HDU 1277 Fast Food

本文介绍了一个关于快餐连锁店 McBurger 的物流配送优化问题。通过在高速公路沿线的餐厅中设立仓库来减少配送距离,使用动态规划算法求解最优的仓库位置及分配方案,以达到最小化总配送距离的目标。

Problem Description
The fastfood chain McBurger owns several restaurants along a highway. Recently, they have decided to build several depots along the highway, each one located at a restaurant and supplying several of the restaurants with the needed ingredients. Naturally, these depots should be placed so that the average distance between a restaurant and its assigned depot is minimized. You are to write a program that computes the optimal positions and assignments of the depots.

To make this more precise, the management of McBurger has issued the following specification: You will be given the positions of n restaurants along the highway as n integers d1 < d2 < ... < dn (these are the distances measured from the company's headquarter, which happens to be at the same highway). Furthermore, a number k (k <= n) will be given, the number of depots to be built.

The k depots will be built at the locations of k different restaurants. Each restaurant will be assigned to the closest depot, from which it will then receive its supplies. To minimize shipping costs, the total distance sum, defined as



must be as small as possible.

Write a program that computes the positions of the k depots, such that the total distance sum is minimized.

Input
The input file contains several descriptions of fastfood chains. Each description starts with a line containing the two integers n and k. n and k will satisfy 1 <= n <= 200, 1 <= k <= 30, k <= n. Following this will n lines containing one integer each, giving the positions di of the restaurants, ordered increasingly.

The input file will end with a case starting with n = k = 0. This case should not be processed.

Output
For each chain, first output the number of the chain. Then output a line containing the total distance sum.

Output a blank line after each test case.

Sample Input

 
6 3 5 6 12 19 20 27 0 0

Sample Output

 
Chain 1 Total distance sum = 8

Source





用dp[i][j]表示前j个饭店建第i个仓库的最短路径。dp[i][j]=min{dp[i-1][k]+cost[k+1][j]},

且i-1<=k<j。cost[i][j]表示在i到j之间建一个仓库的最小路径,要在i~j之间建立一个仓库,那么

必须建立在下标为(i+j)/2的饭店。

  1. #include<iostream>
  2. #include<cmath>
  3. usingnamespacestd;
  4. intmain()
  5. {
  6. intn,m;
  7. inti,j,k,t=0;
  8. intdp[202][202],cost[202][202];
  9. intfood[202];
  10. //freopen("in.txt","r",stdin);
  11. while(scanf("%d%d",&n,&m),n+m)
  12. {
  13. t++;
  14. for(i=1;i<=n;i++)
  15. scanf("%d",&food[i]);
  16. memset(cost,0,sizeof(cost));
  17. for(i=1;i<=n;i++)
  18. {
  19. for(j=i;j<=n;j++)
  20. {
  21. for(k=i;k<=j;k++)
  22. {
  23. cost[i][j]+=abs(food[k]-food[(i+j)/2]);
  24. }
  25. }
  26. }
  27. memset(dp,0,sizeof(dp));
  28. for(i=1;i<=n;i++)
  29. {
  30. dp[1][i]=cost[1][i];
  31. }
  32. for(i=2;i<=m;i++)
  33. {
  34. for(j=i+1;j<=n;j++)
  35. {
  36. intMin=0xfffffff;
  37. for(k=i-1;k<j;k++)
  38. {
  39. if(Min>dp[i-1][k]+cost[k+1][j])
  40. {
  41. Min=dp[i-1][k]+cost[k+1][j];
  42. }
  43. }
  44. dp[i][j]=Min;
  45. }
  46. }
  47. printf("Chain%d/n",t);
  48. printf("Totaldistancesum=%d/n",dp[m][n]);
  49. printf("/n");
  50. }
  51. return0;
  52. }

内容概要:本文提出了一种基于融合鱼鹰算法和柯西变异的改进麻雀优化算法(OCSSA),用于优化变分模态分解(VMD)的参数,进而结合卷积神经网络(CNN)与双向长短期记忆网络(BiLSTM)构建OCSSA-VMD-CNN-BILSTM模型,实现对轴承故障的高【轴承故障诊断】基于融合鱼鹰和柯西变异的麻雀优化算法OCSSA-VMD-CNN-BILSTM轴承诊断研究【西储大学数据】(Matlab代码实现)精度诊断。研究采用西储大学公开的轴承故障数据集进行实验验证,通过优化VMD的模态数和惩罚因子,有效提升了信号分解的准确性与稳定性,随后利用CNN提取故障特征,BiLSTM捕捉时间序列的深层依赖关系,最终实现故障类型的智能识别。该方法在提升故障诊断精度与鲁棒性方面表现出优越性能。; 适合人群:具备一定信号处理、机器学习基础,从事机械故障诊断、智能运维、工业大数据分析等相关领域的研究生、科研人员及工程技术人员。; 使用场景及目标:①解决传统VMD参数依赖人工经验选取的问题,实现参数自适应优化;②提升复杂工况下滚动轴承早期故障的识别准确率;③为智能制造与预测性维护提供可靠的技术支持。; 阅读建议:建议读者结合Matlab代码实现过程,深入理解OCSSA优化机制、VMD信号分解流程以及CNN-BiLSTM网络架构的设计逻辑,重点关注参数优化与故障分类的联动关系,并可通过更换数据集进一步验证模型泛化能力。
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