菜鸟-需求预测与分仓规划代码
时间: 2023-08-01 12:13:04 浏览: 92
需求预测和分仓规划是一个很常见的问题,一般需要使用时间序列分析和优化算法来解决。以下是一个简单的Python代码示例,可以帮助你对需求进行预测和进行分仓规划。
首先,我们需要导入一些必要的库:
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.tsa.arima_model import ARIMA
from sklearn.metrics import mean_squared_error
import pulp
```
接下来是数据预处理部分。我们需要读取销售数据和库存数据,并将它们合并成一个数据框:
```python
sales_data = pd.read_csv('sales_data.csv')
inventory_data = pd.read_csv('inventory_data.csv')
df = pd.merge(sales_data, inventory_data, on='date')
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)
```
接下来是时间序列分析部分。我们可以使用ARIMA模型来预测销售量:
```python
def arima_model(series):
model = ARIMA(series, order=(3,1,0))
model_fit = model.fit(disp=0)
return model_fit.forecast()[0]
sales_prediction = []
for i in range(len(df)):
if i < 7:
sales_prediction.append(np.nan)
else:
sales_prediction.append(arima_model(df['sales'][i-7:i]))
df['sales_prediction'] = sales_prediction
plt.plot(df['sales'])
plt.plot(df['sales_prediction'])
plt.show()
```
然后,我们可以使用线性规划来进行分仓规划。我们可以使用pulp库来构建和求解线性规划问题:
```python
def optimize_warehouses(num_warehouses, max_capacity):
prob = pulp.LpProblem("Warehouse Planning", pulp.LpMinimize)
inventory_vars = []
for i in range(num_warehouses):
inventory_vars.append(pulp.LpVariable(f"inventory_{i}", lowBound=0, upBound=max_capacity))
total_cost = 0
for i in range(len(df)):
demand = df['sales_prediction'][i]
if np.isnan(demand):
continue
shortfall = []
for j in range(num_warehouses):
shortfall.append(demand - inventory_vars[j])
prob += pulp.lpSum(shortfall) == 0
total_cost += pulp.lpSum([inventory_vars[j] for j in range(num_warehouses)]) * 0.01
prob += total_cost
prob.solve()
inventory_levels = []
for v in prob.variables():
inventory_levels.append(v.varValue)
return inventory_levels
```
最后,我们可以调用optimize_warehouses函数来进行分仓规划,并将结果保存到一个新的数据框中:
```python
inventory_levels = optimize_warehouses(num_warehouses=3, max_capacity=1000)
df['inventory_level'] = inventory_levels
plt.plot(df['inventory_level'])
plt.show()
```
这是一个简单的示例,你可以根据自己的需求进行修改和扩展。
相关推荐
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![rar](https://img-home.csdnimg.cn/images/20210720083606.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)