fix: pyinstaller
Dawn1Ocean
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__pycache__/
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build/
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dist/
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other/
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*.egg-info/
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*.pdf
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*.spec
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__pycache__/
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build/
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dist/
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other/
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*.egg-info/
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*.pdf
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*.spec
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3.12
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3.12
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README.md
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README.md
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# EVA 值班排班工具
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## 环境配置
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本项目使用 `uv` 进行包管理。
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安装项目中所需要的所有包的最新版本。其中 `pyside6` 是一个前端库，`pyinstaller` 用于打包项目，`ortools` 是一个高效的组合优化求解器，`pandas` 用于处理表格数据：
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```bash
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uv sync
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```
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## 项目结构
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```
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EVA_duty_arrange_tool/
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├─ main.py // 主函数，定义了前端界面和组件的回调函数
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├─ solve.py // 定义了值班排班问题的求解函数
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├─ utils.py // 定义了读取、写入 excel 的函数
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├─ pics // 储存了说明文档中用到的图片
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│ ├─ *.jpg/*.png
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├─ *.md // 说明文档
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├─ *.xlsx // 测试用例
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```
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## 项目运行 & 打包
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项目运行方式：
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```bash
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uv run main.py
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```
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本项目使用 `pyinstaller` 工具进行打包。如果要打包，请确保能够正常运行项目。打包命令如下：
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```bash
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uv run pyinstaller --onefile --windowed --name=EVA_duty_arrange_tool main.py
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```
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## 数学原理
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本项目将值班排班问题建模为了一个[组合优化](https://zh.wikipedia.org/wiki/%E7%BB%84%E5%90%88%E4%BC%98%E5%8C%96)问题，使用 Google 的 OR-Tools 求解器中的 SCIP 求解器进行求解。
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### 问题描述
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在本问题中，优化目标是一个涉及多个方面的量化指标：
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- **目标1**：每一班同学数量的平均程度
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- **目标2**：每一班技术部老人数量接近期望值
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- **目标3**：每一班技术部小朋友数量接近期望值
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- **目标4**：每一班人资部小朋友数量接近期望值
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- **目标5**：每一班各部门人数的平均程度
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在本问题中，主要约束是：
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1. 让每位同学每周的班次数符合意愿（特别地，选择3次的同学可以安排2-3次）
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2. 让每位同学只在自己有空的时间段值班
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3. 每班次的总人数在指定范围内
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4. 每班次技术部老人数量在指定范围内
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5. 每班次老人总数在指定范围内
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6. 每班次小朋友总数在指定范围内
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### 数学建模
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设一共有 $n$ 位同学，$m$ 个值班的班次，协会共有 $t=5$ 个部门（电脑部、电器部、人资部、财外部、文宣部），定义以下符号：
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**决策变量：**
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- $x_{ij} \in \{0,1\}, \quad i=1,2,\dots,n,\quad j=1,2,\dots,m$ 表示第 $i$ 位同学是否值第 $j$ 班
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**已知参数：**
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- $N_i \in \mathbb{N}, \quad i=1,2,\dots,n$ 表示第 $i$ 个同学每周愿意值班次数
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- $v_{ij} \in \{0,1\}, \quad i=1,2,\dots,n,\quad j=1,2,\dots,m$ 表示第 $i$ 位同学是否有空值第 $j$ 班
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- $\text{old}_i \in \{0,1\}, \quad i=1,2,\dots,n$ 表示第 $i$ 位同学是否是老人（非小朋友）
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- $d_{ik}\in\{0,1\}, \quad i=1,2,\dots,n,\quad k=1,2,\dots,t$ 表示第 $i$ 位同学是否属于第 $k$ 个部门
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- $\text{tech}_i = d_{i1} + d_{i2} \in \{0,1\}$ 表示第 $i$ 位同学是否属于技术部（电脑部或电器部）
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- $\text{hr}_i = d_{i3} \in \{0,1\}$ 表示第 $i$ 位同学是否属于人资部
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**派生量：**
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- $M_j = \sum_{i=1}^{n} x_{ij}, \quad j=1,2,\dots,m$ 表示第 $j$ 班次实际安排的值班人数
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- $\bar{M} = \frac{\sum_{i=1}^{n}N_i}{m}$ 表示平均每班的人数
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### 优化目标
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为了处理多目标优化问题，我们采用**线性加权法**将多个目标组合成单一目标函数。引入归一化系数 $m_1=2.5, m_2=3, m_3=4, m_4=1, m_5=8$ 和权重 $w_1, w_2, w_3, w_4, w_5$，总目标函数为：
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$$\min \quad Z = w_1 \cdot \frac{X_1}{m_1 \cdot M} + w_2 \cdot \frac{X_2}{m_2 \cdot M} + w_3 \cdot \frac{X_3}{m_3 \cdot M} + w_4 \cdot \frac{X_4}{m_4 \cdot M} + w_5 \cdot \frac{X_5}{m_5 \cdot M}$$
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各目标定义如下：
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**目标1：均衡班次人数**
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$$X_1 = \sum_{j=1}^{m} a_j^{(1)}$$
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其中辅助变量 $a_j^{(1)} \in [0,+\infty)$ 满足：
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$$a_j^{(1)} \ge M_j - \bar{M}, \quad a_j^{(1)} \ge \bar{M} - M_j, \quad \forall j$$
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> 注：在组合优化问题中只能定义线性约束，不能直接使用绝对值。因此引入辅助变量 $a_j^{(1)}$ 并添加两个不等式约束，使得在优化过程中 $a_j^{(1)}$ 自动收敛到 $|M_j - \bar{M}|$。
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**目标2：每班技术部老人数量接近期望值**
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期望每班有 $m_2 = 3$ 位技术部老人，定义缺口：
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$$X_2 = \sum_{j=1}^{m} a_j^{(2)}$$
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其中辅助变量 $a_j^{(2)} \in [0,+\infty)$ 满足：
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$$a_j^{(2)} \ge \sum_{i=1}^{n} x_{ij} \cdot \text{old}_i \cdot \text{tech}_i - m_2$$
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$$a_j^{(2)} \ge m_2 - \sum_{i=1}^{n} x_{ij} \cdot \text{old}_i \cdot \text{tech}_i, \quad \forall j$$
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**目标3：每班技术部小朋友数量接近期望值**
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期望每班有 $m_3 = 4$ 位技术部小朋友，定义缺口：
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$$X_3 = \sum_{j=1}^{m} a_j^{(3)}$$
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其中辅助变量 $a_j^{(3)} \in [0,+\infty)$ 满足：
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$$a_j^{(3)} \ge \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \cdot \text{tech}_i - m_3$$
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$$a_j^{(3)} \ge m_3 - \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \cdot \text{tech}_i, \quad \forall j$$
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**目标4：每班人资部小朋友数量接近期望值**
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期望每班有 $m_4 = 1$ 位人资部小朋友，定义缺口：
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$$X_4 = \sum_{j=1}^{m} a_j^{(4)}$$
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其中辅助变量 $a_j^{(4)} \in [0,+\infty)$ 满足：
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$$a_j^{(4)} \ge \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \cdot \text{hr}_i - m_4$$
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$$a_j^{(4)} \ge m_4 - \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \cdot \text{hr}_i, \quad \forall j$$
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**目标5：均衡各部门人数分布**
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对每个班次 $j$ 和每个部门 $k$，定义该班次该部门的人数偏差：
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$$X_5 = \sum_{j=1}^{m} \sum_{k=1}^{t} a_{jk}^{(5)}$$
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其中 $\bar{M}_j = \frac{M_j}{t}$ 表示第 $j$ 班次各部门的平均人数，$C_{jk} = \sum_{i=1}^{n} x_{ij} \cdot d_{ik}$ 表示第 $j$ 班次第 $k$ 部门的实际人数，辅助变量 $a_{jk}^{(5)} \in [0,+\infty)$ 满足：
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$$a_{jk}^{(5)} \ge C_{jk} - \bar{M}_j, \quad a_{jk}^{(5)} \ge \bar{M}_j - C_{jk}, \quad \forall j,k$$
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### 约束条件
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**基本约束：**
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1. **意愿班次约束**：每位同学的值班次数必须符合其意愿
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$$\sum_{j=1}^{m}x_{ij} = N_i, \quad \forall i \text{ where } N_i \neq 3$$
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$$2 \le \sum_{j=1}^{m}x_{ij} \le 3, \quad \forall i \text{ where } N_i = 3$$
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2. **时间可行性约束**：只在有空的时间段排班
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$$x_{ij} \le v_{ij}, \quad \forall i,j$$
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**班次人数约束：**
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3. **总人数约束**：每班次人数在 $[n_{\min}, n_{\max}]$ 范围内
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$$n_{\min} \le \sum_{i=1}^{n} x_{ij} \le n_{\max}, \quad \forall j$$
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4. **技术部老人约束**：每班次技术部老人数在范围内
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$$n_{\text{tech\_old\_min}} \le \sum_{i=1}^{n} x_{ij} \cdot \text{old}_i \cdot \text{tech}_i \le n_{\text{tech\_old\_max}}, \quad \forall j$$
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5. **老人总数约束**：每班次老人总数在范围内
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$$n_{\text{old\_min}} \le \sum_{i=1}^{n} x_{ij} \cdot \text{old}_i \le n_{\text{old\_max}}, \quad \forall j$$
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6. **小朋友总数约束**：每班次小朋友总数在范围内
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$$n_{\text{new\_min}} \le \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \le n_{\text{new\_max}}, \quad \forall j$$
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### 求解器
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以上完成了整个排班问题的建模。本项目使用 Google OR-Tools 中的 **SCIP 求解器**（Solving Constraint Integer Programs）进行求解。SCIP 是一个强大的混合整数规划（MIP）求解器，特别适合处理这类组合优化问题。
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OR-Tools 支持 `C++`、`Python`、`C#`、`Java` 等多种语言，并提供跨平台支持。在本项目中，我们使用其 Python 接口 `ortools.linear_solver.pywraplp` 进行建模和求解。当找到最优解时，求解器会输出详细的目标值和各个优化指标的统计信息。
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## 维护指南
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- 如果你想更改 Excel 的读取、写入相关的功能，应该修改 `utils.py` 中的相关函数。
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- 如果你想更改软件的前端界面，应该修改 `main.py` 中 `MyWidget` 这个类相关的代码。
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# EVA 值班排班工具
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## 环境配置
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本项目使用 `uv` 进行包管理。
|
||||
|
||||
安装项目中所需要的所有包的最新版本。其中 `pyside6` 是一个前端库，`pyinstaller` 用于打包项目，`ortools` 是一个高效的组合优化求解器，`pandas` 用于处理表格数据：
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```bash
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uv sync
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```
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## 项目结构
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```
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EVA_duty_arrange_tool/
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├─ main.py // 主函数，定义了前端界面和组件的回调函数
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├─ solve.py // 定义了值班排班问题的求解函数
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├─ utils.py // 定义了读取、写入 excel 的函数
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├─ pics // 储存了说明文档中用到的图片
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│ ├─ *.jpg/*.png
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├─ *.md // 说明文档
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├─ *.xlsx // 测试用例
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```
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## 项目运行 & 打包
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项目运行方式：
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```bash
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uv run main.py
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```
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本项目使用 `pyinstaller` 工具进行打包。如果要打包，请确保能够正常运行项目。打包命令如下：
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```bash
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uv run pyinstaller --onefile --windowed --name=EVA_duty_arrange_tool --hidden-import=ortools --collect-all ortools --additional-hooks-dir=. main.py
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```
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> **注意**：由于 `ortools` 库包含大量 DLL 依赖,需要使用 `--collect-all ortools` 参数来收集所有必要的动态链接库。`--additional-hooks-dir=.` 参数指定使用项目根目录下的 `hook-ortools.py` 文件来确保正确打包。
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## 数学原理
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本项目将值班排班问题建模为了一个[组合优化](https://zh.wikipedia.org/wiki/%E7%BB%84%E5%90%88%E4%BC%98%E5%8C%96)问题，使用 Google 的 OR-Tools 求解器中的 SCIP 求解器进行求解。
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### 问题描述
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在本问题中，优化目标是一个涉及多个方面的量化指标：
|
||||
- **目标1**：每一班同学数量的平均程度
|
||||
- **目标2**：每一班技术部老人数量接近期望值
|
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- **目标3**：每一班技术部小朋友数量接近期望值
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- **目标4**：每一班人资部小朋友数量接近期望值
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- **目标5**：每一班各部门人数的平均程度
|
||||
|
||||
在本问题中，主要约束是：
|
||||
1. 让每位同学每周的班次数符合意愿（特别地，选择3次的同学可以安排2-3次）
|
||||
2. 让每位同学只在自己有空的时间段值班
|
||||
3. 每班次的总人数在指定范围内
|
||||
4. 每班次技术部老人数量在指定范围内
|
||||
5. 每班次老人总数在指定范围内
|
||||
6. 每班次小朋友总数在指定范围内
|
||||
|
||||
### 数学建模
|
||||
|
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设一共有 $n$ 位同学，$m$ 个值班的班次，协会共有 $t=5$ 个部门（电脑部、电器部、人资部、财外部、文宣部），定义以下符号：
|
||||
|
||||
**决策变量：**
|
||||
- $x_{ij} \in \{0,1\}, \quad i=1,2,\dots,n,\quad j=1,2,\dots,m$ 表示第 $i$ 位同学是否值第 $j$ 班
|
||||
|
||||
**已知参数：**
|
||||
- $N_i \in \mathbb{N}, \quad i=1,2,\dots,n$ 表示第 $i$ 个同学每周愿意值班次数
|
||||
- $v_{ij} \in \{0,1\}, \quad i=1,2,\dots,n,\quad j=1,2,\dots,m$ 表示第 $i$ 位同学是否有空值第 $j$ 班
|
||||
- $\text{old}_i \in \{0,1\}, \quad i=1,2,\dots,n$ 表示第 $i$ 位同学是否是老人（非小朋友）
|
||||
- $d_{ik}\in\{0,1\}, \quad i=1,2,\dots,n,\quad k=1,2,\dots,t$ 表示第 $i$ 位同学是否属于第 $k$ 个部门
|
||||
- $\text{tech}_i = d_{i1} + d_{i2} \in \{0,1\}$ 表示第 $i$ 位同学是否属于技术部（电脑部或电器部）
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- $\text{hr}_i = d_{i3} \in \{0,1\}$ 表示第 $i$ 位同学是否属于人资部
|
||||
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**派生量：**
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||||
- $M_j = \sum_{i=1}^{n} x_{ij}, \quad j=1,2,\dots,m$ 表示第 $j$ 班次实际安排的值班人数
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- $\bar{M} = \frac{\sum_{i=1}^{n}N_i}{m}$ 表示平均每班的人数
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### 优化目标
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为了处理多目标优化问题，我们采用**线性加权法**将多个目标组合成单一目标函数。引入归一化系数 $m_1=2.5, m_2=3, m_3=4, m_4=1, m_5=8$ 和权重 $w_1, w_2, w_3, w_4, w_5$，总目标函数为：
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$$\min \quad Z = w_1 \cdot \frac{X_1}{m_1 \cdot M} + w_2 \cdot \frac{X_2}{m_2 \cdot M} + w_3 \cdot \frac{X_3}{m_3 \cdot M} + w_4 \cdot \frac{X_4}{m_4 \cdot M} + w_5 \cdot \frac{X_5}{m_5 \cdot M}$$
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||||
|
||||
各目标定义如下：
|
||||
|
||||
**目标1：均衡班次人数**
|
||||
|
||||
$$X_1 = \sum_{j=1}^{m} a_j^{(1)}$$
|
||||
|
||||
其中辅助变量 $a_j^{(1)} \in [0,+\infty)$ 满足：
|
||||
$$a_j^{(1)} \ge M_j - \bar{M}, \quad a_j^{(1)} \ge \bar{M} - M_j, \quad \forall j$$
|
||||
|
||||
> 注：在组合优化问题中只能定义线性约束，不能直接使用绝对值。因此引入辅助变量 $a_j^{(1)}$ 并添加两个不等式约束，使得在优化过程中 $a_j^{(1)}$ 自动收敛到 $|M_j - \bar{M}|$。
|
||||
|
||||
**目标2：每班技术部老人数量接近期望值**
|
||||
|
||||
期望每班有 $m_2 = 3$ 位技术部老人，定义缺口：
|
||||
|
||||
$$X_2 = \sum_{j=1}^{m} a_j^{(2)}$$
|
||||
|
||||
其中辅助变量 $a_j^{(2)} \in [0,+\infty)$ 满足：
|
||||
$$a_j^{(2)} \ge \sum_{i=1}^{n} x_{ij} \cdot \text{old}_i \cdot \text{tech}_i - m_2$$
|
||||
$$a_j^{(2)} \ge m_2 - \sum_{i=1}^{n} x_{ij} \cdot \text{old}_i \cdot \text{tech}_i, \quad \forall j$$
|
||||
|
||||
**目标3：每班技术部小朋友数量接近期望值**
|
||||
|
||||
期望每班有 $m_3 = 4$ 位技术部小朋友，定义缺口：
|
||||
|
||||
$$X_3 = \sum_{j=1}^{m} a_j^{(3)}$$
|
||||
|
||||
其中辅助变量 $a_j^{(3)} \in [0,+\infty)$ 满足：
|
||||
$$a_j^{(3)} \ge \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \cdot \text{tech}_i - m_3$$
|
||||
$$a_j^{(3)} \ge m_3 - \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \cdot \text{tech}_i, \quad \forall j$$
|
||||
|
||||
**目标4：每班人资部小朋友数量接近期望值**
|
||||
|
||||
期望每班有 $m_4 = 1$ 位人资部小朋友，定义缺口：
|
||||
|
||||
$$X_4 = \sum_{j=1}^{m} a_j^{(4)}$$
|
||||
|
||||
其中辅助变量 $a_j^{(4)} \in [0,+\infty)$ 满足：
|
||||
$$a_j^{(4)} \ge \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \cdot \text{hr}_i - m_4$$
|
||||
$$a_j^{(4)} \ge m_4 - \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \cdot \text{hr}_i, \quad \forall j$$
|
||||
|
||||
**目标5：均衡各部门人数分布**
|
||||
|
||||
对每个班次 $j$ 和每个部门 $k$，定义该班次该部门的人数偏差：
|
||||
|
||||
$$X_5 = \sum_{j=1}^{m} \sum_{k=1}^{t} a_{jk}^{(5)}$$
|
||||
|
||||
其中 $\bar{M}_j = \frac{M_j}{t}$ 表示第 $j$ 班次各部门的平均人数，$C_{jk} = \sum_{i=1}^{n} x_{ij} \cdot d_{ik}$ 表示第 $j$ 班次第 $k$ 部门的实际人数，辅助变量 $a_{jk}^{(5)} \in [0,+\infty)$ 满足：
|
||||
|
||||
$$a_{jk}^{(5)} \ge C_{jk} - \bar{M}_j, \quad a_{jk}^{(5)} \ge \bar{M}_j - C_{jk}, \quad \forall j,k$$
|
||||
|
||||
### 约束条件
|
||||
|
||||
**基本约束：**
|
||||
|
||||
1. **意愿班次约束**：每位同学的值班次数必须符合其意愿
|
||||
$$\sum_{j=1}^{m}x_{ij} = N_i, \quad \forall i \text{ where } N_i \neq 3$$
|
||||
$$2 \le \sum_{j=1}^{m}x_{ij} \le 3, \quad \forall i \text{ where } N_i = 3$$
|
||||
|
||||
2. **时间可行性约束**：只在有空的时间段排班
|
||||
$$x_{ij} \le v_{ij}, \quad \forall i,j$$
|
||||
|
||||
**班次人数约束：**
|
||||
|
||||
3. **总人数约束**：每班次人数在 $[n_{\min}, n_{\max}]$ 范围内
|
||||
$$n_{\min} \le \sum_{i=1}^{n} x_{ij} \le n_{\max}, \quad \forall j$$
|
||||
|
||||
4. **技术部老人约束**：每班次技术部老人数在范围内
|
||||
$$n_{\text{tech\_old\_min}} \le \sum_{i=1}^{n} x_{ij} \cdot \text{old}_i \cdot \text{tech}_i \le n_{\text{tech\_old\_max}}, \quad \forall j$$
|
||||
|
||||
5. **老人总数约束**：每班次老人总数在范围内
|
||||
$$n_{\text{old\_min}} \le \sum_{i=1}^{n} x_{ij} \cdot \text{old}_i \le n_{\text{old\_max}}, \quad \forall j$$
|
||||
|
||||
6. **小朋友总数约束**：每班次小朋友总数在范围内
|
||||
$$n_{\text{new\_min}} \le \sum_{i=1}^{n} x_{ij} \cdot (1-\text{old}_i) \le n_{\text{new\_max}}, \quad \forall j$$
|
||||
|
||||
### 求解器
|
||||
|
||||
以上完成了整个排班问题的建模。本项目使用 Google OR-Tools 中的 **SCIP 求解器**（Solving Constraint Integer Programs）进行求解。SCIP 是一个强大的混合整数规划（MIP）求解器，特别适合处理这类组合优化问题。
|
||||
|
||||
OR-Tools 支持 `C++`、`Python`、`C#`、`Java` 等多种语言，并提供跨平台支持。在本项目中，我们使用其 Python 接口 `ortools.linear_solver.pywraplp` 进行建模和求解。当找到最优解时，求解器会输出详细的目标值和各个优化指标的统计信息。
|
||||
|
||||
## 维护指南
|
||||
- 如果你想更改 Excel 的读取、写入相关的功能，应该修改 `utils.py` 中的相关函数。
|
||||
- 如果你想更改软件的前端界面，应该修改 `main.py` 中 `MyWidget` 这个类相关的代码。
|
||||
- 如果你想更换排班问题的建模方式、更换求解器、增减限制条件，应该修改 `solve.py` 中的相关代码。
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
from PyInstaller.utils.hooks import collect_data_files, collect_dynamic_libs
|
||||
|
||||
# 收集 ortools 的所有数据文件和动态链接库
|
||||
datas = collect_data_files('ortools')
|
||||
binaries = collect_dynamic_libs('ortools')
|
||||
802
main.py
802
main.py
|
|
@ -1,402 +1,402 @@
|
|||
from sys import exit
|
||||
from PySide6 import QtCore, QtWidgets, QtGui
|
||||
from PySide6.QtGui import QFont
|
||||
from utils import read_excel, save_to_excel
|
||||
from solve import solve_program
|
||||
from datetime import datetime
|
||||
from traceback import format_exc
|
||||
|
||||
# 约束条件配置
|
||||
CONSTRAINTS_CONFIG = [
|
||||
("每班次人数：", "5", "8"),
|
||||
("每班次电脑或电器的老人数：", "", "2"),
|
||||
("每班次老人数：", "1", ""),
|
||||
("每班次小朋友数：", "2", "")
|
||||
]
|
||||
|
||||
# 权重条件配置
|
||||
WEIGHTS_CONFIG = [
|
||||
("目标 1 权重：每班人数平均程度", "1.0"),
|
||||
("目标 2 权重：每班技术部老人", "1.0"),
|
||||
("目标 3 权重：每班技术部小朋友", "1.0"),
|
||||
("目标 4 权重：每班人资部小朋友", "0.5"),
|
||||
("目标 5 权重：每班部门平均程度", "0.5")
|
||||
]
|
||||
|
||||
# 参数调优顺序和尝试值
|
||||
PARAM_CONFIGS = [
|
||||
('num_min', [5, 4, 3, 2, 1]),
|
||||
('num_max', [8, 9, 10, 11, 12, 13, 14, 15]),
|
||||
('num_old_min', [2, 1, 0]),
|
||||
('num_old_max', [3, 4, 5, 6, 7, 8]),
|
||||
('num_new_min', [1, 0]),
|
||||
('num_tech_old_min', [1, 0]),
|
||||
('num_tech_old_max', [1, 2, 3, 4, 5, 6, 7, 8]),
|
||||
]
|
||||
|
||||
# 工作线程类，用于在后台执行求解任务
|
||||
class SolverThread(QtCore.QThread):
|
||||
# 定义信号用于线程与主界面通信
|
||||
log_signal = QtCore.Signal(str) # 发送日志消息
|
||||
finished_signal = QtCore.Signal(object, dict) # 发送求解结果 (vars, params)
|
||||
error_signal = QtCore.Signal(str) # 发送错误消息
|
||||
|
||||
def __init__(self, preference_mat, depart_mat, want_num_array, is_new_array,
|
||||
auto_mode, manual_params=None, parent=None):
|
||||
super().__init__(parent)
|
||||
self.preference_mat = preference_mat
|
||||
self.depart_mat = depart_mat
|
||||
self.want_num_array = want_num_array
|
||||
self.is_new_array = is_new_array
|
||||
self.auto_mode = auto_mode
|
||||
self.manual_params = manual_params or {}
|
||||
|
||||
def run(self):
|
||||
try:
|
||||
vars = None
|
||||
final_params = {}
|
||||
|
||||
if self.auto_mode:
|
||||
self.log_signal.emit("自动调参开始...")
|
||||
|
||||
auto_params = {
|
||||
'num_min': None, 'num_max': None,
|
||||
'num_tech_old_min': None, 'num_tech_old_max': None,
|
||||
'num_old_min': None, 'num_old_max': None,
|
||||
'num_new_min': None, 'num_new_max': None,
|
||||
'weights': self.manual_params.get('weights', [1.0, 1.0, 1.0, 0.5, 0.5])
|
||||
}
|
||||
|
||||
# 逐步优化参数
|
||||
for param_name, try_values in PARAM_CONFIGS:
|
||||
value = None
|
||||
vars = None
|
||||
for try_value in try_values:
|
||||
self.log_signal.emit(f"尝试 {param_name} = {try_value}...")
|
||||
test_params = auto_params.copy()
|
||||
test_params[param_name] = try_value
|
||||
vars = solve_program(
|
||||
preference_mat=self.preference_mat,
|
||||
depart_mat=self.depart_mat,
|
||||
want_num_array=self.want_num_array,
|
||||
is_new_array=self.is_new_array,
|
||||
**test_params
|
||||
)
|
||||
if vars:
|
||||
value = try_value
|
||||
self.log_signal.emit(f"✓ {param_name} = {try_value} 成功")
|
||||
break
|
||||
else:
|
||||
self.log_signal.emit(f"✗ {param_name} = {try_value} 失败")
|
||||
if value:
|
||||
auto_params[param_name] = value
|
||||
else:
|
||||
break
|
||||
|
||||
final_params = auto_params
|
||||
|
||||
else:
|
||||
self.log_signal.emit("计算最优解中...")
|
||||
vars = solve_program(
|
||||
preference_mat=self.preference_mat,
|
||||
want_num_array=self.want_num_array,
|
||||
depart_mat=self.depart_mat,
|
||||
is_new_array=self.is_new_array,
|
||||
**self.manual_params
|
||||
)
|
||||
final_params = self.manual_params
|
||||
|
||||
# 发送完成信号
|
||||
self.finished_signal.emit(vars, final_params)
|
||||
|
||||
except Exception as e:
|
||||
self.error_signal.emit(format_exc())
|
||||
|
||||
class MyWidget(QtWidgets.QWidget):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
self.solver_thread = None # 用于存储求解线程
|
||||
|
||||
self.main_layout = QtWidgets.QVBoxLayout(self)
|
||||
|
||||
bold_font = QFont()
|
||||
bold_font.setBold(True)
|
||||
|
||||
thin_font = QFont()
|
||||
thin_font.setBold(False)
|
||||
|
||||
# 选择文件部分
|
||||
self._excel_dir = None
|
||||
self.group_box_1 = QtWidgets.QGroupBox("Step 1. 选择问卷星导出的表格文件")
|
||||
self.group_box_1.setFont(bold_font)
|
||||
|
||||
self.openfile_layout = QtWidgets.QVBoxLayout()
|
||||
self.button_openfile = QtWidgets.QPushButton("选择文件")
|
||||
self.button_openfile.setFont(thin_font)
|
||||
self.button_openfile.clicked.connect(self.open_file)
|
||||
self.label_openfile = QtWidgets.QLabel("未选择文件")
|
||||
self.label_openfile.setFont(thin_font)
|
||||
|
||||
self.openfile_layout.addWidget(self.button_openfile)
|
||||
self.openfile_layout.addWidget(self.label_openfile)
|
||||
self.group_box_1.setLayout(self.openfile_layout)
|
||||
self.main_layout.addWidget(self.group_box_1)
|
||||
|
||||
# 限制条件部分
|
||||
self.group_box_2 = QtWidgets.QGroupBox("Step 2. 输入限制条件，权重参数均已归一化")
|
||||
self.group_box_2.setFont(bold_font)
|
||||
self.cond_layout_overall = QtWidgets.QVBoxLayout()
|
||||
self.group_box_2.setLayout(self.cond_layout_overall)
|
||||
|
||||
# 创建权重输入框
|
||||
self.weight_widgets = {}
|
||||
|
||||
for i, (label_text, value) in enumerate(WEIGHTS_CONFIG, 1):
|
||||
layout, edit = self._create_weight_row(label_text, value, thin_font)
|
||||
self.weight_widgets[f'layout_{i}'] = layout
|
||||
self.weight_widgets[f'edit_{i}'] = edit
|
||||
self.cond_layout_overall.addLayout(layout)
|
||||
|
||||
# 自动模式开关
|
||||
self._auto_mode = True
|
||||
self.cond_layout_0 = QtWidgets.QVBoxLayout()
|
||||
self.auto_switch = QtWidgets.QCheckBox("自动调参模式")
|
||||
self.auto_switch.setTristate(False) # 禁用三态模式
|
||||
self.auto_switch.setChecked(True)
|
||||
self.auto_switch.stateChanged.connect(self.on_switch_toggled)
|
||||
self.auto_switch.setFont(thin_font)
|
||||
|
||||
self.cond_layout_0.addWidget(self.auto_switch)
|
||||
self.cond_layout_overall.addLayout(self.cond_layout_0)
|
||||
|
||||
# 创建限制条件输入框
|
||||
self.constraint_widgets = {}
|
||||
|
||||
for i, (label_text, min_value, max_value) in enumerate(CONSTRAINTS_CONFIG, 1):
|
||||
layout, min_edit, max_edit = self._create_constraint_row(label_text, min_value, max_value, thin_font)
|
||||
self.constraint_widgets[f'layout_{i}'] = layout
|
||||
self.constraint_widgets[f'min_{i}'] = min_edit
|
||||
self.constraint_widgets[f'max_{i}'] = max_edit
|
||||
self.cond_layout_overall.addLayout(layout)
|
||||
|
||||
self.main_layout.addWidget(self.group_box_2)
|
||||
|
||||
# 处理文件部分
|
||||
self.group_box_3 = QtWidgets.QGroupBox("Step 3. 获取最优结果")
|
||||
self.group_box_3.setFont(bold_font)
|
||||
|
||||
self.button_solve = QtWidgets.QPushButton("开始排班！")
|
||||
self.button_solve.setFont(thin_font)
|
||||
self.text_solve = QtWidgets.QTextEdit(self)
|
||||
self.text_solve.setFont(thin_font)
|
||||
self.text_solve.setReadOnly(True) # 设置为只读模式
|
||||
|
||||
# 创建 QScrollArea 并将 QTextEdit 添加进去
|
||||
self.scroll_area = QtWidgets.QScrollArea(self)
|
||||
self.scroll_area.setWidget(self.text_solve) # 将 QTextEdit 设置为 QScrollArea 的内容
|
||||
self.scroll_area.setWidgetResizable(True) # 允许 QTextEdit 根据 QScrollArea 的大小自动调整
|
||||
|
||||
self.button_solve.clicked.connect(self.magic)
|
||||
|
||||
self.solve_layout = QtWidgets.QVBoxLayout()
|
||||
self.solve_layout.addWidget(self.scroll_area)
|
||||
self.solve_layout.addWidget(self.button_solve)
|
||||
self.group_box_3.setLayout(self.solve_layout)
|
||||
self.main_layout.addWidget(self.group_box_3)
|
||||
|
||||
def _create_constraint_row(self, label_text, min_value, max_value, font):
|
||||
"""创建一行限制条件输入框"""
|
||||
layout = QtWidgets.QHBoxLayout()
|
||||
|
||||
label = QtWidgets.QLabel(label_text, self)
|
||||
label.setFont(font)
|
||||
|
||||
# 创建输入框的辅助函数
|
||||
def create_input(value):
|
||||
edit = QtWidgets.QLineEdit(self)
|
||||
edit.setFont(font)
|
||||
edit.setValidator(QtGui.QIntValidator())
|
||||
edit.setPlaceholderText("无限制")
|
||||
if value:
|
||||
edit.setText(value)
|
||||
edit.setEnabled(False)
|
||||
return edit
|
||||
|
||||
min_edit = create_input(min_value)
|
||||
to_label = QtWidgets.QLabel("到", self)
|
||||
to_label.setFont(font)
|
||||
max_edit = create_input(max_value)
|
||||
|
||||
layout.addWidget(label)
|
||||
layout.addWidget(min_edit)
|
||||
layout.addWidget(to_label)
|
||||
layout.addWidget(max_edit)
|
||||
|
||||
return layout, min_edit, max_edit
|
||||
|
||||
def _create_weight_row(self, label_text, value, font):
|
||||
"""创建一行权重输入框"""
|
||||
layout = QtWidgets.QHBoxLayout()
|
||||
|
||||
label = QtWidgets.QLabel(label_text, self)
|
||||
label.setFont(font)
|
||||
|
||||
edit = QtWidgets.QLineEdit(self)
|
||||
edit.setFont(font)
|
||||
edit.setValidator(QtGui.QDoubleValidator(0.0, 1.0, 2, self))
|
||||
edit.setText(str(value))
|
||||
|
||||
layout.addWidget(label)
|
||||
layout.addWidget(edit)
|
||||
|
||||
return layout, edit
|
||||
|
||||
def open_file(self):
|
||||
file_path, _ = QtWidgets.QFileDialog.getOpenFileName(self, "打开文件", "", "所有文件 (*.*);;文本文件 (*.txt)")
|
||||
if file_path:
|
||||
self.label_openfile.setText(f"选中文件: {file_path}")
|
||||
self._excel_dir = file_path
|
||||
|
||||
def on_switch_toggled(self, state):
|
||||
enabled = state == 0
|
||||
self._auto_mode = not enabled
|
||||
|
||||
for i in range(1, len(CONSTRAINTS_CONFIG) + 1):
|
||||
self.constraint_widgets[f'min_{i}'].setEnabled(enabled)
|
||||
self.constraint_widgets[f'max_{i}'].setEnabled(enabled)
|
||||
|
||||
@QtCore.Slot()
|
||||
def magic(self):
|
||||
try:
|
||||
self.text_solve.append(f"******** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} ********")
|
||||
|
||||
# 读取文件
|
||||
if self._excel_dir is None:
|
||||
self.text_solve.append("请先选择文件！")
|
||||
return
|
||||
|
||||
# 禁用按钮，防止重复点击
|
||||
self.button_solve.setEnabled(False)
|
||||
self.button_solve.setText("计算中...")
|
||||
|
||||
self.text_solve.append("开始排班...")
|
||||
self.text_solve.append("读取文件中...")
|
||||
self.all_data, self.index_to_name_dict, preference_mat, depart_mat, want_num_array, is_new_array = read_excel(self._excel_dir)
|
||||
self.text_solve.append("读取文件成功！")
|
||||
|
||||
# 计算并打印统计信息
|
||||
is_tech_array = [depart_mat[i][0] == 1 or depart_mat[i][1] == 1 for i in range(len(depart_mat))]
|
||||
stu_num = len(self.index_to_name_dict)
|
||||
class_num = len(preference_mat[0])
|
||||
tech_sum = sum(want_num_array[i] for i in range(len(preference_mat))
|
||||
if not is_new_array[i] and is_tech_array[i])
|
||||
all_sum = sum(want_num_array)
|
||||
min_prefer = min(sum(preference_mat[i][j] for i in range(len(preference_mat)))
|
||||
for j in range(class_num))
|
||||
|
||||
self.text_solve.append("信息统计：")
|
||||
self.text_solve.append(f"\t学生总人数：{stu_num}")
|
||||
self.text_solve.append(f"\t班次总数：{class_num}")
|
||||
self.text_solve.append(f"\t电脑部或电器部的所有老人的意愿班次之和：{tech_sum}")
|
||||
self.text_solve.append(f"\t所有人的意愿班次之和：{all_sum}")
|
||||
self.text_solve.append(f"\t平均每班人数：{all_sum/20}")
|
||||
self.text_solve.append(f"\t所有班次中最少拥有意愿数：{min_prefer}")
|
||||
|
||||
def get_weight_value(key):
|
||||
text = self.weight_widgets[key].text()
|
||||
return float(text) if text else 0.0
|
||||
|
||||
def get_constraint_value(key):
|
||||
text = self.constraint_widgets[key].text()
|
||||
return int(text) if text else None
|
||||
|
||||
# 准备参数
|
||||
params = {
|
||||
'num_min': get_constraint_value('min_1'),
|
||||
'num_max': get_constraint_value('max_1'),
|
||||
'num_tech_old_min': get_constraint_value('min_2'),
|
||||
'num_tech_old_max': get_constraint_value('max_2'),
|
||||
'num_old_min': get_constraint_value('min_3'),
|
||||
'num_old_max': get_constraint_value('max_3'),
|
||||
'num_new_min': get_constraint_value('min_4'),
|
||||
'num_new_max': get_constraint_value('max_4'),
|
||||
'weights': [get_weight_value(f'edit_{i}') for i in range(1, len(WEIGHTS_CONFIG) + 1)]
|
||||
}
|
||||
|
||||
# 创建并启动求解线程
|
||||
self.solver_thread = SolverThread(
|
||||
preference_mat, depart_mat, want_num_array, is_new_array,
|
||||
self._auto_mode, params, self
|
||||
)
|
||||
|
||||
# 连接信号
|
||||
self.solver_thread.log_signal.connect(self.on_log_message)
|
||||
self.solver_thread.finished_signal.connect(self.on_solve_finished)
|
||||
self.solver_thread.error_signal.connect(self.on_solve_error)
|
||||
|
||||
# 启动线程
|
||||
self.solver_thread.start()
|
||||
|
||||
except Exception:
|
||||
self.text_solve.append("程序出现严重错误，请联系开发者解决问题！！！")
|
||||
self.text_solve.append(f"Error Details:\n{format_exc()}")
|
||||
self.button_solve.setEnabled(True)
|
||||
self.button_solve.setText("开始排班！")
|
||||
|
||||
@QtCore.Slot(str)
|
||||
def on_log_message(self, message):
|
||||
"""接收线程发送的日志消息并实时显示"""
|
||||
self.text_solve.append(message)
|
||||
|
||||
@QtCore.Slot(object, dict)
|
||||
def on_solve_finished(self, vars, params):
|
||||
"""求解完成后的回调"""
|
||||
try:
|
||||
if vars is None:
|
||||
if self._auto_mode:
|
||||
self.text_solve.append("自动求解失败！请尝试手动调参！")
|
||||
else:
|
||||
self.text_solve.append("在目前限制条件下无解！请尝试更改限制条件！")
|
||||
else:
|
||||
if self._auto_mode:
|
||||
self.text_solve.append("自动计算成功！")
|
||||
self.text_solve.append("参数：")
|
||||
self.text_solve.append(f"\t每班人数：[{params['num_min']}, {params['num_max']}]")
|
||||
self.text_solve.append(f"\t每班电脑或电器的老人数：[{params['num_tech_old_min']}, {params['num_tech_old_max']}]")
|
||||
self.text_solve.append(f"\t每班老人数：[{params['num_old_min']}, {params['num_old_max']}]")
|
||||
self.text_solve.append(f"\t每班小朋友数：[{params['num_new_min']}, inf]")
|
||||
else:
|
||||
self.text_solve.append("计算最优解成功！")
|
||||
|
||||
# 保存结果到 excel
|
||||
self.text_solve.append("保存结果中...")
|
||||
save_dir = f"result_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx"
|
||||
save_to_excel(vars, self.all_data, self.index_to_name_dict, save_dir)
|
||||
self.text_solve.append(f"保存结果成功！保存路径：{save_dir}")
|
||||
|
||||
except Exception:
|
||||
self.text_solve.append("保存结果时出现错误！")
|
||||
self.text_solve.append(f"Error Details:\n{format_exc()}")
|
||||
finally:
|
||||
# 恢复按钮状态
|
||||
self.button_solve.setEnabled(True)
|
||||
self.button_solve.setText("开始排班！")
|
||||
|
||||
@QtCore.Slot(str)
|
||||
def on_solve_error(self, error_msg):
|
||||
"""处理求解过程中的错误"""
|
||||
self.text_solve.append("程序出现严重错误，请联系开发者解决问题！！！")
|
||||
self.text_solve.append(f"Error Details:\n{error_msg}")
|
||||
self.button_solve.setEnabled(True)
|
||||
self.button_solve.setText("开始排班！")
|
||||
|
||||
if __name__ == "__main__":
|
||||
app = QtWidgets.QApplication([])
|
||||
|
||||
widget = MyWidget()
|
||||
widget.setWindowTitle("EVA 值班排班软件")
|
||||
widget.resize(600, 600)
|
||||
widget.show()
|
||||
|
||||
from sys import exit
|
||||
from PySide6 import QtCore, QtWidgets, QtGui
|
||||
from PySide6.QtGui import QFont
|
||||
from utils import read_excel, save_to_excel
|
||||
from solve import solve_program
|
||||
from datetime import datetime
|
||||
from traceback import format_exc
|
||||
|
||||
# 约束条件配置
|
||||
CONSTRAINTS_CONFIG = [
|
||||
("每班次人数：", "5", "8"),
|
||||
("每班次电脑或电器的老人数：", "", "2"),
|
||||
("每班次老人数：", "1", ""),
|
||||
("每班次小朋友数：", "2", "")
|
||||
]
|
||||
|
||||
# 权重条件配置
|
||||
WEIGHTS_CONFIG = [
|
||||
("目标 1 权重：每班人数平均程度", "1.0"),
|
||||
("目标 2 权重：每班技术部老人", "1.0"),
|
||||
("目标 3 权重：每班技术部小朋友", "1.0"),
|
||||
("目标 4 权重：每班人资部小朋友", "0.5"),
|
||||
("目标 5 权重：每班部门平均程度", "0.5")
|
||||
]
|
||||
|
||||
# 参数调优顺序和尝试值
|
||||
PARAM_CONFIGS = [
|
||||
('num_min', [5, 4, 3, 2, 1]),
|
||||
('num_max', [8, 9, 10, 11, 12, 13, 14, 15]),
|
||||
('num_old_min', [2, 1, 0]),
|
||||
('num_old_max', [3, 4, 5, 6, 7, 8]),
|
||||
('num_new_min', [1, 0]),
|
||||
('num_tech_old_min', [1, 0]),
|
||||
('num_tech_old_max', [1, 2, 3, 4, 5, 6, 7, 8]),
|
||||
]
|
||||
|
||||
# 工作线程类，用于在后台执行求解任务
|
||||
class SolverThread(QtCore.QThread):
|
||||
# 定义信号用于线程与主界面通信
|
||||
log_signal = QtCore.Signal(str) # 发送日志消息
|
||||
finished_signal = QtCore.Signal(object, dict) # 发送求解结果 (vars, params)
|
||||
error_signal = QtCore.Signal(str) # 发送错误消息
|
||||
|
||||
def __init__(self, preference_mat, depart_mat, want_num_array, is_new_array,
|
||||
auto_mode, manual_params=None, parent=None):
|
||||
super().__init__(parent)
|
||||
self.preference_mat = preference_mat
|
||||
self.depart_mat = depart_mat
|
||||
self.want_num_array = want_num_array
|
||||
self.is_new_array = is_new_array
|
||||
self.auto_mode = auto_mode
|
||||
self.manual_params = manual_params or {}
|
||||
|
||||
def run(self):
|
||||
try:
|
||||
vars = None
|
||||
final_params = {}
|
||||
|
||||
if self.auto_mode:
|
||||
self.log_signal.emit("自动调参开始...")
|
||||
|
||||
auto_params = {
|
||||
'num_min': None, 'num_max': None,
|
||||
'num_tech_old_min': None, 'num_tech_old_max': None,
|
||||
'num_old_min': None, 'num_old_max': None,
|
||||
'num_new_min': None, 'num_new_max': None,
|
||||
'weights': self.manual_params.get('weights', [1.0, 1.0, 1.0, 0.5, 0.5])
|
||||
}
|
||||
|
||||
# 逐步优化参数
|
||||
for param_name, try_values in PARAM_CONFIGS:
|
||||
value = None
|
||||
vars = None
|
||||
for try_value in try_values:
|
||||
self.log_signal.emit(f"尝试 {param_name} = {try_value}...")
|
||||
test_params = auto_params.copy()
|
||||
test_params[param_name] = try_value
|
||||
vars = solve_program(
|
||||
preference_mat=self.preference_mat,
|
||||
depart_mat=self.depart_mat,
|
||||
want_num_array=self.want_num_array,
|
||||
is_new_array=self.is_new_array,
|
||||
**test_params
|
||||
)
|
||||
if vars:
|
||||
value = try_value
|
||||
self.log_signal.emit(f"✓ {param_name} = {try_value} 成功")
|
||||
break
|
||||
else:
|
||||
self.log_signal.emit(f"✗ {param_name} = {try_value} 失败")
|
||||
if value:
|
||||
auto_params[param_name] = value
|
||||
else:
|
||||
break
|
||||
|
||||
final_params = auto_params
|
||||
|
||||
else:
|
||||
self.log_signal.emit("计算最优解中...")
|
||||
vars = solve_program(
|
||||
preference_mat=self.preference_mat,
|
||||
want_num_array=self.want_num_array,
|
||||
depart_mat=self.depart_mat,
|
||||
is_new_array=self.is_new_array,
|
||||
**self.manual_params
|
||||
)
|
||||
final_params = self.manual_params
|
||||
|
||||
# 发送完成信号
|
||||
self.finished_signal.emit(vars, final_params)
|
||||
|
||||
except Exception as e:
|
||||
self.error_signal.emit(format_exc())
|
||||
|
||||
class MyWidget(QtWidgets.QWidget):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
self.solver_thread = None # 用于存储求解线程
|
||||
|
||||
self.main_layout = QtWidgets.QVBoxLayout(self)
|
||||
|
||||
bold_font = QFont()
|
||||
bold_font.setBold(True)
|
||||
|
||||
thin_font = QFont()
|
||||
thin_font.setBold(False)
|
||||
|
||||
# 选择文件部分
|
||||
self._excel_dir = None
|
||||
self.group_box_1 = QtWidgets.QGroupBox("Step 1. 选择问卷星导出的表格文件")
|
||||
self.group_box_1.setFont(bold_font)
|
||||
|
||||
self.openfile_layout = QtWidgets.QVBoxLayout()
|
||||
self.button_openfile = QtWidgets.QPushButton("选择文件")
|
||||
self.button_openfile.setFont(thin_font)
|
||||
self.button_openfile.clicked.connect(self.open_file)
|
||||
self.label_openfile = QtWidgets.QLabel("未选择文件")
|
||||
self.label_openfile.setFont(thin_font)
|
||||
|
||||
self.openfile_layout.addWidget(self.button_openfile)
|
||||
self.openfile_layout.addWidget(self.label_openfile)
|
||||
self.group_box_1.setLayout(self.openfile_layout)
|
||||
self.main_layout.addWidget(self.group_box_1)
|
||||
|
||||
# 限制条件部分
|
||||
self.group_box_2 = QtWidgets.QGroupBox("Step 2. 输入限制条件，权重参数均已归一化")
|
||||
self.group_box_2.setFont(bold_font)
|
||||
self.cond_layout_overall = QtWidgets.QVBoxLayout()
|
||||
self.group_box_2.setLayout(self.cond_layout_overall)
|
||||
|
||||
# 创建权重输入框
|
||||
self.weight_widgets = {}
|
||||
|
||||
for i, (label_text, value) in enumerate(WEIGHTS_CONFIG, 1):
|
||||
layout, edit = self._create_weight_row(label_text, value, thin_font)
|
||||
self.weight_widgets[f'layout_{i}'] = layout
|
||||
self.weight_widgets[f'edit_{i}'] = edit
|
||||
self.cond_layout_overall.addLayout(layout)
|
||||
|
||||
# 自动模式开关
|
||||
self._auto_mode = True
|
||||
self.cond_layout_0 = QtWidgets.QVBoxLayout()
|
||||
self.auto_switch = QtWidgets.QCheckBox("自动调参模式")
|
||||
self.auto_switch.setTristate(False) # 禁用三态模式
|
||||
self.auto_switch.setChecked(True)
|
||||
self.auto_switch.stateChanged.connect(self.on_switch_toggled)
|
||||
self.auto_switch.setFont(thin_font)
|
||||
|
||||
self.cond_layout_0.addWidget(self.auto_switch)
|
||||
self.cond_layout_overall.addLayout(self.cond_layout_0)
|
||||
|
||||
# 创建限制条件输入框
|
||||
self.constraint_widgets = {}
|
||||
|
||||
for i, (label_text, min_value, max_value) in enumerate(CONSTRAINTS_CONFIG, 1):
|
||||
layout, min_edit, max_edit = self._create_constraint_row(label_text, min_value, max_value, thin_font)
|
||||
self.constraint_widgets[f'layout_{i}'] = layout
|
||||
self.constraint_widgets[f'min_{i}'] = min_edit
|
||||
self.constraint_widgets[f'max_{i}'] = max_edit
|
||||
self.cond_layout_overall.addLayout(layout)
|
||||
|
||||
self.main_layout.addWidget(self.group_box_2)
|
||||
|
||||
# 处理文件部分
|
||||
self.group_box_3 = QtWidgets.QGroupBox("Step 3. 获取最优结果")
|
||||
self.group_box_3.setFont(bold_font)
|
||||
|
||||
self.button_solve = QtWidgets.QPushButton("开始排班！")
|
||||
self.button_solve.setFont(thin_font)
|
||||
self.text_solve = QtWidgets.QTextEdit(self)
|
||||
self.text_solve.setFont(thin_font)
|
||||
self.text_solve.setReadOnly(True) # 设置为只读模式
|
||||
|
||||
# 创建 QScrollArea 并将 QTextEdit 添加进去
|
||||
self.scroll_area = QtWidgets.QScrollArea(self)
|
||||
self.scroll_area.setWidget(self.text_solve) # 将 QTextEdit 设置为 QScrollArea 的内容
|
||||
self.scroll_area.setWidgetResizable(True) # 允许 QTextEdit 根据 QScrollArea 的大小自动调整
|
||||
|
||||
self.button_solve.clicked.connect(self.magic)
|
||||
|
||||
self.solve_layout = QtWidgets.QVBoxLayout()
|
||||
self.solve_layout.addWidget(self.scroll_area)
|
||||
self.solve_layout.addWidget(self.button_solve)
|
||||
self.group_box_3.setLayout(self.solve_layout)
|
||||
self.main_layout.addWidget(self.group_box_3)
|
||||
|
||||
def _create_constraint_row(self, label_text, min_value, max_value, font):
|
||||
"""创建一行限制条件输入框"""
|
||||
layout = QtWidgets.QHBoxLayout()
|
||||
|
||||
label = QtWidgets.QLabel(label_text, self)
|
||||
label.setFont(font)
|
||||
|
||||
# 创建输入框的辅助函数
|
||||
def create_input(value):
|
||||
edit = QtWidgets.QLineEdit(self)
|
||||
edit.setFont(font)
|
||||
edit.setValidator(QtGui.QIntValidator())
|
||||
edit.setPlaceholderText("无限制")
|
||||
if value:
|
||||
edit.setText(value)
|
||||
edit.setEnabled(False)
|
||||
return edit
|
||||
|
||||
min_edit = create_input(min_value)
|
||||
to_label = QtWidgets.QLabel("到", self)
|
||||
to_label.setFont(font)
|
||||
max_edit = create_input(max_value)
|
||||
|
||||
layout.addWidget(label)
|
||||
layout.addWidget(min_edit)
|
||||
layout.addWidget(to_label)
|
||||
layout.addWidget(max_edit)
|
||||
|
||||
return layout, min_edit, max_edit
|
||||
|
||||
def _create_weight_row(self, label_text, value, font):
|
||||
"""创建一行权重输入框"""
|
||||
layout = QtWidgets.QHBoxLayout()
|
||||
|
||||
label = QtWidgets.QLabel(label_text, self)
|
||||
label.setFont(font)
|
||||
|
||||
edit = QtWidgets.QLineEdit(self)
|
||||
edit.setFont(font)
|
||||
edit.setValidator(QtGui.QDoubleValidator(0.0, 1.0, 2, self))
|
||||
edit.setText(str(value))
|
||||
|
||||
layout.addWidget(label)
|
||||
layout.addWidget(edit)
|
||||
|
||||
return layout, edit
|
||||
|
||||
def open_file(self):
|
||||
file_path, _ = QtWidgets.QFileDialog.getOpenFileName(self, "打开文件", "", "所有文件 (*.*);;文本文件 (*.txt)")
|
||||
if file_path:
|
||||
self.label_openfile.setText(f"选中文件: {file_path}")
|
||||
self._excel_dir = file_path
|
||||
|
||||
def on_switch_toggled(self, state):
|
||||
enabled = state == 0
|
||||
self._auto_mode = not enabled
|
||||
|
||||
for i in range(1, len(CONSTRAINTS_CONFIG) + 1):
|
||||
self.constraint_widgets[f'min_{i}'].setEnabled(enabled)
|
||||
self.constraint_widgets[f'max_{i}'].setEnabled(enabled)
|
||||
|
||||
@QtCore.Slot()
|
||||
def magic(self):
|
||||
try:
|
||||
self.text_solve.append(f"******** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} ********")
|
||||
|
||||
# 读取文件
|
||||
if self._excel_dir is None:
|
||||
self.text_solve.append("请先选择文件！")
|
||||
return
|
||||
|
||||
# 禁用按钮，防止重复点击
|
||||
self.button_solve.setEnabled(False)
|
||||
self.button_solve.setText("计算中...")
|
||||
|
||||
self.text_solve.append("开始排班...")
|
||||
self.text_solve.append("读取文件中...")
|
||||
self.all_data, self.index_to_name_dict, preference_mat, depart_mat, want_num_array, is_new_array = read_excel(self._excel_dir)
|
||||
self.text_solve.append("读取文件成功！")
|
||||
|
||||
# 计算并打印统计信息
|
||||
is_tech_array = [depart_mat[i][0] == 1 or depart_mat[i][1] == 1 for i in range(len(depart_mat))]
|
||||
stu_num = len(self.index_to_name_dict)
|
||||
class_num = len(preference_mat[0])
|
||||
tech_sum = sum(want_num_array[i] for i in range(len(preference_mat))
|
||||
if not is_new_array[i] and is_tech_array[i])
|
||||
all_sum = sum(want_num_array)
|
||||
min_prefer = min(sum(preference_mat[i][j] for i in range(len(preference_mat)))
|
||||
for j in range(class_num))
|
||||
|
||||
self.text_solve.append("信息统计：")
|
||||
self.text_solve.append(f"\t学生总人数：{stu_num}")
|
||||
self.text_solve.append(f"\t班次总数：{class_num}")
|
||||
self.text_solve.append(f"\t电脑部或电器部的所有老人的意愿班次之和：{tech_sum}")
|
||||
self.text_solve.append(f"\t所有人的意愿班次之和：{all_sum}")
|
||||
self.text_solve.append(f"\t平均每班人数：{all_sum/20}")
|
||||
self.text_solve.append(f"\t所有班次中最少拥有意愿数：{min_prefer}")
|
||||
|
||||
def get_weight_value(key):
|
||||
text = self.weight_widgets[key].text()
|
||||
return float(text) if text else 0.0
|
||||
|
||||
def get_constraint_value(key):
|
||||
text = self.constraint_widgets[key].text()
|
||||
return int(text) if text else None
|
||||
|
||||
# 准备参数
|
||||
params = {
|
||||
'num_min': get_constraint_value('min_1'),
|
||||
'num_max': get_constraint_value('max_1'),
|
||||
'num_tech_old_min': get_constraint_value('min_2'),
|
||||
'num_tech_old_max': get_constraint_value('max_2'),
|
||||
'num_old_min': get_constraint_value('min_3'),
|
||||
'num_old_max': get_constraint_value('max_3'),
|
||||
'num_new_min': get_constraint_value('min_4'),
|
||||
'num_new_max': get_constraint_value('max_4'),
|
||||
'weights': [get_weight_value(f'edit_{i}') for i in range(1, len(WEIGHTS_CONFIG) + 1)]
|
||||
}
|
||||
|
||||
# 创建并启动求解线程
|
||||
self.solver_thread = SolverThread(
|
||||
preference_mat, depart_mat, want_num_array, is_new_array,
|
||||
self._auto_mode, params, self
|
||||
)
|
||||
|
||||
# 连接信号
|
||||
self.solver_thread.log_signal.connect(self.on_log_message)
|
||||
self.solver_thread.finished_signal.connect(self.on_solve_finished)
|
||||
self.solver_thread.error_signal.connect(self.on_solve_error)
|
||||
|
||||
# 启动线程
|
||||
self.solver_thread.start()
|
||||
|
||||
except Exception:
|
||||
self.text_solve.append("程序出现严重错误，请联系开发者解决问题！！！")
|
||||
self.text_solve.append(f"Error Details:\n{format_exc()}")
|
||||
self.button_solve.setEnabled(True)
|
||||
self.button_solve.setText("开始排班！")
|
||||
|
||||
@QtCore.Slot(str)
|
||||
def on_log_message(self, message):
|
||||
"""接收线程发送的日志消息并实时显示"""
|
||||
self.text_solve.append(message)
|
||||
|
||||
@QtCore.Slot(object, dict)
|
||||
def on_solve_finished(self, vars, params):
|
||||
"""求解完成后的回调"""
|
||||
try:
|
||||
if vars is None:
|
||||
if self._auto_mode:
|
||||
self.text_solve.append("自动求解失败！请尝试手动调参！")
|
||||
else:
|
||||
self.text_solve.append("在目前限制条件下无解！请尝试更改限制条件！")
|
||||
else:
|
||||
if self._auto_mode:
|
||||
self.text_solve.append("自动计算成功！")
|
||||
self.text_solve.append("参数：")
|
||||
self.text_solve.append(f"\t每班人数：[{params['num_min']}, {params['num_max']}]")
|
||||
self.text_solve.append(f"\t每班电脑或电器的老人数：[{params['num_tech_old_min']}, {params['num_tech_old_max']}]")
|
||||
self.text_solve.append(f"\t每班老人数：[{params['num_old_min']}, {params['num_old_max']}]")
|
||||
self.text_solve.append(f"\t每班小朋友数：[{params['num_new_min']}, inf]")
|
||||
else:
|
||||
self.text_solve.append("计算最优解成功！")
|
||||
|
||||
# 保存结果到 excel
|
||||
self.text_solve.append("保存结果中...")
|
||||
save_dir = f"result_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx"
|
||||
save_to_excel(vars, self.all_data, self.index_to_name_dict, save_dir)
|
||||
self.text_solve.append(f"保存结果成功！保存路径：{save_dir}")
|
||||
|
||||
except Exception:
|
||||
self.text_solve.append("保存结果时出现错误！")
|
||||
self.text_solve.append(f"Error Details:\n{format_exc()}")
|
||||
finally:
|
||||
# 恢复按钮状态
|
||||
self.button_solve.setEnabled(True)
|
||||
self.button_solve.setText("开始排班！")
|
||||
|
||||
@QtCore.Slot(str)
|
||||
def on_solve_error(self, error_msg):
|
||||
"""处理求解过程中的错误"""
|
||||
self.text_solve.append("程序出现严重错误，请联系开发者解决问题！！！")
|
||||
self.text_solve.append(f"Error Details:\n{error_msg}")
|
||||
self.button_solve.setEnabled(True)
|
||||
self.button_solve.setText("开始排班！")
|
||||
|
||||
if __name__ == "__main__":
|
||||
app = QtWidgets.QApplication([])
|
||||
|
||||
widget = MyWidget()
|
||||
widget.setWindowTitle("EVA 值班排班软件")
|
||||
widget.resize(600, 600)
|
||||
widget.show()
|
||||
|
||||
exit(app.exec())
|
||||
|
|
@ -1,13 +1,13 @@
|
|||
[project]
|
||||
name = "eva-duty-arrange-tool"
|
||||
version = "2.0.0"
|
||||
description = "EVA 值班排班工具"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.12"
|
||||
dependencies = [
|
||||
"openpyxl>=3.1.5",
|
||||
"ortools>=9.14.6206",
|
||||
"pandas>=2.3.3",
|
||||
"pyinstaller>=6.16.0",
|
||||
"pyside6>=6.10.0",
|
||||
]
|
||||
[project]
|
||||
name = "eva-duty-arrange-tool"
|
||||
version = "2.0.0"
|
||||
description = "EVA 值班排班工具"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.12"
|
||||
dependencies = [
|
||||
"openpyxl>=3.1.5",
|
||||
"ortools>=9.14.6206",
|
||||
"pandas>=2.3.3",
|
||||
"pyinstaller>=6.16.0",
|
||||
"pyside6>=6.10.0",
|
||||
]
|
||||
|
|
|
|||
418
solve.py
418
solve.py
|
|
@ -1,210 +1,210 @@
|
|||
from ortools.linear_solver import pywraplp
|
||||
|
||||
def solve_program(preference_mat:list, depart_mat:list, want_num_array:list, is_new_array:list,
|
||||
num_min=None, num_max=None,
|
||||
num_tech_old_min=None, num_tech_old_max=None,
|
||||
num_old_min=None, num_old_max=None,
|
||||
num_new_min=None, num_new_max=None,
|
||||
weights=[1.0, 1.0, 1.0, 0.5, 0.5]
|
||||
):
|
||||
|
||||
is_old_array = [not is_new for is_new in is_new_array]
|
||||
is_tech_array = [depart_mat[i][0] == 1 or depart_mat[i][1] == 1 for i in range(len(depart_mat))]
|
||||
is_hr_array = [depart_mat[i][2] == 1 for i in range(len(depart_mat))]
|
||||
|
||||
# 使用 SCIP 求解器求解组合优化问题
|
||||
solver = pywraplp.Solver.CreateSolver("SCIP")
|
||||
if not solver:
|
||||
return
|
||||
|
||||
N = len(preference_mat) # 学生人数
|
||||
M = len(preference_mat[0]) # 班次数
|
||||
avg_num = sum(want_num_array) / M
|
||||
print(f"平均人数：{avg_num}")
|
||||
|
||||
# 各个目标的期望值/归一化系数
|
||||
m = [2.5, 3, 4, 1, 8] # [班次人数偏差, 技术部老人, 技术部小朋友, 人资部小朋友, 部门均衡]
|
||||
|
||||
# 创建决策变量：variables[i][j] 表示第 i 个学生是否选择第 j 个班次
|
||||
variables = [[solver.IntVar(0.0, 1.0, f"choice_{i}_{j}")
|
||||
for j in range(M)] for i in range(N)]
|
||||
|
||||
# 创建辅助变量用于优化目标1（均衡班次人数）
|
||||
aux_vars_x1 = [solver.NumVar(0.0, solver.infinity(), f"aux_x1_{j}")
|
||||
for j in range(M)]
|
||||
|
||||
# 创建辅助变量用于优化目标2（每班次技术部老人数量缺口）
|
||||
aux_vars_x2 = [solver.NumVar(0.0, solver.infinity(), f"aux_x2_{j}")
|
||||
for j in range(M)]
|
||||
|
||||
# 创建辅助变量用于优化目标3（每班次技术部小朋友数量缺口）
|
||||
aux_vars_x3 = [solver.NumVar(0.0, solver.infinity(), f"aux_x3_{j}")
|
||||
for j in range(M)]
|
||||
|
||||
# 创建辅助变量用于优化目标4（每班次人资部小朋友数量缺口）
|
||||
aux_vars_x4 = [solver.NumVar(0.0, solver.infinity(), f"aux_x4_{j}")
|
||||
for j in range(M)]
|
||||
|
||||
# 创建辅助变量用于优化目标5（均衡各部门人数）
|
||||
# 如果提供了部门信息，则创建相应的辅助变量
|
||||
aux_vars_x5 = []
|
||||
num_departments = 5 # 电脑部、电器部、人资部、财外部、文宣部
|
||||
# 为每个班次的每个部门创建辅助变量
|
||||
aux_vars_x5 = [[solver.NumVar(0.0, solver.infinity(), f"aux_x5_{j}_{k}")
|
||||
for k in range(num_departments)] for j in range(M)]
|
||||
|
||||
print("Number of variables =", solver.NumVariables())
|
||||
|
||||
# 约束：每个同学意愿一定要满足
|
||||
for i in range(N):
|
||||
for j in range(M):
|
||||
solver.Add(variables[i][j] <= preference_mat[i][j])
|
||||
|
||||
# 约束：辅助变量 X1 用于计算与平均人数的偏差（绝对值）
|
||||
for j in range(M):
|
||||
actual_num = sum(variables[i][j] for i in range(N))
|
||||
solver.Add(aux_vars_x1[j] >= actual_num - avg_num)
|
||||
solver.Add(aux_vars_x1[j] >= avg_num - actual_num)
|
||||
|
||||
# 约束：辅助变量 X2 用于计算每班次技术部老人数量超出期望值的部分
|
||||
# 辅助变量表示超出期望值(期望每班有m2个技术部老人,超过即计入)
|
||||
# 目标是让尽可能多的班次达到但不超过这个期望值
|
||||
tech_old_target = m[1] # 使用归一化系数作为期望值
|
||||
for j in range(M):
|
||||
tech_old_count = sum(variables[i][j] * is_old_array[i] * is_tech_array[i] for i in range(N))
|
||||
# 辅助变量捕获超出部分和不足部分
|
||||
solver.Add(aux_vars_x2[j] >= tech_old_count - tech_old_target)
|
||||
solver.Add(aux_vars_x2[j] >= tech_old_target - tech_old_count)
|
||||
|
||||
# 约束：辅助变量 X3 用于计算每班次技术部小朋友数量超出期望值的部分
|
||||
tech_new_target = m[2] # 使用归一化系数作为期望值
|
||||
for j in range(M):
|
||||
tech_new_count = sum(variables[i][j] * is_new_array[i] * is_tech_array[i] for i in range(N))
|
||||
solver.Add(aux_vars_x3[j] >= tech_new_count - tech_new_target)
|
||||
solver.Add(aux_vars_x3[j] >= tech_new_target - tech_new_count)
|
||||
|
||||
# 约束：辅助变量 X4 用于计算每班次人资部小朋友数量超出期望值的部分
|
||||
hr_new_target = m[3] # 使用归一化系数作为期望值
|
||||
for j in range(M):
|
||||
hr_new_count = sum(variables[i][j] * is_new_array[i] * is_hr_array[i] for i in range(N))
|
||||
solver.Add(aux_vars_x4[j] >= hr_new_count - hr_new_target)
|
||||
solver.Add(aux_vars_x4[j] >= hr_new_target - hr_new_count)
|
||||
|
||||
# 约束：辅助变量 X5 用于计算各部门人数与平均值的偏差（绝对值）
|
||||
if aux_vars_x5 is not None:
|
||||
for j in range(M):
|
||||
# 计算第 j 班次的总人数
|
||||
shift_total = sum(variables[i][j] for i in range(N))
|
||||
# 计算平均每个部门应有的人数
|
||||
avg_dept = shift_total / num_departments
|
||||
|
||||
# 对每个部门 k
|
||||
for k in range(num_departments):
|
||||
# 计算第 j 班次中第 k 个部门的实际人数
|
||||
dept_count = sum(variables[i][j] * depart_mat[i][k] for i in range(N))
|
||||
# 添加绝对值约束
|
||||
solver.Add(aux_vars_x5[j][k] >= dept_count - avg_dept)
|
||||
solver.Add(aux_vars_x5[j][k] >= avg_dept - dept_count)
|
||||
|
||||
# 约束：满足每位同学的意愿班次数
|
||||
for i in range(N):
|
||||
total_shifts = sum(variables[i])
|
||||
if want_num_array[i] == 3:
|
||||
solver.Add(total_shifts >= 2)
|
||||
solver.Add(total_shifts <= 3)
|
||||
else:
|
||||
solver.Add(total_shifts == want_num_array[i])
|
||||
|
||||
# 添加约束的辅助函数
|
||||
def add_shift_constraint(array, min_val, max_val):
|
||||
for j in range(M):
|
||||
shift_count = sum(variables[i][j] * array[i] for i in range(N))
|
||||
if min_val is not None:
|
||||
solver.Add(shift_count >= min_val)
|
||||
if max_val is not None:
|
||||
solver.Add(shift_count <= max_val)
|
||||
|
||||
# 约束：每个班次的总人数
|
||||
add_shift_constraint([1] * N, num_min, num_max)
|
||||
|
||||
# 约束：每班次电脑或电器的老人数量
|
||||
tech_old_array = [is_old_array[i] * is_tech_array[i] for i in range(N)]
|
||||
add_shift_constraint(tech_old_array, num_tech_old_min, num_tech_old_max)
|
||||
|
||||
# 约束：每班次老人数量
|
||||
add_shift_constraint(is_old_array, num_old_min, num_old_max)
|
||||
|
||||
# 约束：每班次小朋友数量
|
||||
add_shift_constraint(is_new_array, num_new_min, num_new_max)
|
||||
|
||||
# 构建多目标优化函数
|
||||
m1, m2, m3, m4, m5 = m[0], m[1], m[2], m[3], m[4]
|
||||
|
||||
# X1: 每班次人数与平均人数的偏差
|
||||
x1 = sum(aux_vars_x1)
|
||||
obj_x1 = x1 / (m1 * M) # 归一化处理，防止数值过大
|
||||
|
||||
# X2: 每班次技术部老人数量的缺口总和（优化目标是让每班都达到目标值）
|
||||
x2 = sum(aux_vars_x2)
|
||||
obj_x2 = x2 / (m2 * M) # 归一化处理，缺口越小越好
|
||||
|
||||
# X3: 每班次技术部小朋友数量的缺口总和
|
||||
x3 = sum(aux_vars_x3)
|
||||
obj_x3 = x3 / (m3 * M) # 归一化处理，缺口越小越好
|
||||
|
||||
# X4: 每班次人资部小朋友数量的缺口总和
|
||||
x4 = sum(aux_vars_x4)
|
||||
obj_x4 = x4 / (m4 * M) # 归一化处理，缺口越小越好
|
||||
|
||||
# X5: 每班次各部门人数的平均程度
|
||||
obj_x5 = 0
|
||||
if aux_vars_x5:
|
||||
x5 = sum(sum(aux_vars_x5[j]) for j in range(M))
|
||||
obj_x5 = x5 / (m5 * M) # 归一化处理
|
||||
|
||||
# 线性加权组合所有目标
|
||||
total_objective = (
|
||||
weights[0] * obj_x1 + weights[1] * obj_x2 + weights[2] * obj_x3 + weights[3] * obj_x4 + weights[4] * obj_x5
|
||||
)
|
||||
|
||||
print(f"优化目标权重: X1={weights[0]}, X2={weights[1]}, X3={weights[2]}, X4={weights[3]}, X5={weights[4]}")
|
||||
solver.Minimize(total_objective)
|
||||
|
||||
# 求解
|
||||
status = solver.Solve()
|
||||
|
||||
if status == pywraplp.Solver.OPTIMAL:
|
||||
print("Optimal solution found:")
|
||||
variables_return = [[variables[i][j].solution_value() for j in range(M)]
|
||||
for i in range(N)]
|
||||
aux_vars_x1_return = sum([aux_vars_x1[j].solution_value() for j in range(M)]) / M
|
||||
|
||||
print(f"Optimized objective value: {solver.Objective().Value()}")
|
||||
print(f"X1 (每班次人数平均偏差): {aux_vars_x1_return}")
|
||||
|
||||
# 输出各个优化目标的详细信息
|
||||
tech_old_array = [is_old_array[i] * is_tech_array[i] for i in range(N)]
|
||||
tech_new_array = [is_new_array[i] * is_tech_array[i] for i in range(N)]
|
||||
hr_new_array = [is_new_array[i] * is_hr_array[i] for i in range(N)]
|
||||
|
||||
x2_value = sum(sum(variables[i][j].solution_value() * tech_old_array[i] for i in range(N)) for j in range(M)) / M
|
||||
x3_value = sum(sum(variables[i][j].solution_value() * tech_new_array[i] for i in range(N)) for j in range(M)) / M
|
||||
x4_value = sum(sum(variables[i][j].solution_value() * hr_new_array[i] for i in range(N)) for j in range(M)) / M
|
||||
|
||||
aux_vars_x2_return = sum([aux_vars_x2[j].solution_value() for j in range(M)]) / M
|
||||
aux_vars_x3_return = sum([aux_vars_x3[j].solution_value() for j in range(M)]) / M
|
||||
aux_vars_x4_return = sum([aux_vars_x4[j].solution_value() for j in range(M)]) / M
|
||||
|
||||
print(f"X2 (每班平均技术部老人): {x2_value}, 缺口平均: {aux_vars_x2_return}")
|
||||
print(f"X3 (每班平均技术部小朋友): {x3_value}, 缺口平均: {aux_vars_x3_return}")
|
||||
print(f"X4 (每班平均人资部小朋友): {x4_value}, 缺口平均: {aux_vars_x4_return}")
|
||||
|
||||
if aux_vars_x5:
|
||||
x5_value = sum(sum(aux_vars_x5[j][k].solution_value() for k in range(num_departments)) for j in range(M))
|
||||
print(f"X5 (部门人数偏差): {x5_value}")
|
||||
|
||||
return variables_return
|
||||
else:
|
||||
print("No solution found.")
|
||||
return None
|
||||
from ortools.linear_solver import pywraplp
|
||||
|
||||
def solve_program(preference_mat:list, depart_mat:list, want_num_array:list, is_new_array:list,
|
||||
num_min=None, num_max=None,
|
||||
num_tech_old_min=None, num_tech_old_max=None,
|
||||
num_old_min=None, num_old_max=None,
|
||||
num_new_min=None, num_new_max=None,
|
||||
weights=[1.0, 1.0, 1.0, 0.5, 0.5]
|
||||
):
|
||||
|
||||
is_old_array = [not is_new for is_new in is_new_array]
|
||||
is_tech_array = [depart_mat[i][0] == 1 or depart_mat[i][1] == 1 for i in range(len(depart_mat))]
|
||||
is_hr_array = [depart_mat[i][2] == 1 for i in range(len(depart_mat))]
|
||||
|
||||
# 使用 SCIP 求解器求解组合优化问题
|
||||
solver = pywraplp.Solver.CreateSolver("SCIP")
|
||||
if not solver:
|
||||
return
|
||||
|
||||
N = len(preference_mat) # 学生人数
|
||||
M = len(preference_mat[0]) # 班次数
|
||||
avg_num = sum(want_num_array) / M
|
||||
print(f"平均人数：{avg_num}")
|
||||
|
||||
# 各个目标的期望值/归一化系数
|
||||
m = [2.5, 3, 4, 1, 8] # [班次人数偏差, 技术部老人, 技术部小朋友, 人资部小朋友, 部门均衡]
|
||||
|
||||
# 创建决策变量：variables[i][j] 表示第 i 个学生是否选择第 j 个班次
|
||||
variables = [[solver.IntVar(0.0, 1.0, f"choice_{i}_{j}")
|
||||
for j in range(M)] for i in range(N)]
|
||||
|
||||
# 创建辅助变量用于优化目标1（均衡班次人数）
|
||||
aux_vars_x1 = [solver.NumVar(0.0, solver.infinity(), f"aux_x1_{j}")
|
||||
for j in range(M)]
|
||||
|
||||
# 创建辅助变量用于优化目标2（每班次技术部老人数量缺口）
|
||||
aux_vars_x2 = [solver.NumVar(0.0, solver.infinity(), f"aux_x2_{j}")
|
||||
for j in range(M)]
|
||||
|
||||
# 创建辅助变量用于优化目标3（每班次技术部小朋友数量缺口）
|
||||
aux_vars_x3 = [solver.NumVar(0.0, solver.infinity(), f"aux_x3_{j}")
|
||||
for j in range(M)]
|
||||
|
||||
# 创建辅助变量用于优化目标4（每班次人资部小朋友数量缺口）
|
||||
aux_vars_x4 = [solver.NumVar(0.0, solver.infinity(), f"aux_x4_{j}")
|
||||
for j in range(M)]
|
||||
|
||||
# 创建辅助变量用于优化目标5（均衡各部门人数）
|
||||
# 如果提供了部门信息，则创建相应的辅助变量
|
||||
aux_vars_x5 = []
|
||||
num_departments = 5 # 电脑部、电器部、人资部、财外部、文宣部
|
||||
# 为每个班次的每个部门创建辅助变量
|
||||
aux_vars_x5 = [[solver.NumVar(0.0, solver.infinity(), f"aux_x5_{j}_{k}")
|
||||
for k in range(num_departments)] for j in range(M)]
|
||||
|
||||
print("Number of variables =", solver.NumVariables())
|
||||
|
||||
# 约束：每个同学意愿一定要满足
|
||||
for i in range(N):
|
||||
for j in range(M):
|
||||
solver.Add(variables[i][j] <= preference_mat[i][j])
|
||||
|
||||
# 约束：辅助变量 X1 用于计算与平均人数的偏差（绝对值）
|
||||
for j in range(M):
|
||||
actual_num = sum(variables[i][j] for i in range(N))
|
||||
solver.Add(aux_vars_x1[j] >= actual_num - avg_num)
|
||||
solver.Add(aux_vars_x1[j] >= avg_num - actual_num)
|
||||
|
||||
# 约束：辅助变量 X2 用于计算每班次技术部老人数量超出期望值的部分
|
||||
# 辅助变量表示超出期望值(期望每班有m2个技术部老人,超过即计入)
|
||||
# 目标是让尽可能多的班次达到但不超过这个期望值
|
||||
tech_old_target = m[1] # 使用归一化系数作为期望值
|
||||
for j in range(M):
|
||||
tech_old_count = sum(variables[i][j] * is_old_array[i] * is_tech_array[i] for i in range(N))
|
||||
# 辅助变量捕获超出部分和不足部分
|
||||
solver.Add(aux_vars_x2[j] >= tech_old_count - tech_old_target)
|
||||
solver.Add(aux_vars_x2[j] >= tech_old_target - tech_old_count)
|
||||
|
||||
# 约束：辅助变量 X3 用于计算每班次技术部小朋友数量超出期望值的部分
|
||||
tech_new_target = m[2] # 使用归一化系数作为期望值
|
||||
for j in range(M):
|
||||
tech_new_count = sum(variables[i][j] * is_new_array[i] * is_tech_array[i] for i in range(N))
|
||||
solver.Add(aux_vars_x3[j] >= tech_new_count - tech_new_target)
|
||||
solver.Add(aux_vars_x3[j] >= tech_new_target - tech_new_count)
|
||||
|
||||
# 约束：辅助变量 X4 用于计算每班次人资部小朋友数量超出期望值的部分
|
||||
hr_new_target = m[3] # 使用归一化系数作为期望值
|
||||
for j in range(M):
|
||||
hr_new_count = sum(variables[i][j] * is_new_array[i] * is_hr_array[i] for i in range(N))
|
||||
solver.Add(aux_vars_x4[j] >= hr_new_count - hr_new_target)
|
||||
solver.Add(aux_vars_x4[j] >= hr_new_target - hr_new_count)
|
||||
|
||||
# 约束：辅助变量 X5 用于计算各部门人数与平均值的偏差（绝对值）
|
||||
if aux_vars_x5 is not None:
|
||||
for j in range(M):
|
||||
# 计算第 j 班次的总人数
|
||||
shift_total = sum(variables[i][j] for i in range(N))
|
||||
# 计算平均每个部门应有的人数
|
||||
avg_dept = shift_total / num_departments
|
||||
|
||||
# 对每个部门 k
|
||||
for k in range(num_departments):
|
||||
# 计算第 j 班次中第 k 个部门的实际人数
|
||||
dept_count = sum(variables[i][j] * depart_mat[i][k] for i in range(N))
|
||||
# 添加绝对值约束
|
||||
solver.Add(aux_vars_x5[j][k] >= dept_count - avg_dept)
|
||||
solver.Add(aux_vars_x5[j][k] >= avg_dept - dept_count)
|
||||
|
||||
# 约束：满足每位同学的意愿班次数
|
||||
for i in range(N):
|
||||
total_shifts = sum(variables[i])
|
||||
if want_num_array[i] == 3:
|
||||
solver.Add(total_shifts >= 2)
|
||||
solver.Add(total_shifts <= 3)
|
||||
else:
|
||||
solver.Add(total_shifts == want_num_array[i])
|
||||
|
||||
# 添加约束的辅助函数
|
||||
def add_shift_constraint(array, min_val, max_val):
|
||||
for j in range(M):
|
||||
shift_count = sum(variables[i][j] * array[i] for i in range(N))
|
||||
if min_val is not None:
|
||||
solver.Add(shift_count >= min_val)
|
||||
if max_val is not None:
|
||||
solver.Add(shift_count <= max_val)
|
||||
|
||||
# 约束：每个班次的总人数
|
||||
add_shift_constraint([1] * N, num_min, num_max)
|
||||
|
||||
# 约束：每班次电脑或电器的老人数量
|
||||
tech_old_array = [is_old_array[i] * is_tech_array[i] for i in range(N)]
|
||||
add_shift_constraint(tech_old_array, num_tech_old_min, num_tech_old_max)
|
||||
|
||||
# 约束：每班次老人数量
|
||||
add_shift_constraint(is_old_array, num_old_min, num_old_max)
|
||||
|
||||
# 约束：每班次小朋友数量
|
||||
add_shift_constraint(is_new_array, num_new_min, num_new_max)
|
||||
|
||||
# 构建多目标优化函数
|
||||
m1, m2, m3, m4, m5 = m[0], m[1], m[2], m[3], m[4]
|
||||
|
||||
# X1: 每班次人数与平均人数的偏差
|
||||
x1 = sum(aux_vars_x1)
|
||||
obj_x1 = x1 / (m1 * M) # 归一化处理，防止数值过大
|
||||
|
||||
# X2: 每班次技术部老人数量的缺口总和（优化目标是让每班都达到目标值）
|
||||
x2 = sum(aux_vars_x2)
|
||||
obj_x2 = x2 / (m2 * M) # 归一化处理，缺口越小越好
|
||||
|
||||
# X3: 每班次技术部小朋友数量的缺口总和
|
||||
x3 = sum(aux_vars_x3)
|
||||
obj_x3 = x3 / (m3 * M) # 归一化处理，缺口越小越好
|
||||
|
||||
# X4: 每班次人资部小朋友数量的缺口总和
|
||||
x4 = sum(aux_vars_x4)
|
||||
obj_x4 = x4 / (m4 * M) # 归一化处理，缺口越小越好
|
||||
|
||||
# X5: 每班次各部门人数的平均程度
|
||||
obj_x5 = 0
|
||||
if aux_vars_x5:
|
||||
x5 = sum(sum(aux_vars_x5[j]) for j in range(M))
|
||||
obj_x5 = x5 / (m5 * M) # 归一化处理
|
||||
|
||||
# 线性加权组合所有目标
|
||||
total_objective = (
|
||||
weights[0] * obj_x1 + weights[1] * obj_x2 + weights[2] * obj_x3 + weights[3] * obj_x4 + weights[4] * obj_x5
|
||||
)
|
||||
|
||||
print(f"优化目标权重: X1={weights[0]}, X2={weights[1]}, X3={weights[2]}, X4={weights[3]}, X5={weights[4]}")
|
||||
solver.Minimize(total_objective)
|
||||
|
||||
# 求解
|
||||
status = solver.Solve()
|
||||
|
||||
if status == pywraplp.Solver.OPTIMAL:
|
||||
print("Optimal solution found:")
|
||||
variables_return = [[variables[i][j].solution_value() for j in range(M)]
|
||||
for i in range(N)]
|
||||
aux_vars_x1_return = sum([aux_vars_x1[j].solution_value() for j in range(M)]) / M
|
||||
|
||||
print(f"Optimized objective value: {solver.Objective().Value()}")
|
||||
print(f"X1 (每班次人数平均偏差): {aux_vars_x1_return}")
|
||||
|
||||
# 输出各个优化目标的详细信息
|
||||
tech_old_array = [is_old_array[i] * is_tech_array[i] for i in range(N)]
|
||||
tech_new_array = [is_new_array[i] * is_tech_array[i] for i in range(N)]
|
||||
hr_new_array = [is_new_array[i] * is_hr_array[i] for i in range(N)]
|
||||
|
||||
x2_value = sum(sum(variables[i][j].solution_value() * tech_old_array[i] for i in range(N)) for j in range(M)) / M
|
||||
x3_value = sum(sum(variables[i][j].solution_value() * tech_new_array[i] for i in range(N)) for j in range(M)) / M
|
||||
x4_value = sum(sum(variables[i][j].solution_value() * hr_new_array[i] for i in range(N)) for j in range(M)) / M
|
||||
|
||||
aux_vars_x2_return = sum([aux_vars_x2[j].solution_value() for j in range(M)]) / M
|
||||
aux_vars_x3_return = sum([aux_vars_x3[j].solution_value() for j in range(M)]) / M
|
||||
aux_vars_x4_return = sum([aux_vars_x4[j].solution_value() for j in range(M)]) / M
|
||||
|
||||
print(f"X2 (每班平均技术部老人): {x2_value}, 缺口平均: {aux_vars_x2_return}")
|
||||
print(f"X3 (每班平均技术部小朋友): {x3_value}, 缺口平均: {aux_vars_x3_return}")
|
||||
print(f"X4 (每班平均人资部小朋友): {x4_value}, 缺口平均: {aux_vars_x4_return}")
|
||||
|
||||
if aux_vars_x5:
|
||||
x5_value = sum(sum(aux_vars_x5[j][k].solution_value() for k in range(num_departments)) for j in range(M))
|
||||
print(f"X5 (部门人数偏差): {x5_value}")
|
||||
|
||||
return variables_return
|
||||
else:
|
||||
print("No solution found.")
|
||||
return None
|
||||
|
||||
284
utils.py
284
utils.py
|
|
@ -1,143 +1,143 @@
|
|||
import pandas as pd
|
||||
from datetime import datetime
|
||||
from random import choice
|
||||
|
||||
# 对于表格每一列：
|
||||
col_timestamp = 1 # i = 1 -> 时间戳
|
||||
col_name = 6 # i = 6 -> 姓名
|
||||
col_department = 7 # i = 7 -> 部门编号
|
||||
col_member = 8 # i = 8 -> 老人 / 小朋友
|
||||
col_dutystart = 9 # i = 9 -> 值班开始时间
|
||||
col_dutyend = 28 # i = 28 -> 值班结束时间
|
||||
col_dutyfreq = 29 # i = 29 -> 想要值班的次数
|
||||
|
||||
index_to_departments = { # 部门编号和部门名称的对应关系
|
||||
1: "电脑部",
|
||||
2: "电器部",
|
||||
3: "人资部",
|
||||
4: "财外部",
|
||||
5: "文宣部"
|
||||
}
|
||||
|
||||
index_to_type = { # 老人 / 小朋友和对应的编号
|
||||
1: "老人",
|
||||
2: "小朋友"
|
||||
}
|
||||
|
||||
def read_excel(file_path):
|
||||
df = pd.read_excel(file_path)
|
||||
|
||||
# 待返回的所有信息。N 为学生人数，M 为班次数
|
||||
index_to_name_dict = {} # 长度为 N 的字典，包含数组下标和学生姓名的对应关系
|
||||
preference_mat = [] # N x M 的二维数组，表示每位学生对每个班次的满意度
|
||||
depart_mat = [] # N x D 的二维数组，表示每位学生所属的部门（D 为部门数量）
|
||||
want_num_array = [] # 长度为 N 的数组，表示每位学生想要值班的次数
|
||||
is_new_array = [] # 长度为 N 的数组，表示每位学生是否是小朋友
|
||||
|
||||
'''
|
||||
考虑到原始填表信息中可能有某位同学多次提交的记录，先过滤一下冗余信息
|
||||
'''
|
||||
all_data = {} # 储存过滤后的数据
|
||||
time_format = "%Y/%m/%d %H:%M:%S"
|
||||
for index, row in df.iterrows():
|
||||
name = row.iloc[col_name]
|
||||
time_this = datetime.strptime(row.iloc[col_timestamp], time_format)
|
||||
if name not in all_data or time_this > datetime.strptime(all_data[name][col_timestamp], time_format):
|
||||
all_data[name] = row.tolist()
|
||||
|
||||
for index, (name, info_list) in enumerate(all_data.items()):
|
||||
index_to_name_dict[index] = name
|
||||
preference_mat.append(info_list[col_dutystart:col_dutyend + 1])
|
||||
depart_mat.append([1 if info_list[col_department] == i else 0 for i in range(1, 6)])
|
||||
want_num_array.append(info_list[col_dutyfreq])
|
||||
is_new_array.append(index_to_type[info_list[col_member]] == "小朋友")
|
||||
|
||||
return all_data, index_to_name_dict, preference_mat, depart_mat, want_num_array, is_new_array
|
||||
|
||||
def save_to_excel(variables, all_data, index_to_name_dict, file_path):
|
||||
|
||||
# 收集每个班次的值班人员信息
|
||||
all_result = []
|
||||
max_single_class_num = 0
|
||||
|
||||
for j in range(len(variables[0])):
|
||||
on_duty_list = []
|
||||
hr_new_index = []
|
||||
none_tech_new_index = []
|
||||
new_index = []
|
||||
all_index = []
|
||||
|
||||
for i in range(len(variables)):
|
||||
if variables[i][j] == 1:
|
||||
name = index_to_name_dict[i]
|
||||
department = index_to_departments[all_data[name][col_department]]
|
||||
member_type = index_to_type[all_data[name][col_member]]
|
||||
|
||||
single_stu_info = {
|
||||
"name": name,
|
||||
"department": department,
|
||||
"type": member_type,
|
||||
"duty_monitor": False
|
||||
}
|
||||
on_duty_list.append(single_stu_info)
|
||||
|
||||
idx = len(all_index)
|
||||
all_index.append(idx)
|
||||
if member_type == "小朋友":
|
||||
new_index.append(idx)
|
||||
if department in ["人资部", "财外部", "文宣部"]:
|
||||
none_tech_new_index.append(idx)
|
||||
if department == "人资部":
|
||||
hr_new_index.append(idx)
|
||||
|
||||
# 选择值班组长（优先级：人资部小朋友 > 非技术部小朋友 > 小朋友 > 所有人）
|
||||
if hr_new_index:
|
||||
duty_monitor_index = choice(hr_new_index)
|
||||
elif none_tech_new_index:
|
||||
duty_monitor_index = choice(none_tech_new_index)
|
||||
elif new_index:
|
||||
duty_monitor_index = choice(new_index)
|
||||
else:
|
||||
duty_monitor_index = choice(all_index)
|
||||
on_duty_list[duty_monitor_index]["duty_monitor"] = True
|
||||
|
||||
on_duty_list.sort(key=lambda x: x["department"])
|
||||
all_result.append(on_duty_list)
|
||||
max_single_class_num = max(max_single_class_num, len(on_duty_list))
|
||||
|
||||
|
||||
from openpyxl import Workbook
|
||||
from openpyxl.styles import PatternFill
|
||||
|
||||
wb = Workbook()
|
||||
ws = wb.active if wb.active else wb.create_sheet(title="Sheet1")
|
||||
|
||||
# 设置表头
|
||||
weekdays = ["星期一", "星期二", "星期三", "星期四", "星期五", "星期六", "星期日"]
|
||||
for idx, day in enumerate(weekdays, start=2):
|
||||
col = chr(ord('A') + idx - 1)
|
||||
ws[f'{col}1'] = day
|
||||
ws.column_dimensions[col].width = 25
|
||||
|
||||
# 设置班次标题和起始行
|
||||
shift_names = ["第一班", "第二班", "第三班"]
|
||||
start_index = []
|
||||
for shift_idx, shift_name in enumerate(shift_names):
|
||||
start_row = 2 + shift_idx * (max_single_class_num + 1)
|
||||
end_row = start_row + max_single_class_num
|
||||
ws.merge_cells(f'A{start_row}:A{end_row}')
|
||||
ws[f'A{start_row}'] = shift_name
|
||||
start_index.append(start_row)
|
||||
|
||||
# 填写值班人员信息
|
||||
yellow_fill = PatternFill(fill_type='solid', start_color='FFFF00', end_color='FFFF00')
|
||||
for duty_index, duty in enumerate(all_result):
|
||||
col = chr(ord('B') + duty_index // 3)
|
||||
for stu_index, stu in enumerate(duty):
|
||||
row = start_index[duty_index % 3] + stu_index
|
||||
cell = ws[f'{col}{row}']
|
||||
cell.value = f"{stu['name']} {stu['department']} {stu['type']}"
|
||||
if stu['duty_monitor']:
|
||||
cell.fill = yellow_fill
|
||||
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
from random import choice
|
||||
|
||||
# 对于表格每一列：
|
||||
col_timestamp = 1 # i = 1 -> 时间戳
|
||||
col_name = 6 # i = 6 -> 姓名
|
||||
col_department = 7 # i = 7 -> 部门编号
|
||||
col_member = 8 # i = 8 -> 老人 / 小朋友
|
||||
col_dutystart = 9 # i = 9 -> 值班开始时间
|
||||
col_dutyend = 28 # i = 28 -> 值班结束时间
|
||||
col_dutyfreq = 29 # i = 29 -> 想要值班的次数
|
||||
|
||||
index_to_departments = { # 部门编号和部门名称的对应关系
|
||||
1: "电脑部",
|
||||
2: "电器部",
|
||||
3: "人资部",
|
||||
4: "财外部",
|
||||
5: "文宣部"
|
||||
}
|
||||
|
||||
index_to_type = { # 老人 / 小朋友和对应的编号
|
||||
1: "老人",
|
||||
2: "小朋友"
|
||||
}
|
||||
|
||||
def read_excel(file_path):
|
||||
df = pd.read_excel(file_path)
|
||||
|
||||
# 待返回的所有信息。N 为学生人数，M 为班次数
|
||||
index_to_name_dict = {} # 长度为 N 的字典，包含数组下标和学生姓名的对应关系
|
||||
preference_mat = [] # N x M 的二维数组，表示每位学生对每个班次的满意度
|
||||
depart_mat = [] # N x D 的二维数组，表示每位学生所属的部门（D 为部门数量）
|
||||
want_num_array = [] # 长度为 N 的数组，表示每位学生想要值班的次数
|
||||
is_new_array = [] # 长度为 N 的数组，表示每位学生是否是小朋友
|
||||
|
||||
'''
|
||||
考虑到原始填表信息中可能有某位同学多次提交的记录，先过滤一下冗余信息
|
||||
'''
|
||||
all_data = {} # 储存过滤后的数据
|
||||
time_format = "%Y/%m/%d %H:%M:%S"
|
||||
for index, row in df.iterrows():
|
||||
name = row.iloc[col_name]
|
||||
time_this = datetime.strptime(row.iloc[col_timestamp], time_format)
|
||||
if name not in all_data or time_this > datetime.strptime(all_data[name][col_timestamp], time_format):
|
||||
all_data[name] = row.tolist()
|
||||
|
||||
for index, (name, info_list) in enumerate(all_data.items()):
|
||||
index_to_name_dict[index] = name
|
||||
preference_mat.append(info_list[col_dutystart:col_dutyend + 1])
|
||||
depart_mat.append([1 if info_list[col_department] == i else 0 for i in range(1, 6)])
|
||||
want_num_array.append(info_list[col_dutyfreq])
|
||||
is_new_array.append(index_to_type[info_list[col_member]] == "小朋友")
|
||||
|
||||
return all_data, index_to_name_dict, preference_mat, depart_mat, want_num_array, is_new_array
|
||||
|
||||
def save_to_excel(variables, all_data, index_to_name_dict, file_path):
|
||||
|
||||
# 收集每个班次的值班人员信息
|
||||
all_result = []
|
||||
max_single_class_num = 0
|
||||
|
||||
for j in range(len(variables[0])):
|
||||
on_duty_list = []
|
||||
hr_new_index = []
|
||||
none_tech_new_index = []
|
||||
new_index = []
|
||||
all_index = []
|
||||
|
||||
for i in range(len(variables)):
|
||||
if variables[i][j] == 1:
|
||||
name = index_to_name_dict[i]
|
||||
department = index_to_departments[all_data[name][col_department]]
|
||||
member_type = index_to_type[all_data[name][col_member]]
|
||||
|
||||
single_stu_info = {
|
||||
"name": name,
|
||||
"department": department,
|
||||
"type": member_type,
|
||||
"duty_monitor": False
|
||||
}
|
||||
on_duty_list.append(single_stu_info)
|
||||
|
||||
idx = len(all_index)
|
||||
all_index.append(idx)
|
||||
if member_type == "小朋友":
|
||||
new_index.append(idx)
|
||||
if department in ["人资部", "财外部", "文宣部"]:
|
||||
none_tech_new_index.append(idx)
|
||||
if department == "人资部":
|
||||
hr_new_index.append(idx)
|
||||
|
||||
# 选择值班组长（优先级：人资部小朋友 > 非技术部小朋友 > 小朋友 > 所有人）
|
||||
if hr_new_index:
|
||||
duty_monitor_index = choice(hr_new_index)
|
||||
elif none_tech_new_index:
|
||||
duty_monitor_index = choice(none_tech_new_index)
|
||||
elif new_index:
|
||||
duty_monitor_index = choice(new_index)
|
||||
else:
|
||||
duty_monitor_index = choice(all_index)
|
||||
on_duty_list[duty_monitor_index]["duty_monitor"] = True
|
||||
|
||||
on_duty_list.sort(key=lambda x: x["department"])
|
||||
all_result.append(on_duty_list)
|
||||
max_single_class_num = max(max_single_class_num, len(on_duty_list))
|
||||
|
||||
|
||||
from openpyxl import Workbook
|
||||
from openpyxl.styles import PatternFill
|
||||
|
||||
wb = Workbook()
|
||||
ws = wb.active if wb.active else wb.create_sheet(title="Sheet1")
|
||||
|
||||
# 设置表头
|
||||
weekdays = ["星期一", "星期二", "星期三", "星期四", "星期五", "星期六", "星期日"]
|
||||
for idx, day in enumerate(weekdays, start=2):
|
||||
col = chr(ord('A') + idx - 1)
|
||||
ws[f'{col}1'] = day
|
||||
ws.column_dimensions[col].width = 25
|
||||
|
||||
# 设置班次标题和起始行
|
||||
shift_names = ["第一班", "第二班", "第三班"]
|
||||
start_index = []
|
||||
for shift_idx, shift_name in enumerate(shift_names):
|
||||
start_row = 2 + shift_idx * (max_single_class_num + 1)
|
||||
end_row = start_row + max_single_class_num
|
||||
ws.merge_cells(f'A{start_row}:A{end_row}')
|
||||
ws[f'A{start_row}'] = shift_name
|
||||
start_index.append(start_row)
|
||||
|
||||
# 填写值班人员信息
|
||||
yellow_fill = PatternFill(fill_type='solid', start_color='FFFF00', end_color='FFFF00')
|
||||
for duty_index, duty in enumerate(all_result):
|
||||
col = chr(ord('B') + duty_index // 3)
|
||||
for stu_index, stu in enumerate(duty):
|
||||
row = start_index[duty_index % 3] + stu_index
|
||||
cell = ws[f'{col}{row}']
|
||||
cell.value = f"{stu['name']} {stu['department']} {stu['type']}"
|
||||
if stu['duty_monitor']:
|
||||
cell.fill = yellow_fill
|
||||
|
||||
wb.save(file_path)
|
||||
852
uv.lock
852
uv.lock
|
|
@ -1,426 +1,426 @@
|
|||
version = 1
|
||||
revision = 3
|
||||
requires-python = ">=3.12"
|
||||
|
||||
[[package]]
|
||||
name = "absl-py"
|
||||
version = "2.3.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/10/2a/c93173ffa1b39c1d0395b7e842bbdc62e556ca9d8d3b5572926f3e4ca752/absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9", size = 116588, upload-time = "2025-07-03T09:31:44.05Z" }
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||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d", size = 135811, upload-time = "2025-07-03T09:31:42.253Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "altgraph"
|
||||
version = "0.17.4"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/de/a8/7145824cf0b9e3c28046520480f207df47e927df83aa9555fb47f8505922/altgraph-0.17.4.tar.gz", hash = "sha256:1b5afbb98f6c4dcadb2e2ae6ab9fa994bbb8c1d75f4fa96d340f9437ae454406", size = 48418, upload-time = "2023-09-25T09:04:52.164Z" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/4d/3f/3bc3f1d83f6e4a7fcb834d3720544ca597590425be5ba9db032b2bf322a2/altgraph-0.17.4-py2.py3-none-any.whl", hash = "sha256:642743b4750de17e655e6711601b077bc6598dbfa3ba5fa2b2a35ce12b508dff", size = 21212, upload-time = "2023-09-25T09:04:50.691Z" },
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||||
[[package]]
|
||||
name = "et-xmlfile"
|
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version = "2.0.0"
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source = { registry = "https://pypi.org/simple" }
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sdist = { url = "https://files.pythonhosted.org/packages/d3/38/af70d7ab1ae9d4da450eeec1fa3918940a5fafb9055e934af8d6eb0c2313/et_xmlfile-2.0.0.tar.gz", hash = "sha256:dab3f4764309081ce75662649be815c4c9081e88f0837825f90fd28317d4da54", size = 17234, upload-time = "2024-10-25T17:25:40.039Z" }
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wheels = [
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||||
]
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||||
|
||||
[[package]]
|
||||
name = "eva-duty-arrange-tool"
|
||||
version = "2.0.0"
|
||||
source = { virtual = "." }
|
||||
dependencies = [
|
||||
{ name = "openpyxl" },
|
||||
{ name = "ortools" },
|
||||
{ name = "pandas" },
|
||||
{ name = "pyinstaller" },
|
||||
{ name = "pyside6" },
|
||||
]
|
||||
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "openpyxl", specifier = ">=3.1.5" },
|
||||
{ name = "ortools", specifier = ">=9.14.6206" },
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{ name = "pandas", specifier = ">=2.3.3" },
|
||||
{ name = "pyinstaller", specifier = ">=6.16.0" },
|
||||
{ name = "pyside6", specifier = ">=6.10.0" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "immutabledict"
|
||||
version = "4.2.2"
|
||||
source = { registry = "https://pypi.org/simple" }
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||||
sdist = { url = "https://files.pythonhosted.org/packages/ce/12/1da8e1a9050d0603ba65fb1796ed8860a705b906701c96e77f85cc7490be/immutabledict-4.2.2.tar.gz", hash = "sha256:cb6ed3090df593148f94cb407d218ca526fd2639694afdb553dc4f50ce6feeca", size = 6099, upload-time = "2025-10-12T13:32:59.755Z" }
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||||
wheels = [
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||||
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||||
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||||
|
||||
[[package]]
|
||||
name = "macholib"
|
||||
version = "1.16.3"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "altgraph" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/95/ee/af1a3842bdd5902ce133bd246eb7ffd4375c38642aeb5dc0ae3a0329dfa2/macholib-1.16.3.tar.gz", hash = "sha256:07ae9e15e8e4cd9a788013d81f5908b3609aa76f9b1421bae9c4d7606ec86a30", size = 59309, upload-time = "2023-09-25T09:10:16.155Z" }
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wheels = [
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||||
|
||||
[[package]]
|
||||
name = "numpy"
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||||
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|
||||
source = { registry = "https://pypi.org/simple" }
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sdist = { url = "https://files.pythonhosted.org/packages/d0/19/95b3d357407220ed24c139018d2518fab0a61a948e68286a25f1a4d049ff/numpy-2.3.3.tar.gz", hash = "sha256:ddc7c39727ba62b80dfdbedf400d1c10ddfa8eefbd7ec8dcb118be8b56d31029", size = 20576648, upload-time = "2025-09-09T16:54:12.543Z" }
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200
使用手册.md
200
使用手册.md
|
|
@ -1,100 +1,100 @@
|
|||
# EVA 值班排班工具使用手册 - v2.0.0
|
||||
|
||||
## 前言
|
||||
这是浙江大学学生E志者协会“排班工具软件”的使用手册，将简要地介绍该软件的使用方法和注意事项。请注意，这是面向使用者而非开发者的手册，如果想了解该工具的开发流程和所使用的技术，请移步至[此仓库](https://git.zjueva.net/happywind/EVA_duty_arrange_tool)的说明手册。
|
||||
|
||||
## 软件运行环境 & 基本情况
|
||||
- 本软件只能在 Windows 系统上运行。
|
||||
- 本软件在部分版本的 Win10 和 Win11 系统上均测试过，能够正常运行。但是不排除可能出现缺少相关 dll 文件而无法运行的情况，如果出现请联系开发者解决。
|
||||
- 本软件将排班问题建模为**多目标组合优化问题**，使用 Google OR-Tools 的 SCIP 求解器求解。
|
||||
- 优化目标包括：
|
||||
1. 各班次人数尽可能平均
|
||||
2. 每班技术部老人数量接近期望值（3人）
|
||||
3. 每班技术部小朋友数量接近期望值（4人）
|
||||
4. 每班人资部小朋友数量接近期望值（1人）
|
||||
5. 每班各部门人数尽可能均衡
|
||||
- 在指定的限制条件下，若有解，则得到的一定是**全局最优解**。
|
||||
|
||||
## 软件使用方法
|
||||
|
||||
双击运行可执行文件，会看到如下软件界面：
|
||||
|
||||
<div style="text-align: center;">
|
||||
<img src="./pics/screen.jpg" style="width:50%;">
|
||||
</div>
|
||||
|
||||
进行以下三个步骤：
|
||||
|
||||
1. 选择待输入的问卷星结果 Excel 表格。对于选择的 Excel 表格，有以下**格式要求**：
|
||||
- 第 1 行，即表头的名称不会影响软件运行，但需要保证每一列的数据的格式和含义正确
|
||||
- 第 1 列为序号，不会用到，但需要有
|
||||
- 第 2 列为提交答卷时间，格式为 "year/month/day hour:min:sec"
|
||||
- 第 3 列为填表所用时间，不会用到，但需要有
|
||||
- 第 4 列为来源，不会用到，但需要有
|
||||
- 第 5 列为来源详情，不会用到，但需要有
|
||||
- 第 6 列为 IP，不会用到，但需要有
|
||||
- 第 7 列为姓名，这是每一位同学的唯一标识
|
||||
- 第 8 列为部门序号，用一个数字表示，映射关系如下：
|
||||
```python
|
||||
index_to_departments = { # 部门编号和部门名称的对应关系
|
||||
1: "电脑部",
|
||||
2: "电器部",
|
||||
3: "人资部",
|
||||
4: "财外部",
|
||||
5: "文宣部"
|
||||
}
|
||||
```
|
||||
- 第 9 列标识老人还是小朋友，用一个数字表示，映射关系如下：
|
||||
```python
|
||||
index_to_type = { # 老人/小朋友和对应的编号
|
||||
1: "老人",
|
||||
2: "小朋友"
|
||||
}
|
||||
```
|
||||
- 第 10 ~ 29 列，表示对于每个班次的意愿（1 代表有时间，0 代表没时间）。因为从周一第一班到周日第二班一共有 20 班，所以总共有 20 列。
|
||||
- 第 30 列，表示愿意排几班，用一个数字表示。
|
||||
|
||||
**我们提供了一个名为“问卷星结果_样例输入.xlsx”的文件，以作为正确输入格式的参考，以及一个名为“问卷星结果_较极端案例.xlsx”的文件，以作为程序运行的测试。**
|
||||
|
||||
**本软件可以自动处理同一名同学多次填写问卷的情况**，只要保证多次填写中“姓名”保持一致即可，软件将会自动选择最后一次填写的结果作为最终意愿。由于“姓名”是每位同学的唯一标识符，如果出现了某位同学“姓名”填错的情况，需要手动删除该姓名的记录。
|
||||
|
||||
如果出现了**两位同学撞名的情况**，需要在填表时“姓名”后加上后缀以作区分，比如“王五1”和“王五2”，否则就会当作一个人来看待。
|
||||
|
||||
2. 设置优化参数
|
||||
|
||||
软件界面的 Step 2 部分包含两类参数：
|
||||
|
||||
### 2.1 权重参数（始终可设置）
|
||||
|
||||
软件提供了 5 个优化目标的权重设置，用于调整各个目标的重要程度：
|
||||
- **目标 1 权重**：每班人数平均程度（默认 1.0）
|
||||
- **目标 2 权重**：每班技术部老人数量（默认 1.0）
|
||||
- **目标 3 权重**：每班技术部小朋友数量（默认 1.0）
|
||||
- **目标 4 权重**：每班人资部小朋友数量（默认 0.5）
|
||||
- **目标 5 权重**：每班部门平均程度（默认 0.5）
|
||||
|
||||
权重值范围建议为 0.0 - 5.0，权重越大表示该目标越重要。所有权重参数均已归一化处理。
|
||||
|
||||
### 2.2 约束条件（自动/手动模式）
|
||||
|
||||
**自动调参模式（推荐）：**
|
||||
- 勾选"自动调参模式"复选框
|
||||
- 软件会自动尝试不同的约束参数组合，实时显示尝试过程
|
||||
- 从严格到宽松逐步调整参数，找到第一个可行解
|
||||
- 无需手动设置约束条件，约束输入框会被禁用
|
||||
|
||||
**手动模式：**
|
||||
- 取消勾选"自动调参模式"
|
||||
- 可手动设置以下约束条件：
|
||||
- **每班次人数**：最少和最多人数（建议 5-8 人）
|
||||
- **每班次电脑或电器的老人数**：技术部老人数量范围（建议最多 2 人）
|
||||
- **每班次老人数**：所有老人数量范围（建议最少 1 人）
|
||||
- **每班次小朋友数**：所有小朋友数量范围（建议最少 2 人）
|
||||
- 留空表示该项无限制
|
||||
- **注意**：约束条件设置过严可能导致无解，需要适当放宽限制
|
||||
|
||||
3. 开始排班并查看进度
|
||||
|
||||
点击"开始排班！"按钮开始计算：
|
||||
- 输出结果以 Excel 表格形式保存，文件名为 `result_<当前时间戳>.xlsx`
|
||||
- **在输出结果中，组长会被标黄**
|
||||
# EVA 值班排班工具使用手册 - v2.0.0
|
||||
|
||||
## 前言
|
||||
这是浙江大学学生E志者协会“排班工具软件”的使用手册，将简要地介绍该软件的使用方法和注意事项。请注意，这是面向使用者而非开发者的手册，如果想了解该工具的开发流程和所使用的技术，请移步至[此仓库](https://git.zjueva.net/happywind/EVA_duty_arrange_tool)的说明手册。
|
||||
|
||||
## 软件运行环境 & 基本情况
|
||||
- 本软件只能在 Windows 系统上运行。
|
||||
- 本软件在部分版本的 Win10 和 Win11 系统上均测试过，能够正常运行。但是不排除可能出现缺少相关 dll 文件而无法运行的情况，如果出现请联系开发者解决。
|
||||
- 本软件将排班问题建模为**多目标组合优化问题**，使用 Google OR-Tools 的 SCIP 求解器求解。
|
||||
- 优化目标包括：
|
||||
1. 各班次人数尽可能平均
|
||||
2. 每班技术部老人数量接近期望值（3人）
|
||||
3. 每班技术部小朋友数量接近期望值（4人）
|
||||
4. 每班人资部小朋友数量接近期望值（1人）
|
||||
5. 每班各部门人数尽可能均衡
|
||||
- 在指定的限制条件下，若有解，则得到的一定是**全局最优解**。
|
||||
|
||||
## 软件使用方法
|
||||
|
||||
双击运行可执行文件，会看到如下软件界面：
|
||||
|
||||
<div style="text-align: center;">
|
||||
<img src="./pics/screen.jpg" style="width:50%;">
|
||||
</div>
|
||||
|
||||
进行以下三个步骤：
|
||||
|
||||
1. 选择待输入的问卷星结果 Excel 表格。对于选择的 Excel 表格，有以下**格式要求**：
|
||||
- 第 1 行，即表头的名称不会影响软件运行，但需要保证每一列的数据的格式和含义正确
|
||||
- 第 1 列为序号，不会用到，但需要有
|
||||
- 第 2 列为提交答卷时间，格式为 "year/month/day hour:min:sec"
|
||||
- 第 3 列为填表所用时间，不会用到，但需要有
|
||||
- 第 4 列为来源，不会用到，但需要有
|
||||
- 第 5 列为来源详情，不会用到，但需要有
|
||||
- 第 6 列为 IP，不会用到，但需要有
|
||||
- 第 7 列为姓名，这是每一位同学的唯一标识
|
||||
- 第 8 列为部门序号，用一个数字表示，映射关系如下：
|
||||
```python
|
||||
index_to_departments = { # 部门编号和部门名称的对应关系
|
||||
1: "电脑部",
|
||||
2: "电器部",
|
||||
3: "人资部",
|
||||
4: "财外部",
|
||||
5: "文宣部"
|
||||
}
|
||||
```
|
||||
- 第 9 列标识老人还是小朋友，用一个数字表示，映射关系如下：
|
||||
```python
|
||||
index_to_type = { # 老人/小朋友和对应的编号
|
||||
1: "老人",
|
||||
2: "小朋友"
|
||||
}
|
||||
```
|
||||
- 第 10 ~ 29 列，表示对于每个班次的意愿（1 代表有时间，0 代表没时间）。因为从周一第一班到周日第二班一共有 20 班，所以总共有 20 列。
|
||||
- 第 30 列，表示愿意排几班，用一个数字表示。
|
||||
|
||||
**我们提供了一个名为“问卷星结果_样例输入.xlsx”的文件，以作为正确输入格式的参考，以及一个名为“问卷星结果_较极端案例.xlsx”的文件，以作为程序运行的测试。**
|
||||
|
||||
**本软件可以自动处理同一名同学多次填写问卷的情况**，只要保证多次填写中“姓名”保持一致即可，软件将会自动选择最后一次填写的结果作为最终意愿。由于“姓名”是每位同学的唯一标识符，如果出现了某位同学“姓名”填错的情况，需要手动删除该姓名的记录。
|
||||
|
||||
如果出现了**两位同学撞名的情况**，需要在填表时“姓名”后加上后缀以作区分，比如“王五1”和“王五2”，否则就会当作一个人来看待。
|
||||
|
||||
2. 设置优化参数
|
||||
|
||||
软件界面的 Step 2 部分包含两类参数：
|
||||
|
||||
### 2.1 权重参数（始终可设置）
|
||||
|
||||
软件提供了 5 个优化目标的权重设置，用于调整各个目标的重要程度：
|
||||
- **目标 1 权重**：每班人数平均程度（默认 1.0）
|
||||
- **目标 2 权重**：每班技术部老人数量（默认 1.0）
|
||||
- **目标 3 权重**：每班技术部小朋友数量（默认 1.0）
|
||||
- **目标 4 权重**：每班人资部小朋友数量（默认 0.5）
|
||||
- **目标 5 权重**：每班部门平均程度（默认 0.5）
|
||||
|
||||
权重值范围建议为 0.0 - 5.0，权重越大表示该目标越重要。所有权重参数均已归一化处理。
|
||||
|
||||
### 2.2 约束条件（自动/手动模式）
|
||||
|
||||
**自动调参模式（推荐）：**
|
||||
- 勾选"自动调参模式"复选框
|
||||
- 软件会自动尝试不同的约束参数组合，实时显示尝试过程
|
||||
- 从严格到宽松逐步调整参数，找到第一个可行解
|
||||
- 无需手动设置约束条件，约束输入框会被禁用
|
||||
|
||||
**手动模式：**
|
||||
- 取消勾选"自动调参模式"
|
||||
- 可手动设置以下约束条件：
|
||||
- **每班次人数**：最少和最多人数（建议 5-8 人）
|
||||
- **每班次电脑或电器的老人数**：技术部老人数量范围（建议最多 2 人）
|
||||
- **每班次老人数**：所有老人数量范围（建议最少 1 人）
|
||||
- **每班次小朋友数**：所有小朋友数量范围（建议最少 2 人）
|
||||
- 留空表示该项无限制
|
||||
- **注意**：约束条件设置过严可能导致无解，需要适当放宽限制
|
||||
|
||||
3. 开始排班并查看进度
|
||||
|
||||
点击"开始排班！"按钮开始计算：
|
||||
- 输出结果以 Excel 表格形式保存，文件名为 `result_<当前时间戳>.xlsx`
|
||||
- **在输出结果中，组长会被标黄**
|
||||
|
|
|
|||
Loading…
Reference in New Issue