如何用Tuple合并4个决策变量数组为二维数组并在OPL IDE中显示

需求说明 #

现有4个决策变量数组，需合并为一个二维数组（行对应Nrows，列对应Nchannels），并实现可视化。原始参数代码如下：

int Nbeams=21;      // 待分配到二维数组的波束数量
int Nchannels=16;   // 列数（通道数）
int Nrows=4;        // 行数

range rows = 1..Nrows;
range beams = 1..Nbeams;

dvar int No_beams_in_each_row[rows] = [6,6,6,4];
dvar int first_beam_in_each_row [rows] = [1,7,13,19];
dvar int beam_firstchannel[beams] = [1,3,5,7,9,11,1,3,5,7,9,11,1,3,5,7,9,11,1,5,9,13];
dvar int beam_nomusedchannel[beams] = [2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,4,4,4,4];
dvar int beam_row[beams] = [1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,4,4,4,4]; 

完整解决方案 #

以下提供两种可视化方式，先通过约束将现有变量合并为二维数组，再分别实现OPL IDE内视图和Python外部可视化：

int Nbeams=21;      // 待分配到二维数组的波束数量
int Nchannels=16;   // 列数（通道数）
int Nrows=4;        // 行数

range rows = 1..Nrows;
range beams = 1..Nbeams;
range channels = 1..Nchannels;

dvar int No_beams_in_each_row[rows] = [6,6,6,4];
dvar int first_beam_in_each_row [rows] = [1,7,13,19];
dvar int beam_firstchannel[beams] = [1,3,5,7,9,11,1,3,5,7,9,11,1,3,5,7,9,11,1,5,9,13];
dvar int beam_nomusedchannel[beams] = [2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,4,4,4,4];
dvar int beam_row[beams] = [1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,4,4,4,4]; 

// 定义二维数组：row×channel，存储对应位置的波束编号（0表示未占用）
dvar int beam_in_channel[rows][channels] in 0..Nbeams;

subject to {
  // 约束：根据波束的行、起始通道、占用通道数填充二维数组
  forall(b in beams) {
    let r = beam_row[b];
    let start_c = beam_firstchannel[b];
    let num_c = beam_nomusedchannel[b];
    // 确保波束占用的通道不超出范围
    start_c + num_c - 1 <= Nchannels;
    forall(c in start_c..start_c + num_c - 1) {
      beam_in_channel[r][c] == b;
    }
  }
  // 约束：未被占用的位置设为0
  forall(r in rows, c in channels) {
    if (not exists(b in beams: beam_row[b]==r && c >= beam_firstchannel[b] && c < beam_firstchannel[b]+beam_nomusedchannel[b])) {
      beam_in_channel[r][c] == 0;
    }
  }
}

// --- 方式1：在OPL IDE中通过Gantt视图展示二维数组 ---
tuple sequence_like {
   int start;
   int end;
   string label;
   int type;
};

// 为每一行生成Gantt视图所需的序列数据
{sequence_like} channel_beam_view[r in rows] = {<c-1, c, (beam_in_channel[r][c]==0 ? " " : "Beam " + beam_in_channel[r][c]), beam_in_channel[r][c]> | c in channels};

execute show_in_opl_ide {
   channel_beam_view; // 在OPL IDE的"Output"标签下可查看Gantt视图
}

// --- 方式2：调用Python生成热力图可视化 ---
tuple BeamChannelSolution {
    int row;
    int channel;
    int beam_id;
};
{BeamChannelSolution} beam_channel_solution = {<r, c, beam_in_channel[r][c]> | r in rows, c in channels};

execute display_with_python {
    // 生成Python可视化脚本
    var python = new IloOplOutputFile("c:/temp/beam_channel_display.py");
    python.writeln("import matplotlib.pyplot as plt");
    python.writeln("import numpy as np");
    python.writeln("grid = np.array([");
    
    // 逐行写入二维数组数据
    for(var r in rows) {
        python.write("[");
        for(var c in channels) {
            python.write(beam_in_channel[r][c]);
            if (c != channels.last()) python.write(", ");
        }
        python.writeln("],");
    }
    
    python.writeln("])");
    python.writeln("plt.figure(figsize=(12, 4))");
    python.writeln("im = plt.imshow(grid, cmap='viridis', aspect='auto')");
    python.writeln("plt.colorbar(im, label='Beam ID (0 = 未占用)')");
    python.writeln("plt.xticks(ticks=np.arange(channels.size()), labels=np.arange(1, channels.size()+1))");
    python.writeln("plt.yticks(ticks=np.arange(rows.size()), labels=np.arange(1, rows.size()+1))");
    python.writeln("plt.xlabel('通道')");
    python.writeln("plt.ylabel('行')");
    python.writeln("plt.title('波束在-行通道网格上的分布')");
    python.writeln("plt.show()");
    python.close();
    
    // 执行脚本，需替换为你的Python实际安装路径
    IloOplExec("C:/Python39/python.exe c:/temp/beam_channel_display.py");
}

说明 #

1. 二维数组构建：通过约束将beam_row、beam_firstchannel、beam_nomusedchannel三个变量映射到beam_in_channel[row][channel]二维数组，实现数据合并。
2. OPL IDE视图：生成Gantt序列数据后，在OPL IDE的Output标签中选中channel_beam_view即可查看二维分布。
3. Python可视化：自动生成Python脚本并执行，生成带标签的热力图，直观展示波束在各行各通道的分布情况，注意替换脚本中的Python路径为实际安装路径。

内容的提问来源于stack exchange，提问作者MO MO MA