UVA 11729 Commando War

本文探讨了一种指挥官战争背景下的任务分配问题,通过合理安排士兵的任务执行顺序,实现整个作战计划时间最短化的目标。文章提供了一个具体的算法实现方案,即按任务执行时间升序排列并依次执行。

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G

Commando  War

Input: Standard Input

Output: Standard Output

 

 

“Waiting for orders we held in the wood, word from the front never came

By evening the sound of the gunfire was miles away

Ah softly we moved through the shadows, slip away through the trees

Crossing their lines in the mists in the fields on our hands and our knees

And all that I ever, was able to see

The fire in the air, glowing red, silhouetting the smoke on the breeze”

 

There is a war and it doesn't look very promising for your country. Now it's time to act. You have a commando squad at your disposal and planning an ambush on an important enemy camp located nearby. You haveN soldiers in your squad. In your master-plan, every single soldier has a unique responsibility and you don't want any of your soldier to know the plan for other soldiers so that everyone can focus on his task only. In order to enforce this, you brief every individual soldier about his tasks separately and just before sending him to the battlefield. You know that every single soldier needs a certain amount of time to execute his job. You also know very clearly how much time you need to brief every single soldier. Being anxious to finish the total operation as soon as possible, you need to find an order of briefing your soldiers that will minimize the time necessary for all the soldiers to complete their tasks. You may assume that, no soldier has a plan that depends on the tasks of his fellows. In other words, once a soldier  begins a task, he can finish it without the necessity of pausing in between.

 

Input

 

There will be multiple test cases in the input file. Every test case starts with an integerN (1<=N<=1000), denoting the number of soldiers. Each of the following N lines describe a soldier with two integersB (1<=B<=10000) & J (1<=J<=10000). B seconds are needed to brief the soldier while completing his job needs J seconds. The end of input will be denoted by a case with N =0 . This case should not be processed.

 

Output

 

For each test case, print a line in the format, “Case X: Y”, where X is the case number & Y is the total number of seconds counted from the start of your first briefing till the completion of all jobs.

 

Sample Input                                               Output for Sample Input

3

2 5

3 2

2 1

3

3 3

4 4

5 5

0

Case 1: 8

Case 2: 15

 

 

 

你要交代你n个部下每人一项任务,交代第i个部下的交代时间是Bi分钟,他的执行时间是Ji分钟,不能同时交代两项任务。要求你有选择交代任务的顺序,求所有任务最快完成的时间。

将J从小到大排序,然后依次执行。

#include<stdio.h>
#include<algorithm>
using namespace std;
struct node
{
	int b,j;
}solider[1007];

int cmp(const node a,const node b)
{
	return a.j>b.j;
}
int main()
{
	int n,kase=1;
	while(scanf("%d",&n),n)
	{
		int i,j;
		for(i=0;i<n;i++)
		scanf("%d%d",&solider[i].b,&solider[i].j);
		sort(solider,solider+n,cmp);
		int ans=0,s=0;
		for(i=0;i<n;i++)
		{
			s+=solider[i].b;
			ans=max(ans,s+solider[i].j);
		}
		printf("Case %d: %d\n",kase++,ans);
	}
	return 0;
}


 

内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
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