usaco 4.2 Job Processing(贪心)

本文探讨了一种生产流水线任务调度问题,通过算法优化找到完成所有任务所需的时间。详细介绍了输入输出格式,提供了示例输入输出,并通过代码实现了解决方案。

Job Processing
IOI'96

A factory is running a production line that requires two operations to be performed on each job: first operation "A" then operation "B". Only a certain number of machines are capable of performing each operation.

Figure 1 shows the organization of the production line that works as follows. A type "A" machine takes a job from the input container, performs operation "A" and puts the job into the intermediate container. A type "B" machine takes a job from the intermediate container, performs operation "B" and puts the job into the output container. All machines can work in parallel and independently of each other, and the size of each container is unlimited. The machines have different performance characteristics, a given machine requires a given processing time for its operation.

Give the earliest time operation "A" can be completed for all N jobs provided that the jobs are available at time 0. Compute the minimal amount of time that is necessary to perform both operations (successively, of course) on all N jobs.

PROGRAM NAME: job

INPUT FORMAT

Line 1:Three space-separated integers:
  • N, the number of jobs (1<=N<=1000).
  • M1, the number of type "A" machines (1<=M1<=30)
  • M2, the number of type "B" machines (1<=M2<=30)
Line 2..etc:M1 integers that are the job processing times of each type "A" machine (1..20) followed by M2 integers, the job processing times of each type "B" machine (1..20).

SAMPLE INPUT (file job.in)

5 2 3
1 1 3 1 4

OUTPUT FORMAT

A single line containing two integers: the minimum time to perform all "A" tasks and the minimum time to perform all "B" tasks (which require "A" tasks, of course).

SAMPLE OUTPUT (file job.out)

3 5

题目: http://ace.delos.com/usacoprob2?a=6usYpMytQ3l&S=job

题意:给你n个任务,每个任务要用ab两个步骤完成,有m1台机器做a步骤,m2台机器做b步骤,求完成a步骤的时间,和完成b步骤的时间

分析:这题显然是任务调度问题,对于第一问,也就是完成a步骤,对于第二问,一开始我以为是网络流问题,因为这道题放在这个地方= =,其实还是个贪心问题,我们只要假设没有a步骤,然后同a步骤的做法,求出每个任务完成的具体时间,ab两个时间时间表反向相加的最大值就是第二问

代码:

/*
ID: 15114582
PROG: job
LANG: C++
*/
#include<cstdio>
#include<iostream>
#include<cstring>
#include<algorithm>
using namespace std;
int a[33],b[33],s1[33],s2[33],aa[1111],bb[1111];
int main()
{
    freopen("job.in","r",stdin);
    freopen("job.out","w",stdout);
    int i,j,k,n,m1,m2,t;
    while(~scanf("%d%d%d",&n,&m1,&m2))
    {
        for(i=0;i<m1;++i)
            scanf("%d",&a[i]),s1[i]=0;;
        for(i=0;i<m2;++i)
            scanf("%d",&b[i]),s2[i]=0;
        for(t=0;t<n;++t)
        {
            k=1e9;
            for(i=0;i<m1;++i)
                if(s1[i]+a[i]<k)
                {
                    k=s1[i]+a[i];
                    j=i;
                }
            aa[t]=s1[j]=k;
            k=1e9;
            for(i=0;i<m2;++i)
                if(s2[i]+b[i]<k)
                {
                    k=s2[i]+b[i];
                    j=i;
                }
            bb[t]=s2[j]=k;
        }
        sort(aa,aa+n);
        sort(bb,bb+n);
        printf("%d ",aa[n-1]);
        for(k=i=0;i<n;++i)
            k=max(k,aa[i]+bb[n-i-1]);
        printf("%d\n",k);
    }
    return 0;
}



内容概要:本文介绍了一个基于冠豪猪优化算法(CPO)的无人机三维路径规划项目,利用Python实现了在复杂三维环境中为无人机规划安全、高效、低能耗飞行路径的完整解决方案。项目涵盖空间环境建模、无人机动力学约束、路径编码、多目标代价函数设计以及CPO算法的核心实现。通过体素网格建模、动态障碍物处理、路径平滑技术和多约束融合机制,系统能够在高维、密集障碍环境下快速搜索出满足飞行可行性、安全性与能效最优的路径,并支持在线重规划以适应动态环境变化。文中还提供了关键模块的代码示例,包括环境建模、路径评估和CPO优化流程。; 适合人群:具备一定Python编程基础和优化算法基础知识,从事无人机、智能机器人、路径规划或智能优化算法研究的相关科研人员与工程技术人员,尤其适合研究生及有一定工作经验的研发工程师。; 使用场景及目标:①应用于复杂三维环境下的无人机自主导航与避障;②研究智能优化算法(如CPO)在路径规划中的实际部署与性能优化;③实现多目标(路径最短、能耗最低、安全性最高)耦合条件下的工程化路径求解;④构建可扩展的智能无人系统决策框架。; 阅读建议:建议结合文中模型架构与代码示例进行实践运行,重点关注目标函数设计、CPO算法改进策略与约束处理机制,宜在仿真环境中测试不同场景以深入理解算法行为与系统鲁棒性。
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