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<body>
<h1 id="toc_0">Binpacking</h1>
<h4 id="toc_1">* Python3</h4>
<h4 id="toc_2">1. Algorithm</h4>
<blockquote>
<p>非降序算法，在线启发式算法 next_fit,first_fit,worst_fit,almost_worst<em>fit,best_fit
降序算法，离线启发式算法 next_fit,first_fit,worst_fit,almost_worst</em>fit,best_fit
MBS, MBS', AMBS, IAMBS</p>
</blockquote>
<h4 id="toc_3">2. 目录结构</h4>
<div><pre><code class="language-none">├── README.md
├── __pycache__
├── _backup # 历史备份
├── add_knownSOL.py # 增加已知最优解到最终结果
├── analysis.py # 分析平均竞争比和平均运行时间
├── benchmark # 数据集的目前已知最优解集
│ ├── SCH_WAE_KOS.csv
│ ├── WAE_GAU_KOS.csv # ！！！！！！这部分经验证是错误的已知最优解
│ ├── bin1data_KOS.csv
│ ├── bin2data_KOS.csv
│ ├── bin3data_KOS.csv
│ ├── instances_KOS.csv
│ └── ragion_formal # 已知最优解数据集原文件
├── bin_pack.py # 装箱算法
├── bin_pack_main_AMBS.py # 主程序
├── binary_tree.py # 平衡二叉树
├── binpacking_data_set # 数据集
│ ├── bin1data
│ ├── bin2data
│ ├── bin3data
│ ├── hard28
│ ├── instances
│ ├── schwerin
│ ├── test_set # 测试数据集
│ └── waescher
├── compare_kol # 实验结果----对比实现已知最优解的数量
│ ├── _results_
│ └── bin-pack_08-15_03-02-01_instances.csv
├── compare_result.py # 对比实现已知最优解的数量
├── results # 实验结果----理论竞争比
│ ├── _results_
│ ├── bin-pack_08-15_03-02-01_instances.csv
│ └── bin-pack_08-15_03-02-01_instances.csv.png
├── results_final # 实验结果----已知最优解竞争比
│ ├── bin-pack_08-15_03-02-01_instances.csv
│ └── bin-pack_08-15_03-02-01_instances.csv.png</code></pre></div>
<h4 id="toc_4">3. 程序运行</h4>
<h5 id="toc_5">- 选择要运行的程序</h5>
<div><pre><code class="language-none">bin_pack.py</code></pre></div>
<p>非降序算法和降序算法不要在同一次试验中同时运行，因为算法名称相同，后期数据分析会吧结果混掉。
现有的数据集默认降序排列，所有建议只运行降序算法
<img src="./img/algorithms.png" alt=""></p>
<h5 id="toc_6">- 运行主程序</h5>
<div><pre><code class="language-none">bin_pack_main_AMBS.py
</code></pre></div>
<p><img src="./img/main_entry.png" alt=""></p>
<h5 id="toc_7">- 试验结果</h5>
<p><img src="./img/results.png" alt=""></p>
</body>
</html>