=re.size()-2;i>=0;i--)
		cout<<"->"<<re[i];
	cout<<" "<<minsurplus;
	return 0;
}

 

内容概要:本文针对火电厂参与直购交易挤占风电上网空间的问题,提出了一种风火打捆参与大用户直购交易的新模式。通过分析可再生能源配额机制下的双边博弈关系,建立了基于动态非合作博弈理论的博弈模型,以直购电价和直购电量为决策变量,实现双方收益均衡最大化。论文论证了纳什均衡的存在性,并提出了基于纳什谈判法的风-火利益分配方法。算例结果表明,该模式能够增加各方收益、促进风电消纳并提高电网灵活性。文中详细介绍了模型构建、成本计算和博弈均衡的实现过程,并通过Python代码复现了模型,包括参数定义、收益函数、纳什均衡求解、利益分配及可视化分析等功能。 适合人群:电力系统研究人员、能源政策制定者、从事电力市场交易的工程师和分析师。 使用场景及目标:①帮助理解风火打捆参与大用户直购交易的博弈机制;②为电力市场设计提供理论依据和技术支持;③评估不同政策(如可再生能源配额)对电力市场的影响;④通过代码实现和可视化工具辅助教学和研究。 其他说明:该研究不仅提供了理论分析,还通过详细的代码实现和算例验证了模型的有效性,为实际应用提供了参考。此外,论文还探讨了不同场景下的敏感性分析,如证书价格、风电比例等对市场结果的影响,进一步丰富了研究内容。
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值